Modular construction methods, where products are manufactured beforehand in a factory and then transported to the site for installation, are becoming increasingly popular for construction projects in many countries as this method facilitates the use of the advanced technologies that support sustainability in building projects. This approach requires dual factory–site process management to be carefully coordinated and the factory module manufacturing process must therefore be managed in a detailed and quantitative manner. However, currently, the limited algorithms available to support this process are based on mathematical methodologies that do not consider the complex mix of equipment, factories, personnel, and materials involved. This paper presents three new building information modeling-based 4D simulation frameworks to manage the three elements—process, quantity, and quality—that determine the productivity of factory module manufacturing. These frameworks leverage the advantages of 4D simulation and provide more precise information than existing conventional documents. By utilizing a 4D model that facilitates the visualization of a wide range of data variables, manufacturers can plan the module manufacturing process in detail and fully understand the material, equipment, and workflow needed to accomplish the manufacturing tasks. Managers can also access information about material quantities for each process and use this information for earned value management, warehousing/storage, fabrication, and assembly planning. By having a 4D view that connects 2D drawing models, manufacturing errors and rework can be minimized and problems such as construction delays, quality lapses, and cost overruns vastly reduced.

The U.S. Green Building Council’s (USGBC) Leadership in Energy and Environmental Design (LEED) certification program supports sustainable construction as part of the effort to address climate change and resource depletion. It is the world’s most popular green building certification system, with more than 146,400 projects. Satisfying the LEED requirements brings many benefits to a project’s design performance and adds community value, but it does incur additional costs and challenges. This study examined the choices made by those working on the 222 LEED New Construction version 4 (LEED-NC-V4) projects that were certified between September 2014 and March 2020 to determine how the LEED project teams selected appropriate LEED goals. The results reveal interesting insights into the way project LEED goals and the credits corresponding to the target certification level were chosen, as well as the links and trade-offs between the various credit options. Based on these findings, useful suggestions are made for ways to help LEED project teams achieve their target certification levels and encourage authorities to continue to improve their local green regulations. The analysis of actual certified projects’ data makes it possible to re-evaluate the effects of newly updated requests in LEED v4 in the light of the stated goals of the USGBC.

Established by the U.S. Green Building Council (USGBC), the Leadership in Energy and Environmental Design (LEED) became a sustainable leader of green building rating systems in American and many other countries. In Vietnam, LEED is expected as a potential solution in improving the sustainable quality of buildings for residents and solving the housing/infrastructure demand with a limit in resource consumption and minimizing negative environmental impacts. The study analyzed the awarded LEED 2009 credits by investigating the data of 36 of the total 42 LEED BC+D 2009 certified projects in Vietnam. The results of the investigation indicated the awarded credits were significantly implemented in Vietnam. These results were converted based on the summary updated on LEED version 4 of the USGBC report, to become a useful guideline for green building cost-efficiency strategies. Additionally, it also served as reference data for the Vietnamese public agency to update their green regulations based on the specific characteristics of Vietnam.

Of all the machinery and equipment used on construction sites, excavators are responsible for the greatest number of fatal accidents. Excavation is an inherently risky process due to imprecise work processes and the unstable external environment on most work sites. The resulting accidents cause serious injuries that threaten the lives of not only the excavator pilots but also those working around them. In this study, we propose a mechanical device that is capable of operating the excavator remotely from a nearby safe location such as a site office. To ensure worker safety and at the same time boost the productivity of excavation operations, data from 3D scanners, cameras, and sensors were combined to create a detailed 3D picture of the area surrounding the excavation site to guide a remotely operated excavating system. Rather than modifying the excavator itself, a removable manipulator was mounted on the joystick in the excavator’s cockpit. Our proposed system was tested on a standard commercial excavator to verify its performance for a real-world excavation task.

Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

To realize sustainable construction, planning for future maintenance costs is essential. In the case of multi-family housing, various maintenance issues can be expected to appear starting 10 years after completion. Therefore, preventive maintenance must be implemented in a systematic manner to cope with the problems caused by the natural aging of multi-family dwellings and to maintain a sustainable level of quality for the properties. In this study, maintenance costs were investigated for 224 multi-family housing units aged 20 years or older in Seoul, South Korea. Using Monte Carlo simulation in conjunction with expert interviews, a probabilistic maintenance cost analysis was conducted to analyze and estimate the variability in maintenance costs. The findings of the study propose that the use of probabilistic maintenance cost analysis can be developed into a useful planning tool for determining reasonable future maintenance costs in sustainable construction.