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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]

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]

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]

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]

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]

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]

• Mechanical properties in centrifugal casting ductile iron pipes vary across thickness. • Equations are proposed to evaluate tube properties in ring hoop tension test. • Casting defects cause material inhomogeneity in ductile iron pipe walls. • Round bars in pipeline standards tend to overestimate actual pipe behaviour. Ductile iron pipes are widely used in pipe manufacturing for water and sewage transmission and distribution. In pipeline standards such as EN545, the pipe material is assumed isotropic and its mechanical properties are determined by tensile testing of round bars extracted along the longitudinal direction. This study experimentally examined the mechanical properties of centrifugal casting ductile iron pipes, focusing on the effects of sampling orientation, location, preparation, and test methodology. A ring hoop tension test (RHTT) was designed to evaluate circumferential properties. Force analysis of RHTT was performed and theoretical equation was derived to quantify the friction coefficient that existed between the coupon specimen and the loading fixture. A numerical study was conducted to further validate the effectiveness of the proposed theory. The test results indicated that the pipe mechanical property was inhomogeneous across the wall thickness, being inferior in the internal section and superior in the middle and external sections. This inferior layer would develop crack first and lead to subsequent outward propagation. This phenomenon led to a substantial degradation in the overall mechanical performance of the entire specimens, in comparison to the material in the middle portion. The material exhibited better performance in the circumferential direction compared to the longitudinal direction in terms of its mechanical properties, such as tensile strength and ductility. Flattened specimens showed enhanced strength and reduced ductility compared to the base pipe material. Fractographic and metallographic analyses revealed the existence of casting defects of porosity and agglomerated graphite in the internal section, which were the primary cause of material inhomogeneity. The round bars suggested per EN545 tended to overestimate the actual mechanical behaviour of ductile iron pipes, and may not be a true representation of the finished product of pipes. Flattened specimens as per ASTM E8/E8M were not recommended for ductile iron pipe material assessment, as the flattening process altered the stress–strain characteristics significantly. [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]

● 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]

• 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]

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]

• 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]

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]

• Critical causes of RMAA-related accidents are identified. • Novel hybrid fuzzy-based algorithms are employed for result accuracy enhancement. • Considerable inherent interrelationships exist among RMAA-related causal factors. • "Limited financial/safety resources of SMEs" is the most influential causal factor. • The identified causes are prioritized using IVIF-DEMATEL-ANP method. Despite the recent improvements made to the area of occupational health and safety (OHS) within the construction sector, the Repair, Maintenance, Minor alteration, and Addition (RMAA) works have been given scant attention. In this study, given the significance of the injuries reported in the RMAA sector, a meticulous investigation is conducted into the causal factors contributing to the related accidents by capturing their causal interrelationships together with their importance levels. To this end, first, a comprehensive list of factors contributing to RMAA accidents was obtained through an extensive literature review and experts' interviews. Then, through the lenses of qualified relevant experts in Hong Kong, the proposed interval-valued intuitionistic fuzzy (IVIF) DEMATEL and IVIF analytic network process were employed to respectively uncover the cause-and-effect relationships among these factors and prioritize them. The findings show that "the lack of assessment and praising of workers' OHS understanding and performance," "the high turnover rate of workers resulting in difficulties in providing safety training and education," and "lack of safety training for workers" are the most critical causes to be given full attention by construction safety managers. The methodological approach proposed in this study brings about two theoretical contributions: unraveling interrelationships existing among the causal factors, and prioritization of them considering their interrelationships. The findings reported in this study also aid decision-makers in improving the critical causal factors in a way to enhance the OHS of RMAA sector. [ABSTRACT FROM AUTHOR]

