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Ensuring the safety of gas pipelines is crucial for the reliable and efficient transportation of natural gas. This study introduces a methodology for evaluating the time-varying reliability of corroded gas pipelines. The proposed approach employs both importance sampling and copula theory to effectively address the uncertainties associated with random variables involved and provide efficient estimates of failure probability. Small leak and burst failure modes are specifically investigated within this study. The methodology first establishes a step-by-step procedure for analysis. Subsequently, the study investigates the failure probability of corroded gas pipelines, taking into account both small leak and burst failures, under scenarios involving single and multiple defects. Furthermore, the study examines the influence of correlation strength and dependence structure among the involved random variables using the copula theory. Additionally, the generation of newly formed defects is thoroughly investigated, along with its impact on failure probability. Numerical examples are provided to evaluate the performance of the proposed methodology, comparing it with the benchmark failure probability estimated through Monte Carlo simulation. The results validate the precision and efficiency of the proposed methodology, while offering practical and insightful suggestions for reliability analysis of real-world gas pipelines. • Copula-based method improves time-varying reliability analysis for pipelines. • Importance sampling aligns with MCS and enhances analysis efficiency. • Correlations influence the probabilities of pipeline failures at specific pipe ages. • Field inspection data is essential to conduct a comprehensive reliability analysis. [ABSTRACT FROM AUTHOR]

• 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]

Effective functioning of water systems is critical to ensure the quality of human life. Therefore, failure prediction of water mains under climatic variations is necessary to avoid socio-economic and environmental losses. This paper aims to propose a hybrid model named STL-GC-LSTM for an accurate failure prediction of water mains under the impact of climatic variations. Firstly, the seasonal-trend decomposition based on Loess (STL) method is employed to decompose the failure time series. Next, significant climate variables are selected from the Granger causality (GC) test. Lastly, the final predicted failure of water mains is acquired by adding up the predictive results of the three components which are learned by Long Short-Term Memory (LSTM) models. Several evaluation metrics are used to assess the prediction performance. The results from a case study in Hong Kong imply that STL decomposition is promising for fully mining intrinsic properties of failure series. The developed hybrid models are effective in specifically identifying which component climatic variations exert influence on, and the final failure predictions show satisfactory agreement compared with peer models. This paper could provide an accurate estimation for failures of water mains ahead of time and be used as an essential complement to other numerical prediction models. [ABSTRACT FROM AUTHOR]

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry. [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]

Circular economy (CE) is an emerging concept in the construction industry that focuses on maintaining materials in a continuous cycle to maximize their value. Although previous studies have explored CE in different contexts, there is a lack of studies investigating the relationship between CE barriers and drivers in developing countries. To address this gap, this study aims to identify a list of barriers and drivers of CE adoption and investigate their relationship. The literature review classifies 21 barriers into four constructs, including market policy and technology-related, legal and institutional, supply chain-related, and product design and waste management barriers. Through a questionnaire-based survey with respondents mainly from the Ghanaian construction industry, the research uncovers the most critical barriers within each construct and identifies 13 out of 16 drivers to be critical for CE implementation. The relationship between CE barriers and drivers is found to be significant and substantial, as demonstrated by the path coefficient (β = 0.723) and the p-value (< 0.05). This study's outcomes offer both theoretical and practical implications for academic and industry practitioners, empowering them to craft evidence-based strategies that facilitate successful CE adoption in developing nations. • Comprehensive understandings of the critical barriers and drivers are outlined. • Relationships between barriers and drivers are found to be significant. • Insights may inform data-driven strategies for CE adoption. • Focus on GCI study reveals CE adoption awareness in emerging economies. [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]

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]

• 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]

• The DT Implementation barriers are categorized under six categories. • The PLS-SEM technique was developed to prioritize DT implementation barriers. • There is a strong relationship between overcoming DT implementation barriers and OSS. • Overcoming DT implementation barriers can enhance construction sustainability by 23 %. A Digital Twin (DT) is a digital copy of a real-world object or process. Although DT has gained traction in construction, its relationship with sustainable success remains insufficiently studied. This research addresses this gap by investigating barriers to implementing DT in sustainable construction. The study employs a hybrid approach involving literature review, expert interviews, and modeling techniques, with data collected from 108 construction experts based on a number of criteria, including the experience, degree, and familiarity of the experts about the Hong Kong building and construction sector Hong Kong. The findings reveal 45 barriers categorized into six clusters, including notable obstacles such as "legacy systems," "data uncertainties," and "connectivity." The key clusters identified are "performance" and "security," while the "social" aspect of sustainable success is least supported. Recognizing these challenges assists decision-makers in navigating obstacles and utilizing DT for environmentally conscious construction, streamlined processes, and positive societal impacts. Future research could delve into integrating sustainability throughout the project lifecycle using technology adoption theories. [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]

Highlights • Sustainability is one of the primary concern the modern building industry, especially the existing buildings, to overcome its environmental impacts. • Each rating system has its assessment attributes, evaluation models, and ranking scale. • The key aspect of developing a global or generic rating tool is identifying the sustainability assessment attributes that have the significant effect on the sustainability of existing buildings. • Introducing multilevel weight-based assessment model helps in reflecting the local variations in the sustainability appraisal process. • Integration of BIM and energy simulation modeling are utilized in the proposed sustainability assessment framework. • Explicit calculation procedures and easiness of use are crucial aspects in the overall sustainability appraisal process. Abstract Under climate change impacts, the world is becoming one village. This motivated the application of sustainability rating systems of buildings away from their origins which is hindered by the different attributes and weights. Hence, this study developed a global sustainability rating tool for existing buildings, considering the regional variations through proposing sustainability assessment attributes and determining their weights utilizing fuzzy logic. Data was collected through Canadian and Egyptian experts' questionnaires to stand for the impact of the regional variations on the weight values. Fuzzy topsis was implemented to overcome the uncertainties inherent when considering opinions of individuals. Consequently, the assessment model and the ranking scheme were developed. In addition to questionnaires, the model was implemented using BIM modeling and energy simulations for two cases in the Canadian and Egyptian environments. The results showed that weights vary from country to another, however, energy criteria deemed to have nearly the same weight in both countries. Also, the results showed the ability of the proposed model to address the regional variations through the developed multi-level weight model. The model was validated by applying sensitivity analysis. The developed sustainability assessment model is a step towards a globally working sustainability rating tool that can address regional variations. [ABSTRACT FROM AUTHOR]

Regular inspection, assessment, and rehabilitation are necessary to maintain the performance levels of sewer pipelines. The primary objective of this research is to develop a causality-based model by incorporating twenty-two sewer defects, including erosion voids. The Decision Making Trial Evaluation Laboratory (DEMATEL) was used to study the causality relationship between the defects, based on a designed questionnaire, to conclude the influencing and influenced distress. The results suggested that 59% of the sewer structural and operational defects were influencing ones. Besides, the existing state of the sewer pipelines was evaluated on a scale from 1 (Excellent) to 5 (Critical) by integrating the DEMATEL technique with the Quality Function Deployment (QFD) method. The QFD-DEMATEL model was implemented on a case study to compare the model’s classifications of the pipelines’ states with the actual ones. Based on the comparison, the average accuracy and true negative rate (TNR) of the classifications were 74% and 79% respectively. Also, the average invalidity percentage (AIP) was 41.32%, causing the validity percentage to exceed 50%. The integrated approach keeps decision-makers informed about their sewer pipelines to prioritize pipelines rehabilitation. Thus, defective sewers are reduced and so are the exfiltration cases. [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]