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• A comprehensive analysis of root causes for the accelerated internal corrosion in steel saltwater pipes coated with fusion bonded epoxy. • Cathodic delamination of the epoxy lining is identified as the primary cause of early internal corrosion failure. • Dynamic cyclic load has a greater chance for crack propagation in the epoxy lining compared to static load. • Development of recommendations and precautions based on the results of failure analysis. Effective functioning of saltwater supply system is essential to Hong Kong government agencies. However, it has been frequently observed that steel saltwater pipes suffered from severe internal corrosion and consequently early burst accidents, which may cause high economic loss and safety concerns to the public. Therefore, by taking a sample saltwater pipe made of DN450 mild steel with internal and external walls coated with fusion bonded epoxy in Hong Kong, this paper investigates the root causes and failure mechanism for the internal corrosion of this failed steel saltwater pipe through laboratory experiments and numerical simulation analysis. Two hypotheses are proposed and validated: (1) cathodic delamination of the epoxy lining, and (2) delamination of the epoxy lining due to external mechanical loads. The results verify that the sample saltwater pipe failed due to the cathodic delamination of the epoxy lining, and the electrochemical corrosion of the inner pipe wall. Moreover, it can be concluded that external mechanical load has few significant impacts on the damage of the epoxy lining for this sample pipe. This study exemplifies the importance of an in-depth analysis on the internal corrosion of steel water pipes, especially in a highly-corrosive internal environment. [ABSTRACT FROM AUTHOR]

Tower cranes (TCs) are vital equipment highly sought after on construction sites due to their efficiency in handling and lifting heavy loads. Nevertheless, the use of TCs on construction sites is riddled with significant safety issues, resulting in numerous accidents across the globe. This study uniquely focuses on the emerging tower crane safety technologies (TCST) field and makes vital contributions by investigating and analyzing TCST research. It aims to explore knowledge gaps and propose future research directions through a systematic review. The uniqueness of this study is its comprehensive examination of TCST, which encompasses not only academic research but also industrial technologies and products. TCST has been categorized based on their purposes and specific aspects of crane management, leading to four key categories: (1) proactive preconstruction crane management technologies; (2) monitoring technologies during construction; (3) anti-collision technologies during construction; and (4) stability control technologies during construction. This study makes a threefold contribution. First, it identifies six crucial knowledge gaps, three in academia and three in the industrial sector. These gaps relate to data completeness, data scope, real-world application, post-construction dismantling, accuracy and data reliability, and data security. Second, it outlines future research opportunities in both academic and industrial contexts, providing a clear path to address these gaps and improve tower crane (TC) operation safety and efficiency. Lastly, it delivers valuable insights to industry practitioners by presenting the latest TCST applications in real-world scenarios, thereby promoting safer and more effective construction practices. • Tower cranes enhance construction efficiency but pose safety challenges. • Comprehensive systematic review of TCST conducted in academia and industry. • Four TCST categories identified: preconstruction, monitoring, anti-collision, stability. • A total of six critical knowledge gaps found in academia and industry. • Offers insights and future research directions for safer construction practices. [ABSTRACT FROM AUTHOR]

• An LSTM-GAN generative model is employed for enriching the leak detection dataset. • The model captures the time-series features of leak signals in WDNs. • The performance of LSTM GAN is evaluated through a series of validation approaches. • The method enriches the leak signals and enhances the water leak detection accuracy. Water distribution networks (WDNs) experience significant water loss due to leaks, necessitating advanced water leak detection methods. However, machine learning-based acoustic method heavily relies on signal information and is limited by data scarcity and the limited diversity of available data. To address this challenge and enhance water leak detection in WDNs, this study proposes an LSTM-GAN approach. Acoustic signals are collected from WDNs to train the LSTM-GAN model, which generates synthetic leak signals to enhance the dataset. The validity of the generative method is evaluated through t-SNE and acoustic characteristics analysis. LSTM-based water leak detection models are established and compared using the original and the generated datasets to confirm the efficacy of generated samples in improving water leak detection performances. The capability of LSTM-GAN has been evaluated through different perspectives, including sensitivity analysis and model comparison. The results validate the quality and consistency of the generated acoustic signals under leak conditions. Besides, the optimal number of generated samples should be determined according to the requirements and characteristics of the leak detection task. Furthermore, the comparison between the proposed method and other acoustic generative methods demonstrates the superiority of LSTM-GAN-generated signals in enhancing the performance of leak detection models. The proposed generative method offers an innovative approach to facilitate machine learning-based leak detection models with limited data, thereby enhancing robustness. [Display omitted] [ABSTRACT FROM AUTHOR]

Rutting's impact on riding safety and comfort prompts highway agencies to advance the development of accurate, efficient, and cost-effective rutting measurement methods. This study systematically analyzed over 40 articles and compared over 20 pieces of equipment to investigate the advancements in research and practice related to field rutting measurement on pavement roads. The findings revealed that rut bars and 3D line-laser-imaging systems are the most popular measurement technologies in research and industry. Rutting depth was identified as the predominant measure for characterizing pavement rutting. However, continuous profiling systems enabled the emergence of other rutting indices tailored for specific purposes like safety and comfort evaluation. Articulated future demanding research topics include: (1) selection of rutting indices; (2) optimization of reporting methods including sampling frequency and segmentation strategies; (3) use of non-destructive technologies such as ground penetrating radar to identify rutting mechanisms; and (4) the development of low-cost methods for rutting measurement. [Display omitted] • Rut bars and 3D laser imaging are the most popular rut measurement technologies. • Rut depth is the classical and most used rutting index in rutting measurement. • Studying rutting mechanisms using GPR holds great promise for future research. • Developing technologies that balance accuracy and cost-effectiveness is vital. [ABSTRACT FROM AUTHOR]

[Display omitted] • A reliable blockage detection system is realized by incorporating low-power level sensors and 4G telemetry for real-time monitoring and a smart alarming system. • The blockage detection system employs a set of decision rules and time series analysis to distinguish blockage events from normal behavior. • The proposed system achieved a high success rate in detecting blockage events during a field test, with a low false alarm rate. • The system can help reduce the economic and environmental impacts of Sanitary sewer overflows (SSOs) and improve the overall performance of the wastewater network. Sewer blockages and overflows have significant economic and environmental repercussions on communities. Thus, it is crucial to detect and remove sewer blockages prior to the occurrence of overflows. With the improvement in mobile networks and the development of high-quality and low-power sensors and loggers, wastewater network operators can now adopt monitoring devices enabled by the "Internet of Things" (IoT) technology for real-time monitoring. To this end, this paper studies the current state-of-the-art in sewer blockage management and introduces a novel methodology to monitor sanitary sewer blockages to prevent sanitary sewer overflows (SSO) with the least human interaction. The proposed system incorporates low-power level sensors and 4G telemetry for real-time monitoring of the manhole's sewage level to detect sewer blockages. The blockage detection methodology encompasses a set of decision rules and time series analysis to identify blockage events. The final blockage detection model offers a versatile capability to be implemented on sanitary sewer networks of different types with minimum computational costs. [ABSTRACT FROM AUTHOR]

Hong Kong, among the world's most densely populated cities, has witnessed rapid growth in traffic volume, resulting in increased traffic density and vehicle loads. Regular bridge inspections are imperative to ensure human safety and safeguard property. However, conventional visual inspection methods are highly criticized for their critical limitations such as inaccuracy, subjectivity, labor-intensiveness, tediousness, and hazardousness. Cracks are regarded as the most prevalent type of defects encountered during inspection of reinforced concrete bridges. Automated detection of bridge surface cracks is a quite challenging and hectic task due to their random characteristics and usual in complex and non-uniform background textures. Presence. In light of foregoing, this paper proposes a novel computer vision model for concrete bridge crack detection in an attempt to circumvent the critical deficiencies of manual visual inspection. The developed model is envisioned on the use of you only look once version 8 (YOLOv8) architecture, which is cited as one of the most advanced convolutional neural networks structures for multi-scale object detection. Comprising three fundamental components - the backbone, neck, and head, this model introduces the concept of a decoupled head, segregating it into a detection head and a classification head. This design empowers the model with greater flexibility in handling diverse tasks. Moreover, the incorporation of the global attention module (GAM) and the wise intersection over union (IoU) loss function serves to further boost detection correctness of the developed model and amplify its generalization ability. The developed YOLOv8-GAM-Wise-IoU is compared against some of the widely acknowledged one-stage and two-stage deep learning models using the evaluation metrics of precision, recall, F1-score, mean average precision (mAP) and IoU. It outperformed them accomplishing testing precision, recall, F1-score, mAP50, mAP50–95 and mAP75 of 97.4%, 94.9%, 0.96, 98.1%, 76.2%, and 97.8%, respectively. It is also observed that developed model maintains a modest size of 93.20 M resulting in diminishing the computational cost of training and inference processes. This makes it highly deployable in various crack detection pertaining applications. It can be argued that the developed model can contribute notably to the preservation of safety and integrity of reinforced concrete bridges in Hong Kong environment. • Previously developed bridge crack detection models are reviewed. • A Novel YOLOv8-GAM-Wise-IoU Model is proposed for bridge crack detection. • GAM and Wise-IOU are introduced to enhance detection correctness. • One-stage and two-stage deep learning models are used for validation. • Efficiency of the developed model is extensively scrutinized and exemplified. [ABSTRACT FROM AUTHOR]