One of the main components of smart and sustainable urban management is well-performed sewer networks. Therefore, maintaining and enhancing the functionality of sewer projects at an affordable cost are major challenges for decision-makers. However, the embracement of the Value Engineering (VE) concept to assess new sewer designs has not yet been fully embraced especially in developing countries. Thus, this study introduces a systematic VE approach for major sewer projects. A computer model based on six phases of VE methodology was developed, and named "Value Engineering Model" (VEM). To show its efficacy, it was applied to a real-life case study, leading to the following major contributions to the body of relevant knowledge: (1) unraveling the logical relations between the project functions, (2) a detailed life cycle cost analysis (LCCA), (3) a weighted evaluation matrix towards facilitating the decision-making procedure and (4) achieving an approximate 36% reduction in the sewer's project cost as compared to the common VE analysis technique. The developed model serves as a guide for design engineers and decision-makers in sewer projects. It will enable achieving the embracement of smart and sustainable drainage systems within cities at a greater pace. The results of this study will be a guide for decision-makers to reduce costs and improve sustainability by introducing VEs in the Egyptian sewer industry. • A new inclusive value engineering model for major sewer projects is developed. • The logical relations between the project functions are unravelled. • A detailed life cycle cost analysis (LCCA) of sewer projects is provided. • A weighted evaluation matrix towards facilitating the decision-making procedure is developed. • An approximate 36% reduction in the sewer's project cost is achieved. [ABSTRACT FROM AUTHOR]

The present study proposes a novel hybrid methodological approach for meticulously investigating the factors causing Sewer Overflow (SO). The proposed framework is based on a systematic literature review, experts' interviews, PLS-SEM statistical technique, and system dynamic modeling. Based on a large number of data collected from experts having rich experience in developed countries, three major findings are obtained: (1) three main factors and eighteen sub-factors are the main culprit of SO occurrence, (2) under design pipe diameter, blockages, and infiltration and inflow are the most significant sub-factors within the respective clusters, and (3) physical-related sub-factors are the most influential causes of SO occurrence under a dynamic environment. The findings attained in this study offer an insightful account for the concerned environmental decision-makers on coming up with further fecund measures towards reducing the magnitude of SO, preserving our environment from the occurrence of such harmful incidents. [Display omitted] • Through extensive literature review and experts' interviews, the contributors to sewer overflow are identified. • Through the PLS-SEM, the significance of identified contributors is quantified. • Using the System Dynamic Modeling, complex interrelationships among the contributors are uncovered. • Several theoretical and managerial implications are offered for the concerned decision-makers. [ABSTRACT FROM AUTHOR]

• A meticulous investigation into the casual factors contributing to construction accidents in a developing country is investigated. • A novel fuzzy Delphi method for identifying and prioritizing the critical causes is proposed. • The cause-and-effect relationships among the critical casual factors are unraveled using the FDEMATEL technique. • Twenty-three sub-causes are seen to be the major culprits for the occurrence of related accidents. • Organizational and workplace and environmental causes turn out to be the most influential causes. Though some studies have explored the causes of accidents on construction sites, the interdependency among the underlying causes remains elusive. This undermines the efficacy of any decision made by safety experts in reducing accidents' impact. To fill this gap, a hybrid fuzzy-based framework is developed in this study to comprehensively identify and prioritize critical causes, as well as map interrelationships among these causes. The proposed framework is based on the infusion of the Pentagonal Fuzzy Delphi Method (PFDM) and Fuzzy DEMATEL techniques. Findings show that six main causes and twenty-three corresponding sub-causes (out of forty-seven identified ones) are the major culprits for the occurrence of related accidents. Furthermore, it was revealed that "organizational" and "workplace and environmental" causes turn out to be the most influential causes, while inappropriate safety guidelines and policies, poor safety management system, poor safety culture, poor safety knowledge of management team, the financial instability of firms, and corruption were the predominant sub-causes affecting the related accidents' impact. To validate findings, several interviews with senior experts are undertaken. The outcomes of this study are vital for the concerned safety decision-makers by highlighting the influential causes debilitating the safety and health of involved workers. [ABSTRACT FROM AUTHOR]

