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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]

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]