• Propose an integrated methodology for failure modeling of gas pipelines. • Identity the importance of physical covariates and handling censoring in modeling. • RSF model outperforms Cox model and other machine learning models. • Provide valuable insights for safety and risk management for gas pipelines. This study proposes a methodology to model gas transmission pipeline failures using historical pipeline failure data. Censoring occurs frequently in the dataset, and overlooking it may lead to biased predictions. To address this issue, the statistical Cox model, a survival analysis and machine learning integrated model, i.e., RSF, are introduced in this study, along with other machine learning models (ANN, SVR, RF, and XGBoost), primarily for comparison. The Cox and RSF models provide insights into the influence of covariates on pipeline failure, informing decisions regarding pipeline construction, inspection, and maintenance activities. The findings indicate that the statistical Cox model overestimates failure age due to its limited ability to capture failure nonlinearity, while other machine learning models underestimate failure age because they cannot handle dataset censoring. In contrast, the survival analysis integrated machine learning method, RSF, outperforms other methods for modeling gas pipeline failures. The findings have practical implications for effectively managing reliability and mitigating risks associated with gas transmission pipelines to ensure safety. Moreover, the proposed methodology can potentially be applied to other pipeline systems and various types of systems, provided certain requirements are met. [ABSTRACT FROM AUTHOR]

Sewer overflow (SO) is becoming a concerning issue since discharged wastewater contains toxic substances and debris resulting in hazardous pollution to the surrounding environment and water quality degradation; and spilled stormwater may cause localized flooding and even back-up into buildings. Therefore, it is necessary to predict the occurrence of SO in advance, which enables the utilities to post warnings, prioritize the resource allocation and take proactive measures to minimize negative effects on environment and society. This paper aims to provide a state-of-the-art review for the prediction of sewer overflow which is lacking in literature, including bibliometric survey, scientometric analysis, in-depth systematic review, and elucidation of the existing research gaps and the potential future research directions. The findings reveal that the majority focuses on combined sewer overflow (CSO), and artificial intelligence-based models are the most popular ones. The input factors vary widely among three model categories. Volume , likelihood of occurrence and water level are the three mostly adopted output factors. Further research directions are recommended to fill these gaps (e.g., consider socio-economic factors and pipe properties, deploy IoT facilities to reduce false alarms, distinguish between regular and extreme weather conditions). This state-of-the-art review fills the gap of few endeavors focusing on SO prediction, and could provide the scholars and engineers with inclusive hindsight in dealing with harmful incidents. [ABSTRACT FROM AUTHOR]

Reinforcement corrosion is a common cause of damage in concrete structures, resulting in cracking and spalling of the surrounding concrete. Recently, ground penetrating radar (GPR) has become the primary non-destructive testing (NDT) for examining the corrosiveness of concrete structures. However, the analysis of GPR data can be intricate and time-consuming due to a range of factors affecting GPR waves. To this end, this paper presents a systematic review of influential factors and analysis methods for GPR-based corrosion assessment in concrete structures. The review analysis found that corrosion boosts the reflected waves' amplitude by propagating rust into concrete cracks, while the simultaneous presence of chloride and moisture severely attenuates the amplitude and frequency of electromagnetic waves. In addition, B-scans analysis is more thorough but also subjective and time-consuming and lacks for comprehensive methodology to automate its process. Thereafter, this study offers insights for improving the efficiency of GPR-based corrosion assessment. [Display omitted] • Scientometric and systematic reviews were leveraged to study uses of GPR for corrosion assessment in concrete structures. • The impact of the physical, environmental, and technical factors on the GPR signal was reviewed. • Various GPR analysis methods for corrosion assessment were compared. • Future prospects were suggested to ameliorate the efficiency of GPR-based corrosion assessment in concrete structures. [ABSTRACT FROM AUTHOR]

• The incident records of gas transmission pipelines in the U.S. are investigated. • Effects of physical, environmental and operational factors are investigated. • The link between background, causal factors and incident consequences is explored. • Enhance a better understanding of pipeline failures and aid in development of predictive models. The contemporary world is confronted with a significant and pressing challenge in the form of the energy crisis. Natural gas represents a potential solution to the crisis, and its transportation relies heavily on transmission and distribution pipelines. Regrettably, these pipelines have witnessed a significant number of severe incidents in recent years due to various reasons. This study aims to investigate the influence of different background factors on pipeline incidents triggered by distinct causal factors, using incident records from the PHMSA in the United States. This study reveals that outside force damage is the most frequent causal factor, responsible for 41.21% of total incidents. The failure rate of gas transmission pipelines is notably affected by the operational pressure ratio and the laying location class. Moreover, this study explores the relationship between various factors and each causal factor leading to the incident, assessing the incident's consequences in terms of type, injury, fatality, and total cost. Generally, incidents involving larger-diameter pipelines with higher operational pressure ratios and deployed in higher location classes have more severe consequences, with the highest injury rate, fatality rate, and cost rate being 1.728 × 10−4, 1.043 × 10−4, and 1953.36 $ per incident per kilometer, respectively. These findings contribute to a better understanding of pipeline failures and can aid in the development of accurate and effective predictive models for gas pipelines. [ABSTRACT FROM AUTHOR]

The growing problem of bridge deterioration globally has imposed prominent challenges on transportation agencies, mainly in terms of ensuring safety and serviceability of the bridge infrastructure. The large number of bridges built during the 20th century has aged and produced a complex decision-making problem that departments of transportation need to deal with. Bridge management, as a particular domain of infrastructure asset management, has focused on developing methods for condition rating and deterioration modeling. The current research reviews bridge inspection practices and identifies the main defects and deterioration signs of concrete bridge decks that are typically captured by Visual Inspection (VI) and Non-Destructive Evaluation (NDE) techniques. The research introduces the Quality Function Deployment (QFD) theory and Weibull Distribution Function (WDF) as an integrated novel method to the area of bridge condition assessment and deterioration modeling. The proposed QFD condition assessment model is developed based on integrating VI and Ground Penetrating Radar (GPR) evaluation results to provide consistent condition ratings and performance predictions. The QFD model is demonstrated with a real case study and compared to other condition assessment models. Moreover, the QFD method is validated with data extracted from twenty bridge inspection reports completed by bridge inspectors and assessed by bridge experts. The developed deterioration curves using the reliability function for the Weibull distribution show absolute matching in these results through predicting the structure future performance and defining its useful service life. Accordingly, these models can enhance bridge Maintenance, Repair and Replacement (MRR) decisions since they produce reliable condition ratings and predictions that can link to proper rehabilitation action, and eventually assist in the decision making and planning for the selected MRR action. All these processes are integrated within one framework. [ABSTRACT FROM AUTHOR]