A smart and sustainable drainage system is strongly dependent on appropriate functioning of sewer pipelines existing within the infrastructure of cities' networks. Although numerous studies have been conducted on the sewer networks, the existing body of literature lacks a comprehensive review paper that contains scientometric analysis, critical review, and meta-analysis in one holistic frame. To fill this gap, this paper provides a review of sewer deterioration models and factors causing sewer pipeline deterioration through a hybrid review approach. Having meticulously reviewed the studies undertaken hitherto using the adopted hybrid review approach, the following conclusions are noted: (1) The meta-analysis results indicate that the most important factors impacting sewer pipeline deterioration include operational defect, pipe shape, pipe material, waste type, structural defect, and hydraulic conditions, (2) Factors and defects that cause sewer deterioration have a varying degree of impact on the deterioration process; a good understanding of their individual impact will improve the sewer infrastructure performance and save cost, and (3) Countries from Africa, South America, the Middle East, and Asia are lagging in this research area, although there might be some justification for this, such as availability of funds and research priority. Sewer infrastructure is an important part of the environment, and keen attention needs to be given to it; therefore, there is a need for more international research collaborations. The findings obtained from this review paper are expected to facilitate the adoption of sagacious policies by the concerned decision-makers, paving the way for realizing smarter and more sustainable cities. • An inclusive scientometric analysis on the papers published on sewer pipeline deterioration is undertaken. • An in-depth discussion on the methods used in modeling sewer pipeline deterioration is provided. • Critical factors playing role in the deterioration of sewers networks are identified. • The significance levels of identified critical factors are unraveled using meta-analysis approach. • Current research gaps together with the potential research streams to be undertaken are put forward. [ABSTRACT FROM AUTHOR]

Modular Integrated Construction (MiC) has recently grabbed the attention of numerous researchers and practitioners across the globe, even though their adoption on construction sites has led to some critical safety hazards that differ from those of common construction projects. In this regard, cranes are the most widely utilized equipment used in MiC projects; however, there have not been any studies carried out on the safety issues of cranes in such projects. Lack of such consideration not only leads to overshadowing the greater adoption of MiC-based technologies, but also worsens the safety and health of related workers. Considering this, a Crane Safety Index (CSI) is developed in this study to improve the Occupational Health and Safety (OHS) of such projects, which is based on experts' interviews together with the exploitation of the analytical hierarchy process. To validate the efficacy of the proposed CSI, its application to the case of three MiC-constructed projects was considered. Applying the CSI developed to the selected projects, the following contributions are noted: (1) identification of safety factors playing role in the safety of crane operations that are peculiar to the MiC projects, (2) obtaining a specific safety score for crane operations in MiC projects, and (3) determination of fruitful technologies improving the OHS of related operations. The outcomes of this study provide the related safety decision-makers with an inclusive plan for promoting the embracement of sustainability at a greater pace by improving the well-being of workers. • A crane safety index related to the MiC projects is developed for the first time in the relevant literature. • All the factors that are peculiar to the safety of crane operations in MiC projects are identified. • The identified factors are analyzed using Analytical Hierarchy Process. • Bunch of fruitful recommendations to improve the respective OHS operations are put forward. • The suitability of the developed safety index is shown through its application to case studies. [ABSTRACT FROM AUTHOR]

Green buildings (GBs) have been adopted mainly to minimize the negative effects of the design, construction, and building operations on the environment. However, the GB-related activities have been found to be jeopardizing the occupational health and safety (OHS) of related projects, thereby debilitating the safety and health of respective crew members. Despite such vital issues, no study has been conducted yet to investigate the safety issues associated with GB construction projects in developing countries, where the inclination towards the adoption of GB is on the rise. Using this as a point of departure, the present study assesses the safety risks caused by GB projects with the use of a fuzzy-based RAM, through the lenses of the experts in Kazakhstan. The proposed RAM integrates Fuzzy Delphi Method (FDM) and Fuzzy Best Worst Method (FBWM). The FDM results clearly indicated that sustainable buildings continue to endanger the safety and health of respective workers, while fall from height and overexertion are found to be the leading causes of GB-associated risks using the FBWM. Despite the research limitations, this study prudently assessed the OHS-related risks to the LEED-based (the most widely used certification in the country) projects, and offered a fertile ground for future research to be conducted in developing economy settings. The findings indicated that the construction key players need to pay more attention to the riskiest GB-related hazards by investing their efforts in making the built environment truly sustainable in a not-too-distant future, which can improve the well-being of workers involved. • The critical safety risks threatening the workers involved in green building construction projects are identified. • The critical safety risks in relation to GB projects are prioritized using fuzzy-based-constrained optimization model. • The prioritized safety risks are effectively evaluated using the concept of risk matrix. • The results produced from the study are validated through focus group discussion approach. [ABSTRACT FROM AUTHOR]

