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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]

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]

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]

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]

• 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]