Discrete-Event Simulation (DES) models are constructed from sequential duration-based activities. DES is used in modeling several construction operations including earthmoving. Current earthmoving models cannot accommodate equipment units with different specifications performing the same task. In addition, activity durations are calculated based on primitive methods such as interpolating existing durations of similar activities in previous projects. Finally, model elements behave in a predetermined manner, ignoring special operational real-life scenarios that occur due to resource constraints. These limitations often lead to inaccuracies in calculating productivity and equipment utilization. This paper applies Agent-Based Modeling and Simulation (ABMS) as an effective bottom-up tool to modeling earthmoving operations. An Agent-Based (AB) earthmoving model consisting of smart, adaptive agents is developed. Each agent is assigned a state chart and a set of static and dynamic properties (attributes and variables) to direct its interactions with the environment and with other agents. This framework proves how modeling earthmoving from the agent's prospective and basing agents' interactions on their properties allow for modeling equipment units with different specifications performing the same task (e.g. trucks of different capacities), as well as for an accurate representation of activity durations, resource handling and resource constraint scenarios. A Java-based application named Agent-Based Simulator for Earthmoving Operations (ABSEMO) is developed as an implementation of the proposed model. ABSEMO will be helpful to contractors in planning earthmoving operations according to the AB approach. A real-life case study of a riverbed excavation in a dam construction project is simulated using ABSEMO, and the results are compared with those obtained from existing simulation models to verify ABSEMO's logic. A percentage difference of 0.42% from the existing results is obtained, indicating that the model's flow of resources is indeed accurate. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) is evoking interest as a sustainable construction method leading towards industry 4.0. However, its complex supply chain and fragile logistic operations impede project performance. This paper, therefore, explores the critical influencing factors of MiC supply chain operations and studies their interrelationships. A systematic review is conducted across multiple supply chain research domains to identify relevant factors. These factors are evaluated using eigenvector and MICMAC analysis. Four themes of critical factors are determined using factor's interrelationships: dominating, symbiotic, external, and potential influencing factors. These themes demonstrate the influencing nature and mechanism of factors. As a result, most of the assembling-related factors are found to dominate and dynamically control the MiC supply chain performance. This paper also highlights potential factors that need further research attention, studies prevalent factors affecting the MiC supply chain and offers future research directions. [Display omitted] • General supply chain management, logistics management and construction supply chain domains are systematically reviewed. • Forty-three significant factors were ranked using eigenvector approach. • Critical factors are evaluated and categorized using MICMAC analysis. • Factors' relationships are analysed, and four themes of factors are determined. • Paper studies prevalent factors affecting MiC supply chain and highlights potential factors that need further research. [ABSTRACT FROM AUTHOR]

Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques. [ABSTRACT FROM AUTHOR]

This paper presents newly developed method for detecting and locating leaks in water distribution networks utilizing two detection techniques; ground penetrating radar (GPR) and infrared photography (IR). The experimental work and field investigation were carried out over 2 years in three locations in City of Doha, Qatar to capture 115 IR image frames and 23 GPR image frames. Firstly, GPR technology is utilized to accurately define location of buried pipes. After locating these pipes, IR images are collected for simulated and actual leaks. The developed algorithm segments each image into leakage and non-leakage areas and the centroid of each leakage is calculated using Green's theorem. Subsequently, GPR images are introduced as a second layer and overlaid with IR images to compare pipes location with leak location. The method was successfully applied to detect simulated and actual leaks in summer and winter seasons with small margin of error (2.9–5.6%) in estimating leakage areas. When examining the investigated four operating conditions, it was found that the developed method can predict leaks in a more reliable way if the camera height is 2 m and the speed is 1.65 m/s in both simulated and actual leaks. The newly developed method is robust and can aid operators and city engineers in detecting and locating water leaks with high accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Application of machine intelligence to water leak detection. • Identification of controlling parameters governing the leakage phenomenon. • The derived GEP expressions achieved a reasonable accuracy in leakage prediction. Globally, the loss of potable water through buried and service water distribution networks (WDNs) is one of the persistent challenges confronted by water utilities. Most of the WDNs inspection methods are ad-hoc and typically provide the current status of the pipes. Despite the significant research efforts, a reliable prediction method of the water leak of buried and service water distribution pipes is rare. To overcome this challenge, gene expression programming (GEP) models – that uses extracted features of acoustic signals collected from noise loggers attached to pipeline valves at the chamber – are developed and validated in the current study. To develop these models, extensive fieldwork hitherto unavailable was undertaken in this study to record the flow-induced acoustic signal of WDNs. The GEP models are developed using a step function – that returns a binary output – which is leveraged to exhibit the proposed detection and prediction methods using the acoustic signal collected from the buried WDNs. The models only consider the highly correlated features with the leakage. The models are found to be able to predict leakages on metal and nonmetal pipes with about 95% accuracy. These models were developed to reduce the time and cost of deployment of equipment for leakage detection of pipes. The proposed method presented in this study highlights the prospect and advantage of using the GEP in infrastructural management given the amazing capability of the machine intelligence technique to recognize multi-dimensional circumstances with ease, high prediction accuracy, and potential for unceasing improvement. [ABSTRACT FROM AUTHOR]

This study presented a system boundary-based review to systematically investigate the existing research on crane selection in building construction. The system boundary model comprised 9 boundary elements, namely year, location, research source, project height, project typology, crane type, selection constraint, selection criteria, and selection method. Three detailed analyses were carried out with reference to the boundary elements. Firstly, distribution analysis reflected an increasing research trend in crane selection worldwide. Secondly, regression analysis, considering 25 real cases, indicated that tower crane is better suited for high-rise building construction. Thirdly, systematic analysis identified a total of 19 constraints, 71 criteria, and 8 methods adopted for crane selection. Subsequently, the relationships between crane selection constraints and criteria were revealed. Furthermore, this study disclosed the selection differences between tower and mobile cranes, and the best practices in crane type and model selection. The main future research opportunities include i) using advanced AI technologies for crane type selection; ii) developing simulation models for tower crane model selection; iii) establishing a hybrid method for crane selection; and iv) extending the selection considerations to a larger spatio-temporal and functional scope. Unlabelled Image • Conducted a critical review of crane selection research via a system boundary based model. • Identified a total of 19 constraints, 70 criteria, and 8 methods for crane selection. • Illuminated the differences between tower crane and mobile crane selection. • Explored the best practices for crane selection by cluster analysis. • Highlighted 4 research trends and 4 research opportunities. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) is a revolutionary construction method. However, the logistics management of MiC has always been a major barrier to the wider adoption of MiC. Nonetheless, this challenge can be tackled by the application of lean techniques, namely, just-in-time (JIT). Numerous studies have identified and evaluated the critical factors (CFs) required to implement JIT; however, there is no consensus among the previous studies on these CFs and their level of importance. Therefore, this research, for the first time, provides a systematic review and meta-analysis of these CFs. The systematic review identifies 42 CFs. To further provide a synthesis analysis of previous studies, a meta-analysis approach is used. This analysis is conducted on the identified CFs to evaluate their importance level and hence rank them. The results indicate that all the 42 CFs are important for applying JIT, of which seven are highly significant for successfully implementing JIT in MiC. Although the ranking obtained by meta-analysis is much more reliable than that provided in the individual studies, however, there is still a high heterogeneity in the results, which depicts the uncertain nature of the construction field. Therefore, sub-group analysis is conducted to investigate this heterogeneity and uncover the hidden patterns in the literature. This is achieved by studying the influence of predictive factors (moderators) on the importance level of CFs. This analysis shows that the economy of a country and the type of project executed are influential factors. The results further indicate that developing economies, in contrast to advanced economies, should pay more attention to three CFs. Also, the results show that seven CFs are much more important in MiC projects than the other project types. This research work is highly beneficial for theory development and for practitioners by identification of significant CFs that warrant management dedication to best apply JIT. Researchers, in particular, can consider the recommendations given here for implementing future meta-analysis studies. Image 1 • Critical factors to apply just-in-time in modular construction were reviewed. • Forty-two critical factors were identified through a systematic review. • The significance level of each critical factor was quantified through meta-analysis. • The study provided a more reliable evaluation and prioritization of these factors. • A sub-group analysis was conducted to study the effects of multiple moderators. [ABSTRACT FROM AUTHOR]