Off-site construction (OSC) is an innovative and sustainable construction method. One of its critical success factors is the proper management of its supply chain (SC). Recently, significant research attention has been focused on the modelling of OSC-SC to improve its performance and sustainability. However, the literature still lacks a comprehensive review of the modelling studies on off-site construction -supply chain management (OSC-SCM). Therefore, this research contributes by providing a comprehensive and up-to-date mapping and clustering of 309 journal articles on the modelling of OSC-SC to identify its trends and gaps, and hence, highlight future research opportunities. To achieve these objectives, a mixed review method, consisting of scientometric and systematic reviews, is used. The scientometric review identifies the most prolific journals, researchers, co-occurrence network of keywords, and their citation bursts. On the other hand, the systematic review classifies the articles based on OSC type and supply chain (SC) stage. At each SC stage, the articles are further classified based on problem settings and solution methods. The results show that production problems have received the most attention (i.e., by 28% of the included articles), followed by on-site construction (19%), design (13%), and logistics (7%) problems. However, researchers are more inclined to address problems at multiple SC stages (33%) due to the interrelationship between these stages. Besides, the systematic analysis shows that OSC-SC problems have been solved frequently by a variety of solution methods such as optimization (25%), simulation (13%) and building information modelling (BIM) (9.5%). However, researchers tend more to integrate multiple solution methods (35%) to address the complexities of OSC-SC problems. Finally, the included studies are classified based on the three sustainability dimensions. The results show that economic, environmental, and social sustainability dimensions have been considered in previous studies by 72%, 24%, and 4%, respectively. Therefore, the study identifies research gaps at each SC stage of each OSC type to incentivize future studies to consider more environmental and social sustainability factors in OSC-SC models. Since modular integrated construction (MiC) has the highest prefabrication level, this study provides future research directions for sustainable supply chain management (SCM) in MiC. The present research is an important reference guide that helps researchers and practitioners to understand different problem settings and their solution methods in OSC-SCM. [Display omitted] • Modeling of off-site construction supply chain management (OSC-SCM) is surveyed. • A scientometric review shows trendy and hot research topics in the modelling of OSC-SCM. • Studies are summarized and classified based on problem settings and solution methods. • Most of OSC-SCM models focused on the economic sustainability. • Future research directions to include other sustainability dimensions are provided. [ABSTRACT FROM AUTHOR]

Sustainability principles should be implemented during all the phases of the decision-making process of constructing residential buildings to achieve maximum gains without compromising the function of such projects. This study identified and examined the critical success factors (CSFs) required for implementing value management (VM) in residential building projects. With a view to the sustainability of such the projects. The CSFs for VM were identified from previous studies, which were then contextually adjusted using both semi-structured interviews and a pilot study via the Exploratory Factor Analysis (EFA) technique. The EFA results showed that the VM CSFs could be categorized into four constructs: stakeholders and knowledge, culture and environment, workshop dynamics, and standardization. Questionnaires were then administered to 214 building stakeholders within the Egyptian building industry. Partial least square structural equation modelling (PLS-SEM) was used to develop the CSFs model. Despite that Egyptian professionals with limited practice experiences were quite knowledgeable about VM, results showed that stakeholders and knowledge were crucial CSFs for implementing VM. The results of this study will be a guide for decision-makers to reduce costs and improve sustainability by introducing VMs in the Egyptian construction industry. • Partial least squares-structural equation models (PLS-SEM) for predicting the impact of critical success factors (CSFs) for value management (VM) were validated. • The VM knowledge and awareness among Egyptian residential building practitioners are in a moderate to a high level. • Established that most Egyptian residential building practitioners did not practice VM in their projects. • Stakeholders and knowledge, culture and environment, workshop dynamics, and standardization identified as CSFs for VM. • The proposed PLS-SEM model priorities the VM CSFs and Offering VM implementation "road map" for sustainable residential building success. [ABSTRACT FROM AUTHOR]

