When properly implemented, modular construction offers considerable opportunity to improve project performance. However, the construction industry continues to struggle to achieve higher levels of modularization. Since the characteristics and environments in dense urban areas differ to those in rural areas, strategy, pre-project planning, project execution planning, and actual execution of modular construction should also differ. However, research so far has not focused on the implementation of modular methods in urban environments. Thus, the aim of this study was to identify unique challenges and opportunities for implementing modular techniques in urban environments. To meet this goal, this paper examined the current practices, benefits, and barriers in highly dense urban areas by employing a three staged-sequential approach involving the preliminary investigation, main survey (106 responses from 39 companies), and ex-post in-depth interviews with 10 experts. The results show that (1) ‘better site operations’ was selected by practitioners as the greatest benefit, and ‘site access and on-site storage area’ was selected as the key barrier and (2) the practitioners experienced difficulties in transporting modules in urban areas. These findings offer valuable implications to modular technique implementation in urban areas around the world.

Modularization is a well-established method that can improve the efficiency and effectiveness of the construction industry. However, the industry continues to struggle with its implementation, and not all executed modular projects have resulted in successful project performance. This study investigates the combinatorial effects of modularization critical success factors (CSF) on the cost and schedule performances of industrial modular projects and derives a conceptual model of cost and schedule successes using information from actual cases by applying crisp-set qualitative comparative analysis. The findings confirm that CSFs interactively and collectively affect modular industrial project performance. The most commonly involved CSFs for cost and schedule successes are timely design freeze, owner-furnished/long-lead equipment specification, vendor involvement, and management of execution risks. This work contributes to researchers and practitioners in the industrial sector to improve understanding ...

High levels of modularization have yet to be generally achieved by the construction industry. To suggest how such levels might be achieved, this study identified needed changes or adaptations in conventional project work processes. It did so by identifying and analyzing differences in how modular projects should be planned and executed. Nineteen research team members and two academic researchers identified, classified, and grouped 107 differences in how modular projects should be planned and executed. Later, three modularization case studies were conducted, and the findings were subsequently validated by an external validation committee. At the topic level, nearly 37% of the planning differences pertained to one of four topics: planning and cost estimating; modularization scoping, layout process, and plot plan; basic design standards, models, and deliverables; or detailed design deliverables. The industry may need to pay attention to these four topics to achieve higher levels of modularization. T...

Postdisaster reconstruction of transportation infrastructures generally entails complex and multiobjective planning and implementation options under uncertainty because of a large number of underlying subjective and objective factors, including social, economic, political, and technical aspects. With limited federal, state, and local resources, it is also challenging for decision-makers to establish a meticulous plan for postdisaster transportation recovery. However, previous studies mainly dealt with the specific planning or execution part of the postdisaster recovery process and rarely considered a comprehensive set of objectives in their investigations. This paper aims to develop a new prioritization approach for rapid and optimized postdisaster recovery that evaluates recovery priorities of damaged transportation infrastructure systems and affected regions through a multiagent system using a reinforcement learning technique. The proposed model contributes to the body of knowledge by providing a new optimization framework, considering transportation network recovery, and minimizing the social impact of the current prolonged recovery process on affected communities. This new methodology is expected to help public agencies make an informed decision for distributing given resources and structurally arranging disaster recovery processes of transportation systems by simulating real-world high-dimensional disaster scenarios and optimizing their recovery plans. In particular, the proposed approach pursues to assist disaster-relevant practitioners in considering a holistic perspective for comprehensive decision-making, incorporating diverse factors of planning transportation recovery and assigning their resources according to socioeconomic factors of affected communities.