Cranes are the centrepiece of construction equipment during the on-site installation stage of Modular integrated construction (MiC). Any disruption which occurs during this stage can wipe away the efforts made in the production and transportation stages, and hence the privilege of adopting MiC is missed. Therefore, crane operations and planning (COP) research in MiC has witnessed tremendous growth over the past years. This research aims at monitoring and classifying of COP research in MiC to identify its trends and gaps, and hence highlight future research areas. To achieve this objective, this study utilizes a mixed review method of scientometric and systematic reviews. The scientometric review clarifies the most prolific journals, institutions, researchers, keywords correlations and geospatial connection network between research countries in COP. On the other hand, the systematic review analysis shows that there are five main research topics in COP of MiC. Further analysis shows that these topics are approached by six different solution methodologies. Four potential research areas identified in COP are COP in dynamic MiC sites, COP in multi-cranes high-rise MiC sites, risk management of COP and autonomous COP. Unlabelled Image • A holistic review of crane operations and planning (COP) research in modular integrated construction (MiC) is conducted. • A scientometric review reveals collaboration networks of keywords and countries. • COP research in MiC is classified based on problem types and applied methodologies. • A systematic review indicates that five COP problems have been solved by six methodologies. • Future directions are COP in dynamic MiC sites, COP in multi-crane MiC sites, risk management of COP and autonomous COP. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) has been an attractive research topic in the last decade. The adoption of this technology has increased in several countries worldwide, which shows the need for exploring its main research themes, characteristics, benefits, and challenges. Through a three-step research process integrating bibliometric search, quantitative analysis, and qualitative analysis, the literature of MiC was extensively analyzed. The aim was to identify ongoing research trends and current gaps that will benefit future research in this vital domain. The quantitative analysis of literature showed that almost 50% of research in MiC was conducted in only four countries. Science mapping of author keywords showed the connection between MiC and topics, such as simulation, sustainability, and scheduling, which indicated the diverse nature of the existing literature. The qualitative analysis of the literature showed that the categories of building design and management aspects were dominating the research in the area. Gaps in MiC research encompassed lack of quantitative analysis to assess the benefits of various innovative design proposals, lack of cost analysis for MiC to measure its savings, and lack of analysis to adopt the appropriate project delivery method in MiC projects. Future research directions comprise developing models to analyze stakeholder relationships during the design stage, examining contractual relationship among participants, investigating cost comparisons with different construction methods, and assessing the methods for introducing MiC into the curriculum of future engineers. This study provides a road map for future research projects and raise the practitioners' awareness of the latest methodologies and trends in global MiC research. • The study used mixed method analysis to review Modular integrated construction literature. • Journal and conference articles were analyzed. • Literature of MiC was categorized into six main categories and nine sub-categories. • Gaps in MiC literature were defined with analysis for current trends. • Future directions of research were provided. [ABSTRACT FROM AUTHOR]

Inspection process provides beneficial information to assess the current condition of the bridge deck, which is an essential factor in identifying the most appropriate and timely maintenance strategy. Practically, there are a huge number of bridges that need frequent inspection, which makes planning for this process crucial. In this regard, a bridge deck inspection planning (BDIP) model was developed to manage the inspection process of the concrete bridge deck. This model integrates agent-based and discrete event simulation approaches to mimic the sub-processes involved in the inspection using non-destructive technologies. This paper discusses the details and the adopted procedures to build this model starting from real system analysis to model verification, implementation, and validation. Verification and validation of the model demonstrate its ability to accurately estimate inspection duration and cost. The developed model can assist bridge authorities and contractors in planning this process, measure the traffic disorder due to inspection activities, and compare different scenarios for conducting inspection. • Inspection process of bridge deck was analyzed to identify key elements, parameters, and activities in the process. • A conceptual framework for the process was developed to summarize the findings of the process analysis. • Agent based and discrete event simulation approaches were integrated to build a bridge deck inspection planning model. • Model verification demonstrated that the developed hybrid model has been programmed and implemented correctly. • Model validation proved the capability of the developed model to accurately represent the real process. [ABSTRACT FROM AUTHOR]

Modular construction is considered as the future of the construction industry. While the sector is growing rapidly in some areas, there are still barriers to overcome. For more than two decades, several regions and countries, such as mainland China, Hong Kong, and Singapore, were using prefabricated/precast components to construct high-rise buildings in both private and public sectors. Such practices suffered from several limitations, which warrant the need for modular construction. Modular construction method needs to evolve so as to meet different regional requirements and restrictions, namely regulations, economy, market and building types. But the regional requirements and constraints on adopting modular construction remain unexplored. These differences are not widely appreciated. Hence, this research aims to perform comparative analysis of modular construction practices in terms of policies, specifications, and real projects in Hong Kong, Singapore, and mainland China. The results of the comparative analysis indicate that Singapore has developed a relatively effective policy system to help the construction industry embrace modular construction, while the three regions have not issued authoritative specifications. In general, modular construction still needs more support from the government, especially in terms of technical guidance and innovation. Cases studies show that the sustainability performance of modular construction is not balanced in terms of the economic, social and environmental dimensions. The lessons and challenges of modular construction drawn from the cases are also summarized. The construction industry should work closely with the government to achieve sustainable construction of modular building. The developed research on modular construction will increase the understanding of common practices in close countries where the industry in each can benefit from such practices to advance the domain. Image 1042 • This study aims to perform comparative analysis of modular construction practices. • The three regions have not issued authoritative specifications. • The sustainability performance of modular construction is not balanced. • Construction industry should work closely with the government in modular building. [ABSTRACT FROM AUTHOR]

Sewer overflow contains several hazardous contaminants causing adverse health effects and annoyance to the public. Despite this importance, few studies has hitherto been undertaken on examining the odor nuisance and risk of diseases due to contact with untreated overflow. However, quantitative investigation of odor emission from the sanitary sewage overflows has not been addressed. As such, this study aims to scrupulously investigate the deleterious impact of such phenomenon on public health in terms of the aforesaid matters. To this end, a multi-stage methodological approach was employed. Firstly, field data was collected from the vicinity of a wastewater treatment plant for three years, and then the concentrations of H 2 S in the aqueous phase and gaseous phase were estimated based on the environmental parameters. Afterward, the Gaussian aerial dispersion model and Quantitative Microbial Risk Assessment (QMRA) were employed. In parallel, the impact of the exposure to the malodorous H 2 S emitted from overflow cases was assessed. Furthermore, the results obtained from impact assessment were validated using the developed questionnaire survey. From the results obtained, the following major conclusions are drawn: (1) levels of H 2 S (g) near the overflow were high enough to be perceived by individuals, (2) concentrations of NH 3(g) in the ambient air were estimated lower than the perception threshold, (3) the sulfide concentration in the overflow was the most influential parameter with positive linear correlation with the concentration of H 2 S (g) , (4) the concentration of odor causes high annoyance, according to the questioning from the residents near the overflow events (5) exponential dose-response indicated 89–95% infection risk and (6) the good correlation between the estimated values of annoyance and the real annoyance level perceived by the residents proved accuracy of the methodology for estimation of H 2 S concentration and annoyance level. The unique findings obtained from this study guide the environmental decision-makers to take pre-emptive actions, preventing risk of infection and complaints from the residents. • A dispersion model is developed based on the wastewater and climate data. • A predictive-based model is developed to evaluate the annoyance of the odor from the sanitary overflows. • Initial sulfide concentration is the most significant parameter affecting the odor from an overflow. • The infection risk from the exposure to the sanitary overflows is assessed. [ABSTRACT FROM AUTHOR]

Abstract Ground-penetrating radar (GPR) is one of the most commonly used technologies for condition assessment of concrete bridge decks. However, there have been no fully automated algorithms to visualize the data collected with this technique. In such context, the current paper presents a method for a full automation of GPR data visualization and analysis. Based on the background removal, depth correction, synthetic aperture focusing technique (SAFT), and interpolation algorithms, this automated method produces a plan view map of amplitude of GPR signals. In the obtained map, two types of information are observed at the same time. First, as the strongest reflectors of electromagnetic energy, rebars will appear as the most visible. Second, the areas of corrosive environment and, thus, likely corrosion, will be detected as having low amplitude rebar reflections. As a proof of concept, the proposed method was implemented for two bare concrete bridge decks and two concrete bridge decks with asphalt overlays. In all cases, the results obtained were excellent where the maps pinpointed the areas affected by corrosion. These areas were confirmed by other methods of evaluation, such as electrical resistivity (ER), half-cell potential (HCP), chloride analysis of core samples, or visual inspection. With the demonstrated performance, the proposed method is expected to be an excellent alternative to the available methods of GPR data evaluation and visualization. In the future, it should be improved to provide an indication of corrosion severity/probability at each deck location. [ABSTRACT FROM AUTHOR]

The deterioration of water pipes causes significant socio-economic and environmental burdens. Many predictors/factors are used to mitigate such problems by modelling the water pipe deterioration. However, these predictors have not been thoroughly investigated in the literature. This study adopts mixed systematic and scientometric analyses to review the predictors used in modelling water pipe deterioration. Within the study context, the predictors are categorised into pipe-related, soil and corrosion-induced, operational, and environmental. The results reveal that the pipe-related predictors have received the most attention in the reviewed studies, whereas further investigations are required to study long-term changes in the environmental-induced predictors. Accordingly, future research directions are recommended to fill these gaps (e.g., considering sustainability issues, and deploying real-time monitoring, and IoT facilities to enhance data availability. These directions greatly benefit practitioners and researchers from multidisciplinary backgrounds in research directions related to water pipes. [Display omitted] • The article reviews the predictors of the water pipe deterioration models. • A three-tier methodology is used to perform the review process. • Binary response statistic is used to extract the predictors and analysis them. • The fault tree diagram captures the logic behind the effect of the predictors on the water pipe deterioration. • Research gaps are discussed, and future direction are recommended. [ABSTRACT FROM AUTHOR]