In order to achieve an efficient and a successful operation and maintenance plan for assets, management personnel should have detailed information on the condition of the assets to make informed strategic decisions and properly plan expenditure of capital investments. Condition assessment models for sewage pipelines can be considered as a helpful tool to achieve such objective and from which a decision regarding the required and appropriate intervention can be made. This paper presents a review for the different physical, Artificial Intelligence and statistical models that have been developed to assess the condition of sewage pipelines over a period from 1998 through 2019. The description of different techniques used in building the condition assessment models, and the data required to construct these models are presented. In addition, the major disadvantages and limitations of using these techniques in developing the models have also been discussed. The conducted literature review indicates that various condition assessment models were capable of precisely forecasting the future condition of sewer pipelines. Most of the developed assessment models have been validated with various identified techniques to ensure the adequacy of the predictions. The main problem in model development arises from data availability and liability as several factors were identified by researchers to impact the deterioration of sewer pipelines. In order to overcome this problem, municipalities must utilize the new emerging technologies to facilitate gathering the required dataset in a complete and precise manner. Also, certain techniques such as evidential reasoning or Bayesian Belief Network can be used due to their capabilities in dealing with missing data. Furthermore, the influence of the factors on the pipe condition were identified by some researchers. Although there were discrepancies in the findings, but the majority concluded that both age and material factors have high influence and pipe slope has low influence. • This paper reviews the models for assessing the condition of sewage pipeline. • Sewer pipelines deteriorate by physical, operational and environmental factors. • For assessment, physical, statistical and artificial intelligence models are used. • A combination of assessment models and regular inspection is essential. [ABSTRACT FROM AUTHOR]

• Hybrid SVR techniques are developed to predict the maximum depth of the pitting corrosion for oil and gas pipelines. • GA, PSO and FFA meta-heuristic optimization algorithms are used to determine optimum SVR hyper-parameters. • A comparative study between the developed hybrid models and empirical ones are presented. • The study demonstrates superior performances of the proposed hybrid models in terms of robustness and accuracy. Avoiding failures of corroded steel structures are critical in offshore oil and gas operations. An accurate prediction of maximum depth of pitting corrosion in oil and gas pipelines has significance importance, not only to prevent potential accidents in future but also to reduce the economic charges to both industry and owners. In the present paper, efficient hybrid intelligent model based on the feasibility of Support Vector Regression (SVR) has been developed to predict the maximum depth of pitting corrosion in oil and gas pipelines, whereas the performance of well-known meta-heuristic optimization techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA), are considered to select optimal SVR hyper-parameters. These nature-inspired algorithms are capable of presenting precise optimal predictions and therefore, hybrid models are developed to integrate SVR with GA, PSO, and FFA techniques. The performances of the proposed models are compared with the traditional SVR model where its hyper-parameters are attained through trial and error process on the one hand and empirical models on the other. The developed models have been applied to a large database of maximum pitting corrosion depth. Computational results indicate that hybrid SVR models are efficient tools, which are capable of conducting a more precise prediction of maximum pitting corrosion depth. Moreover, the results revealed that the SVR-FFA model outperformed all other models considered in this study. The developed SVR-FFA model could be adopted to support pipeline operators in the maintenance decision-making process of oil and gas facilities. [ABSTRACT FROM AUTHOR]