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Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation. [ABSTRACT FROM AUTHOR]

There are two crucial resources (i.e., labor and equipment) of productivity in the construction industry. Productivity modeling of these resources would aid stakeholders in project management and improve construction scheduling and monitoring. Hence, this research aims to review machine learning (ML) applications in the process of construction productivity modeling (CPM) for construction labor productivity (CLP) and construction equipment productivity (CEP) from dataset acquisition to data analysis and evaluation, which includes their trends and applicability. An extensive analysis of 131 journals focused on the application of machine learning in construction productivity (ML-CP) from 1990 to 2024 via a mixed review methodology (bibliometric analysis and systematic review) was conducted. It can be concluded that despite the rise in automated dataset collection, the traditional method has its advantages. The review further found that the selection of ML models relies on each particular application, available data, and computational resources. Noticeably, artificial neural networks, convolutional neural networks, support vector machines, and even deep learning demonstrating have been adopted due to their effectiveness in different functionalities and processes in CPM. This study will supplement the insights gained in the review with a comprehensive understanding of how ML applications operate at each stage of CPM, enabling researchers to make future improvements. [ABSTRACT FROM AUTHOR]

Annually, over 10 billion tons of construction and demolition waste is transported globally from sites to reception facilities. Optimal and effective planning of waste transportation holds the potential to mitigate cost and carbon emissions, and alleviate road congestion. A major challenge for developing an effective transportation plan is the uncertainty of the precise volume of waste at each site during the planning stage. However, the existing studies have assumed known demand in planning models but the assumption does not reflect real-world volatility. Taking advantage of the problem structure, this study adopts the stochastic programming methodology to approach the construction waste planning problem. An integer programming model is developed that adeptly addresses the uncertainty of the amount of waste in an elegant manner. The proposed stochastic programming model can efficiently handle practical scale problems. Our numerical experiments amass a comprehensive dataset comprising nearly 4300 records of the actual amount of construction waste generated in Hong Kong. The results demonstrate that incorporating demand uncertainty can reduce the transportation cost by 1% correlating with an increase in profit of 14% compared to those that do not consider the demand uncertainty. [ABSTRACT FROM AUTHOR]

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development. [ABSTRACT FROM AUTHOR]