This study presents a workable procedure for developing and updating deterioration prediction models based on the results of a Non-Destructive Evaluation (NDE) technique, Ground Penetrating Radar (GPR). To this end, a system reliability based deterioration model was developed for a simply-supported reinforced concrete bridge deck, designed according to the Canadian Highway Bridge Design Code (CHBDC-S6). The superstructure element-level deteriorated conditions for different time intervals were applied in the non-linear finite element model developed for the entire bridge deck, and the system level reliability indices were estimated accordingly. The salt contamination was assumed to affect the top surface, soffits of the deck, and beams due to infusion of de-icing salt material. The resulting primary deterioration model was updated based on a GPR test localized defect map that was obtained for an existing bridge with similar plan dimensions. The results indicate that the assumed conservative uniformly defected superstructure is reliable in this case, that is, the estimated reliability indices for the uniform and localized defect plans do not differ considerably. [ABSTRACT FROM AUTHOR]

The analysis of Infrared thermography (IRT) data to evaluate the condition of concrete bridge decks still relies on user interpretation of thermal images. Delaminations are commonly defined based on the pixels associated with temperatures higher than predefined threshold values that are arbitrarily selected. Such a subjective scheme can produce inconsistent results. The present study proposes a robust automated procedure for detecting and classifying delaminations in concrete bridge decks. IRT testing was conducted in-situ on full-scale bridge decks. The thermal images were enhanced and a stitching algorithm was developed using especially written codes to create a mosaicked thermogram of the entire bridge deck from individual images. Image analysis based on the k-means clustering technique was utilized to segment the mosaic and identify objective thresholds. Hence, a condition map delineating different categories of delamination was created. The proposed procedure was implemented in four full-scale bridge decks and validated via the results of other technologies obtained on the same bridges. The proposed approach resulted in effective identification of delaminated areas within the bridge decks. The findings could stimulate wider acceptance of IRT as a rapid, systematic and cost-effective evaluation technique for critical bridge deck deficiencies, allowing to focus maintenance and repair budgets on most deserving bridge decks. [ABSTRACT FROM AUTHOR]

Spalling is a significant surface defect that can compromise the integrity and durability of concrete structures. The detection and evaluation of spalling are predominantly conducted on the basis of visual inspection (VI) techniques. Although these methods can provide substantial inspection data, they are known to be time-consuming, costly, and qualitative in nature. The objective of this paper is to develop an integrated model based on image processing techniques and machine learning to automate consistent spalling detection and numerical representation of distress in subway networks. The integrated model consists of a hybrid algorithm, interactive 3D presentation, and supported by regression analysis to predict spalling depth. First, RGB images are preprocessed by means of a hybrid algorithm to de-noise the image and enhance the crucial clues associated with spalling. Second, a spalling processor is designed to detect distress attributes, thereby providing 3D visualization model of the defect. And third, the depth and severity of spalling distress are measured using a novel regression analysis model in conjunction with image processing techniques in intensity curve projection. The integrated model was validated through 75 images. Regarding the hybrid algorithm, the recall, precision, and accuracy attained, were 91.7%, 94.8%, and 89.3% respectively. The mean, standard deviation of error percentage in spalling region extraction were 11% and 7.1% respectively, while the variance was 25. Also, the regression model was able to satisfactory quantify the spalling depth with an average validity of 93%. The integrated model is a decision support tool, expected to assist infrastructure managers and civil engineers in their future plans and decision making. [ABSTRACT FROM AUTHOR]

The need for sustainable water infrastructure systems continues to grow as clean water is essential for daily life. Despite efforts to sustain water distribution networks (WDNs), they often experience frequent failures, leading to several environmental, social, and economic consequences. Previous studies have investigated the causes of water pipe failure in different contexts. However, a comprehensive and holistic understanding of these causes is lacking in the literature. Therefore, this study contributes to the existing knowledge by presenting 1) a scientometric analysis of the previous literature, 2) a systematic discussion of the causes, 3) an Analytical Hierarchy Process model and fault tree logic to prioritize and map the causes, respectively, and 4) an overview of techniques used in developing failure prediction models. The scientometric analysis reveals that little attention has been paid generally to the operational causes of water pipe failure. The same trend was supported by the systematic review, which divides a total of 33 causes into three main categories: pipe-related, environment-related, and operation-related causes. This study gives insights to academics and practitioners working in this domain on the contributions of various factors to the failure of water pipes, which would be useful in designing a sustainable and resilient WDN. [ABSTRACT FROM AUTHOR]

• Comprehensive scientometric and systematic reviews of corrosion protection methods for metallic pipelines are detailed. • 122 research articles are used to divide, classify, and discuss external and internal corrosion protection methods. • The functions, limitations, and requirements of corrosion protection methods are reported and discussed. • The purpose of this review is to assist researchers in filling research gaps and focusing on future directions. Metallic pipelines carrying water and/or oil/gas are exposed to deterioration, leaks/bursts, and failures due to corrosion. A suitable corrosion protection technique can prevent corrosion of these metallic pipelines, particularly in hostile environments, and corrosive soils. It can also reduce pipe deterioration, leaks/breaks, and failure, prolong service life, and improve the transportation process. Based on prior studies, this comprehensive review is regarded as an early attempt to cover both external and internal corrosion protection techniques for metallic pipelines in depth. The external corrosion protection techniques are classified into passive, active and hybrid corrosion protection techniques. The passive techniques include coatings, linings, barriers, material design, electrical isolation, inhibitors, and multi-passive techniques. Whereas active corrosion protection techniques include sacrificial anode and impressed current cathodic protections. Active and passive techniques are frequently combined to provide a more comprehensive corrosion protection system against newly discovered corrosion causes or coating degradations. On the other hand, internal corrosion protection techniques include internal coatings/linings/barriers, corrosion allowance, inhibitors/chemical treatments, dehydration, pigging, pipe material selection and flow control. The functions, merits, demerits, limitations/shortcomings, and requirements of corrosion protection techniques, as well as the various considerations that control their use have been covered and discussed. This comprehensive review will assist researchers, practitioners, and the industrial sector in prioritizing their policies in order not only to select the appropriate external and internal corrosion protection technique but also to fill current research gaps and focus on the future directions. [ABSTRACT FROM AUTHOR]

Managing multiple construction projects simultaneously involves sharing and allocating different types of resources, including cash, equipment, and manpower among different concurrent projects. Moreover, ensuring the availability of cash throughout the multiple projects' execution period increases the scheduling complexity. Accordingly, this paper presents the development of an automated system that optimizes the scheduling of multiple construction projects with respect to multiple objectives considering both financial and resource aspects under a single platform. The automated system is named Multi-Objective SCheduling OPtimization using Evolutionary Algorithm (MOSCOPEA). The system aims to help contractors in devising schedules that obtain optimal tradeoffs between different projects' objectives, namely: duration of multiple projects, total cost, financing cost, maximum required credit, profit, and resource fluctuations and peak demand. Moreover, it offers the flexibility in selecting the desired set of objectives to be optimized together. Finally, the developed system is tested and implemented using different case studies of different project sizes obtained from literature to demonstrate its capabilities in scheduling optimization. [ABSTRACT FROM AUTHOR]

• Data of multiple machine intelligent systems were fused to assess the subway condition. • Image processing and machine learning detected and quantified various surface defects. • Monte Carlo simulation was leveraged to estimate the defects' condition indices. • Artificial neural networks and regression analysis attained 0.928 and 0.957 validation performance. • Monte Carlo simulation forecast precision was recorded as 95% percentile. Water intrusion through soil is considered the most significant structural issue and the major cause of concrete degradation in subway networks. An enormous amount of water infiltration may expedite the deterioration mechanisms, such as moisture marks, spalling, scaling, and cracks. These mechanisms can compromise the structural durability and jeopardize people's safety. The condition assessment of concrete infrastructure is predominantly conducted based of visual inspection techniques, which are costly, time-consuming, and error prone. In this research, two main models for the condition assessment of subway networks are proposed. First, image processing techniques and machine intelligent systems are streamlined through successive operations to detect and quantify multiple surface defects automatically. Spatial and frequency domain filters are used to enhance the image clues, in tandem with artificial neural networks (ANNs) and regression analysis (RA) for defect recognition. The Monte Carlo simulation (MCS) is then leveraged to deliver advanced optimization and accurate estimation for each defect's condition index in the subway element. The developed method was implemented on four stations in Montréal subway systems, whereby the performance of ANNs and RA was validated through R2 as 0.928 and 0.957, respectively. Moreover, the MCS forecast precision was recorded as 95% percentile, which proves the efficacy of the developed models. This research provides insights for infrastructure managers about maintenance and intervention plans in order to prioritize their spending policies. [ABSTRACT FROM AUTHOR]

Municipal decision-makers struggle to reach effective solutions in asset management. The complexity of this issue increases when trying to consider more than one asset. This article proposes an approach that can optimize integrated decision-making for road, sewer, and water municipal assets. The proposed optimization is accomplished using risk assessment and a performance rating model. The model was tested on the infrastructure network of the city of Guelph and has resulted in a 36% risk dollar reduction under full integration. The model has also shown improved budget-savings when compared to the conventional silos approach. The findings aid municipal decision-makers in maximizing the benefits of their limited budgets, which will increase the number of infrastructure projects. Additionally, the results of the proposed model unveil more opportunities. Specifically, the use of more advanced computational methods along with exploring techniques that allow the user to consider multiple corridors within one repair scenario. • Novel approach for integrated infrastructure repair optimization. • Optimization via risk and performance-based assessments. • Integrating road, water, and sewer municipal infrastructure. • Provided 25% risk reduction in a real-life case study. [ABSTRACT FROM AUTHOR]

A reliable leak detection approach is necessary for reducing water loss caused by leakages. Our research reviews the existing acoustic leak detection approaches, including devices for collecting sound, signal processing methods, and ways to detect the presence of leakages. Firstly, a scientometric analysis of relevant bibliographic data from the Scopus database in the last thirty years is carried out. Secondly, based on the results of the scientometric review, the technologies of acoustic leak detection have been systematically reviewed. Acoustic leak detection approaches are classified into non-real time and real-time methods and are thoroughly discussed. Thirdly, acoustic signals collection through noise loggers, wireless sensor networks, accelerometers, hydrophones, or fiber optic sensors is elaborated. Fourthly, signal features extraction using different methods, including time-domain analysis, frequency domain analysis, time-frequency analysis, correlation analysis, and other pre-processing methods are explained. Fifthly, the determination of the presence of leaks using various detection methods is detailed. This paper provides a useful reference for academia and industry for the further development of advanced acoustic leak detection methods. • Acoustic signals are collected by noise loggers, wireless sensor networks, and so on. • Features are extracted by different analysis and signal processing techniques. • Leaks detected by a threshold or classification model. • Unresolved challenges: avoiding false alarms, detecting weak leaks, and so on [ABSTRACT FROM AUTHOR]

Pipelines daily transport and distribute huge amounts of oil and gas across the world. They are considered the safest method of transporting oil and gas because of their limited number of failures. However, pipelines are subject to deterioration and degradation. It is therefore important that pipelines be effectively monitored to optimize their operation and to reduce their failures to an acceptable safety limit. Numerous models have been developed recently to predict pipeline conditions. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. Hence, this paper presents the development of models that evaluate and predict the condition of offshore oil and gas pipelines based on several factors besides corrosion. The models were developed using artificial neural network (ANN) technique based on historical inspection data collected from three existing offshore oil and gas pipelines in Qatar. The models were able to successfully predict pipeline conditions with an average percent validity above 97% when applied to the validation data set. The models are expected to help pipeline operators to assess and predict the condition of existing oil and gas pipelines and hence prioritize the planning of their inspection and rehabilitation. [ABSTRACT FROM AUTHOR]

• A four-point bending technique for measuring interface fracture toughness between a soft thin-film and a stiff substrate is developed. • The applicability of the technique is verified numerically. • The requirement for the specimen dimensions is proposed. • The technique is successfully applied to characterize the interface fracture toughness between a soft epoxy coating on a steel substrate. Interface fracture toughness, also called work of adhesion or adhesion energy, is a quantity characterizing the resistance of an interface between two adhered solids against interfacial delamination. Determining the interface fracture toughness should be of great value to the studies on the related adhesion and delamination problems. Four-point bending test is an experimental approach that was originally used to measure the flexural stiffness and fracture toughness of monolithic materials. It was also used to characterize the fracture toughness of the interface when a bi-layered notched composite beam specimen is adopted (Int. J. Frac. 1989; 40: 235). However, this method does not work very well when a thin and soft material is encountered because the interface delamination cannot be triggered easily. To address this problem, in this paper, we revise the configuration of the four-point bending specimen from bi-layer to tri-layer by imposing an additional stiffer layer on the top of the thin and soft layer. The analytical solution to the energy release rate of the preexisting interfacial crack is revisited. Finite element analysis is carried out to assess the applicability and limitations of the analytical solution. With the modified four-point bending specimen, the interface fracture toughness between an epoxy coating and a steel substrate is successfully measured from the critical load that leads to the delamination of the preexisting interfacial crack. [ABSTRACT FROM AUTHOR]

• A novel Exfiltration Severity Index (ESI) is proposed to assess sewer pipelines. • A two-tiered model is applied to predict the exfiltration occurrence and severity. • GridSearchCV is employed to optimize the accuracy of the prediction models. • Shapley analysis is utilized to assess the factors affecting sewer exfiltration. Effective management of aging sewer pipelines requires accurate analysis of sewer pipeline exfiltration. Previous research studies have not paid attention to proposing an index to represent the exfiltration severity. To this end, this study introduces a novel Exfiltration Severity Index (ESI) by considering the frequency and severity of defects captured from Closed-Circuit Television (CCTV) reports. To address the need for a proactive tool that automates the exfiltration estimation and reduces dependence on CCTV reports, we leverage Machine Learning (ML) and Deep Learning (DL) models to predict sewer exfiltration occurrence and severity. In this regard, our proposed methodology comprises a series of steps that start by computing the ESI of pipeline segments considering the frequency, type, and severity of defects. After that, physical, environmental, and climatic factors influencing pipeline exfiltration are gathered and aggregated to build predictive models. We compare the performance of six ML models and two DL models, developed in two tiers to predict exfiltration occurrence and severity, respectively. The hyperparameters of each model are optimized using GridSearchCV to enhance prediction accuracy. Among the eight algorithms, the light gradient-boosting machine performs best, with 71% and 85% accuracy in the first and second tiers, respectively. Furthermore, our study investigates the influence of various factors on pipeline exfiltration and reveals that pipe diameter and population have the most significant impact on exfiltration occurrence and severity. Our method provides a valuable tool for managing sewer pipeline exfiltration and can be utilized to prioritize sewer network maintenance and repair efforts. [ABSTRACT FROM AUTHOR]

● Essential factors are discovered for the deterioration modeling of water distribution pipelines. ● A new machine learning approach is proposed to predict the failure risk of water pipelines. ● The proposed method can accommodate limited, high-dimensional, and highly censored data. ● The advantages and drawbacks of the proposed method and other state-of-the-art machine learning paradigms are discussed. Population growth and urbanization worldwide entail the need for continuous renewal plans for urban water distribution networks. Hence, understanding the long-term performance and predicting the service life of water pipelines are essential for facilitating early replacement, avoiding economic losses, and ensuring safe transportation of drinking water from treatment plants to consumers. However, developing a suitable model that can be used for cases where data are insufficient or incomplete remains challenging. Herein, a new advanced meta-learning paradigm based on deep neural networks is introduced. The developed model is used to predict the risk index of pipe failure. The effects of different factors that are considered essential for the deterioration modeling of water pipelines are first examined. The factors include seasonal climatic variation, chlorine content, traffic conditions, pipe material, and the spatial characteristics of water pipes. The results suggest that these factors contribute to estimating the likelihood of failure in water distribution pipelines. The presence of chlorine residual and the number of traffic lanes are the most critical factors, followed by road type, spatial characteristics, month index, traffic type, precipitation, temperature, number of breaks, and pipe depth. The proposed approach can accommodate limited, high-dimensional, and partially observed data and can be applied to any water distribution system. [Display omitted] [ABSTRACT FROM AUTHOR]

• Optimization of fleet size combination in containers terminal is presented. • The optimization minimizes both vessel turnaround time and handling cost. • A new double-cycling handling strategy is incorporated in the optimization process. • The new strategy saved 20% in both time and cost and increased productivity by 25% Every few years, larger containerized vessels are introduced to the market to accommodate the increase in global trade. Although increasing the capacity of vessels results in maximizing the amount of imported and exported goods per voyage, yet it is accompanied with new challenges to terminal planners. One of the primary challenges is minimizing the vessel turnaround time with the least possible cost. In this context, this paper presents the development of a multi-level optimization model using the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal or near-optimal fleet size combination of the different container handling equipment used in the terminal. The model aims to minimize two conflicting objective functions, namely, vessel turnaround time and total handling cost. Furthermore, the model considers a double-cycling strategy for the container handling process to achieve increased productivity and eventually more reduction in the vessel turnaround time. The model was implemented on a real-life case study to demonstrate its efficiency and the benefit of employing the double-cycling strategy compared with the traditional single-cycling strategy. The results demonstrated the efficiency of employing the double-cycling strategy by providing a reduction of above 20% in both the vessel turnaround time and the total handling cost and an increase of above 25% in the productivity when compared to the traditional single-cycling strategy. [ABSTRACT FROM AUTHOR]

Leakages from water pipelines cause economic losses and environmental hazards. Despite the damages, it is challenging to avoid leaks throughout the lifetime. However, leak detection and localization, especially in real-time, minimize the damage. Owing to the recent advances, the micro-electromechanical systems (MEMS) based technologies have started to gaining recognition for water network monitoring in real-time, however, a systematic literature review to analyze the existing research trends, technological advances, and future research opportunities are largely missing. This study has based its investigation on three main MEMS-based technologies for real-time monitoring: MEMS sensors wireless networks, MEMS accelerometers, and MEMS hydrophones. Firstly, a scientometric analysis is conducted to 1) retrieve relevant research articles through Scopus, Web of Science, and Google Scholar, 2) visualize the publication trends, and 3) analyze the science mapping of influential authors, countries, organization, and top keywords occurrences. Secondly, qualitative discussions are made on research themes and sub-themes within three technologies: 1) MEMS WSNs are classified into static and mobile sensor-based wireless sensor networks. Seven sub-themes are categorized under static sensor-based wireless sensor networks such as PIPETECT, whereas three sub-themes are categorized under mobile sensor-based WSNs such as TriopusNet; 2) MEMS accelerometers are categorized into accelerometers based machine learning models and wireless systems; and 3) MEMS hydrophones are represented under one category. Thirdly, nine research opportunities including automated models, on-field real network-based experimental studies, optimal placement of sensor nodes for energy savings in wireless sensor networks, and a comparative analysis of real-time technologies are revealed. This study enhances the familiarity of early researchers with the application of MEMS-based technologies for leak detection and localization and provides seasoned researchers with a platform for future research development. • MEMS-based technologies create opportunities to enhance real-time leak detection and localization in water supply networks. • MEMS-based wireless sensor networks, accelerometers, and hydrophones are classified into different themes and sub-themes. • The bibliometric search process, scientometric analysis, and qualitative analyses are discussed in detail. • Future research directions are provided. [ABSTRACT FROM AUTHOR]

Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing and scheduling using multi-objective optimization in particular. However, up to the knowledge of the authors, there is no research conducted to evaluate and assess the performance of the multi-objective optimization techniques employed in this domain. Thus, the current study developed a finance-based scheduling multi-objective optimization model for multiple projects using the elitist non-dominated sorting genetic algorithm (NSGA-II). Further, the obtained results were compared with the results obtained by solving the same problem in another study from the literature using the multi-objective optimization technique of strength Pareto evolutionary algorithm (SPEA). Benchmarking was conducted based on the quality of the obtained solutions and performance. The results indicated that the NSGA-II outperformed SPEA in most aspects with achieved improvements range from 1.7% to 98.2%. • Finance-based scheduling multi-objective optimization NSGA-II model was developed. • NSGA-II was benchmarked against SPEA using a case study from the literature. • Benchmarking aspects are quality solutions, convergence, diversity, and computational time. • NSGA-II outperformed SPEA concerning the majority of the benchmarking aspects. • A procedure for benchmarking new multi-objective techniques was established. [ABSTRACT FROM AUTHOR]

• A model to detect and map air/water voids in subway systems is developed. • The methodology is based on image analysis of GPR profiles. • A deterioration map that delineates air/water voids zones is generated automatically. • The proposed scheme is applied to a concrete tunnel in Montreal Metro. • Validation via digital images, coring, infrared thermography and laser techniques. Water infiltration through soil is deemed the most serious problem and the main cause of concrete degradation in subway facilities. A huge amount of water intrusion may accelerate the deterioration mechanisms, such as rebar corrosion, spalling, and water voids. Such mechanisms can compromise the structural integrity and jeopardize public safety. The inspection and assessment of concrete structures are predominantly conducted based on visual inspection technologies. Although, these techniques may be consistent in detecting surface defects, e.g. cracks and spalling, they fall short in identifying subsurface distresses such as air voids, and water voids. Ground Penetrating Radar (GPR) has been extensively utilized for probing concrete infrastructure. Nevertheless, the deterioration mapping of air voids and water voids in concrete has seldom been performed. The objective of this paper is to develop an integrated model based on image processing of GPR profiles to automate air/water voids detection and mapping in subway systems. First, an automated localization scheme is developed to create a consistent inspection pattern. Second, subsurface data are collected in a subway facility, and processed using the image-based analysis technique. Third, the locations and dimensions of the detected defects are mapped to evaluate the severity of deterioration. The developed method is implemented on a tunnel in Montreal subway network. Then, validated via field inspection, digital images, coring samples, infrared thermography and 3D laser techniques. The validation outcomes reflect a strong correlation and compatibility with the generated GPR-based maps. The proposed framework is expected to assist infrastructure managers in identifying critical deficiencies and by focusing constrained funding on most deserving assets. [ABSTRACT FROM AUTHOR]

A large portion of natural gas is transferred through transmission pipelines. For these facilities, in the United States, external corrosion has led to more than 1700 failures in the past decades, causing a property damage of approximately $189 M. Such numbers highlight the importance of maintaining such facilities in safe conditions to postpone corrosion failure. Given such criticality, addressing pipeline availability is of high importance from economic consequences perspective for a national economy. However, most of the developed methods merely rely on considering costs, reliability or condition levels as maintenance decision criteria and ignore the importance of continuity of operation and pipeline availability. In this research, a maintenance planning framework is proposed for external corrosion of gas transmission pipelines through an availability-centered reliability-based maintenance planning procedure. This framework is based on the pipeline reliability profile obtained from a Monte Carlo simulation. This simulation is carried on a previously developed failure prediction model for gas transmission pipelines buried in the Great Plains region of the U.S. and considers failure uncertainties. In addition, a discrete event simulation (DES) approach is proposed to assess an availability-cost indicator of different maintenance scheduling alternatives for the case study of a 24-inch gas pipeline. This research reveals that a combination of wrap and replacement maintenance actions at the service life of 30.1 and 40.5 years respectively, is the most effective maintenance alternative in terms of improvement of availability per unit cost for the presented case study. This framework can help pipeline professionals in maintaining such facilities by considering their criticality using an availability-based maintenance scheduling approach. Such method can complement the conventional cost-based industry practices. • If added to inspections, maintenance planning models help avoid excessive inspections. • As a critical asset, considering gas pipeline availability helps reduce shutdowns. • As decision criteria, availability change per cost provides a balance between the two. • Combination of composite wrap with replacement is identified as the most effective. [ABSTRACT FROM AUTHOR]

Ranking and prioritizing pavement infrastructure for maintenance and rehabilitation have become major undertakings for several departments of transportation around the globe. This is a complex decision-making problem because multiple and conflicting criteria can contribute to the assessment. Multi-criteria decision analysis (MCDA) techniques evaluate the trade-off between several quantitative and qualitative criteria and facilitate complex decision-making. This research introduces a framework based on MCDA to support pavement management decision making, while quantifying emerging sustainability-related factors such as safety, noise, and pollution in the decision-making process. The framework features include 1) identifying pavement management main decision elements: objectives, criteria, and attributes by detailing the problem with a five-level hierarchy structure; 2) employing combined analytic hierarchy process and multi-attribute utility theory to develop representative set of utility functions; and 3) ranking and prioritizing large networks of pavement sections while incorporating sustainability-related criteria. Data used to assess the decision criteria and develop the utility functions is extracted by means of a questionnaire survey completed by professionals working in the field of pavement management. The proposed method is applied to a case study consisting of ten pavement sections extracted from the long-term pavement performance database, wherein the sections are ranked based on their attributes. Sensitivity analysis is performed to evaluate the impact of the different criteria on the ranking process. The proposed method has shown potential in ranking pavement networks based on the identified criteria. Future work can test the performance of the proposed methodology with a full-scale pavement network and apply it to other civil infrastructure assets to evaluate its performance with different types of projects. • Pavement management decision elements (objectives, criteria and attributes) are analyzed. • Integrated AHP-MAUT decision support framework for pavement management is developed. • An intuitive procedure to assess utility is implemented to develop the needed utility functions. • Data extracted from LTPP database is used to demonstrate the application of the proposed framework. • Sensitivity analysis is conducted to assess robustness of the developed method. [ABSTRACT FROM AUTHOR]

Estimating wastewater treatment plants' (WWTPs) influent parameters such as 5-day biological oxygen demand (BOD 5) and chemical oxygen demand (COD) is vital for optimizing electricity and energy consumption. Against this backdrop, the existing body of knowledge is bereft of a study employing Artificial Intelligence-based techniques for the prediction of BOD 5 and COD. Thus, in this study, Gene expression programming (GEP), multilayer perception neural networks, multi-linear regression, k-nearest neighbors, gradient boosting, and regression trees -based models were trained for predicting BOD 5 and COD, using monthly data collected from the inflow of 7 WWTPs over a three-year period in Hong Kong. Based on different statistical parameters, GEP provides more accurate estimations, with R2 values of 0.784 and 0.861 for BOD 5 and COD respectively. Furthermore, results of sensitivity analysis undertaken by monte Carlo simulation revealed that both BOD 5 and COD were mostly affected by concentrations of total suspended solids, and a 10% increase in the value of TSS resulted in a 7.94% and 7.92% increase in the values of BOD 5 and COD, respectively. It is seen that the GEP modeling results complied with the fundamental chemistry of the wastewater quality parameters and can be further applied on other sewage sources such as industrial sewage and leachate. The promising results obtained pave the way for forecasting the operational parameters during sludge processing, leading to an extensive energy savings during the wastewater treatment processes. • Different machine learning techniques for predicting BOD 5 and COD were employed. • GEP showed superior performance against other machine learning-based techniques. • BOD 5 and COD were correlated with TSS, NH 3 , OrgN, InorgP and OrgP. • The sensitivity analysis was undertaken through Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

In sewer networks, the economic effects and costs that result from a pipeline failure are rising sharply. As a result, there is huge demand for inspection and rehabilitation of sewer pipelines. In addition to being inaccurate, current practices of sewer pipelines inspection are time consuming and may not keep up with the deterioration rates of the pipelines. This papers presents the development of an automated tool to detect some defects such as: cracks, deformation, settled deposits and joint displacement in sewer pipelines. The automated approach is dependent upon using image-processing techniques and several mathematical formulas to analyze output data from Closed Circuit Television (CCTV) camera images. The automated tool was able to detect cracks, displaced joints, ovality and settled deposits in pipelines using CCTV camera inspection output footage using two different datasets. To examine the performance of the proposed detection methodology, confusion matrices were constructed, in which true positives for crack, settled deposits and displaced joints were 74%, 53% and 65%. As for the ovality, all defects in the images were detected successfully. Although these values could indicate low performance, however the proposed methodology could be improved if additional images were used. Given that one inspection session can result in hundreds of CCTV camera footage, introducing an automated tool would help yield faster results. Additionally, given the subjective nature of evaluating the severity of defects, it would result in more systematic outputs since the current method rely heavily on the operator's experience. [ABSTRACT FROM AUTHOR]

Ground Penetrating Radar (GPR), a locating instrument by design, has attracted much interest as a non-destructive inspection technique for bridge decks due to its ease of use and ability to detect corrosion. The most commonly used technique to interpret GPR data is numerical analysis. The assessment method relies on the fact that corroded reinforcing bars, which cause signal attenuation, can be easily identified using GPR scans. However, several other unrelated factors can attenuate GPR signals such as reinforcing bar depth, surface anomalies and reinforcing bar spacing. These anomalies can be falsely interpreted as deterioration using numerical analysis. Image-based analysis overcomes these drawbacks through analyzing the entire GPR profile while considering prior knowledge of the structure characteristics, thus determining the state of bridge deck. The main objective of this research is to develop a systematic framework of the image-based analysis. The framework is supported by various GPR profiles depicting several causes of signal attenuation and their analysis with respect to deterioration or rebar corrosion status. Two case studies are presented and analyzed using numerical analysis, image-based analysis, and other Non-Destructive Evaluation (NDE) techniques. The results are compared to confirm the validity of the proposed methodology. Further validation is done using concrete cores. The developed approach is believed to assist transportation agencies in a more informed decision making process through highlighting areas of actual deterioration. [ABSTRACT FROM AUTHOR]

Multimodal logistics (ML), which involves multiple transportation modes, has been increasingly used in many Modular integrated Construction (MiC) projects. However, the literature lacks decision support systems (DSS) to simulate, analyze, and optimize ML in MiC (ML-MiC). This paper fills this gap by achieving the following objectives: 1) simulate the internal operations of ML-MiC stakeholders (e.g., manufacturers, logistics service providers, contractors) and their interactions; 2) identify the significant decisions that impact the key performance measures (KPMs) of ML-MiC; and 3) obtain the near-optimum decisions that improve the sustainability of ML-MiC. These objectives are achieved by developing a holistic modelling approach that integrates three methods. First, hybrid multi-agent simulation models the communications between ML-MiC stakeholders and their internal operations. Second, design of experiments (DOE) reveals the main and interaction effects between logistics and construction decisions that significantly affect KPMs, such as the project duration, total costs, and carbon emissions. Third, metamodeling finds the near-optimum logistics and construction decisions (e.g., trucks' number, their dispatching time, ship capacity, inventory, resource planning) that enhance KPMs. The developed approach is applied to a real case study. The DOE analysis indicates that some logistics decisions significantly influence construction KPMs (e.g., project duration, construction costs, construction emissions) and vice versa, calling for more collaboration between stakeholders. Also, the optimized solutions reduce the project duration, total costs, and emissions by 28%, 50%, and 17%, respectively. This paper contributes by integrating three methods to model ML-MiC and enable its stakeholders to discern the impact of their decisions on multiple KPMs and optimize them toward more sustainable MiC. Given this paper's findings, future researchers are urged to investigate the success factors and barriers to applying ML in MiC. Also, the paper emphasizes the need to develop DSS that achieve a win-win collaboration and enhance communication between ML-MiC stakeholders. • Multimodal logistics (ML) are used to import modular units from overseas factories. • The literature lacks models to assess, analyze and optimize ML-related decisions. • A developed multi-agent model evaluates multiple project performance measures (PMs). • A diagnostic analysis reveals the significant ML decisions that impact each of PMs. • Metamodeling finds the optimum ML decisions to improve PMs of modular construction. [ABSTRACT FROM AUTHOR]

Developing a sustainable community is mainly contingent upon energy infrastructure driven by energy alternatives. It has been attempted to accomplish energy sustainability by just ranking energy alternatives. In the current work, an integrated decision support tool (IDST) and multi-level hierarchical structure (MLHS) are developed to instigate new notions for energy alternatives. The developed IDST is unique in dealing with two-dimensional assessment information and can also concurrently analyze hybrid information. The MLHS comprises eighteen attributes grouped into five sustainability aspects (operational, economic, technological, environmental, and social) to assess energy alternatives, including gas, coal, nuclear, solar, hydro, wind, and biomass. Firstly, the importance of attributes is calculated using the optimal weighting approach. Then, the ranking of alternatives is determined and validated. Lastly, the multi-facet performance of alternatives is assessed in terms of distributed performance and indexing using the probabilistic evidential reasoning algorithm, and the acceptability of alternatives is classified. The results revealed hydro is the most favorable energy alternative for achieving sustainability, followed by solar and wind, with a 'very good' index overall. The developed framework is useful for designing sustainable hybrid energy infrastructures and for intergovernmental organizations to draft long- and short-term policies for achieving the energy sustainability targets of SDG-7. • Constructed critical success factors for energy system quantitatively. • Evidential reasoning driven integrated multi-attribute decision tool is used. • Two-dimensional and hybrid assessment information of factors is used concurrently. • Sustainability is evaluated using some new notions in energy domain. • Long- and short-term policy recommendations for energy sustainability in Pakistan. [ABSTRACT FROM AUTHOR]