Each participant in a building construction project requires a dedicated work space in which to execute their activities. In this environment, inappropriate work-space planning in a construction site causes work-space problems, which results in a loss of productivity, safety hazards, and issues of poor quality. Therefore, the work space should be considered one of the most important resources and constraints to manage at a construction site. However, current construction planning techniques have proven to be insufficient for work-space planning because they do not account for the spatial feature of each activity. To establish a formalized work-space planning process, therefore, this paper categorizes work space by its function and movability and suggests a framework for a work-space planning process that contains five phases, including 4D building information model (BIM) generation, work-space requirement identification, work-space occupation representation, work-space problem identification, and work-space problem resolution. The proposed framework in this paper can improve the accuracy of work-space status representation and work-space problem identification by introducing the work-space occupation concept and the integrated work-space planning process that considers characteristics of activity, work space, and construction plan. In addition, this paper aims to ameliorate the work-space planning process through path analysis and a formalized work-space problem resolution process. To validate the proposed approach, a case project was tested. The result shows the efficiency and effectiveness of the proposed framework on improving the work-space planning process. Based on the result of this study, a project manager will be able to prevent possible work-space problems and their negative effects on project performance by devising a pertinent work-space plan during the preconstruction phase.

As large-scale building projects increase in frequency, their construction costs become a matter of great concern, especially because of their lengthy construction periods. In particular, recent volatile fluctuations of construction material prices have fueled problems like cost forecasting. Many researchers try to accurately estimate cost escalations, but price forecasting for numerous construction materials requires a simplified and automated process. The research in this paper develops an automated time-series material cost forecasting (ATMF) system including both autoselected procedures for determining a best-fitting model and an autoextracting module for forecasting values using the Box-Jenkins approach. If the modeling process is simplified and iterative arbitrary decisions for the modeler eliminated, each future prices of a large number of materials can be forecast differently. Thus, the ATMF system can be utilized for predicting future trends in construction material costs. Further, an out-of-sample forecast applying several material price data confirms that this system can be effectively applied to material cost estimation at a more detailed level in object-based cost planning. The proposed system can thus help decision makers in the construction industry deal with changes in economic conditions and design by estimating cost escalations caused by volatile factors such as inflation.

Because diverse functional losses of the overall built environment (e.g., buildings, industrial facilities, and civil infrastructure) from a disaster event cause tremendous inconveniences to refugees, it is essential to implement swift recovery plans. However, in a postdisaster recovery situation, when resources and time are severely limited, there exist difficulties in implementing appropriate plans. These difficulties are attributable to a lack of comprehensive understanding of the overall recovery processes, caused by associated interdependency among the functions of a facility/infrastructure and the dynamic features of multiple recovery efforts. Thus, this research develops a system dynamics (SD) model to understand overall recovery efforts in the whole region from a holistic perspective. Based on an actual disaster case (the 2011 earthquake of Tohoku), this research conducts a case study to analyze the effectiveness of governmental plans. The simulation results show that governmental plans primarily aimed at recovering the daily lives of populations may not always be helpful for rapid restoration operations. The findings also suggest that considering the associated interdependency among built environment functions in recovery planning is key to supporting not only the rapid functional recovery of the facilities but also the improvement of poor restoration work environments. In this situation, the timely uses of preplanned and government-centered recovery programs (e.g., temporary housing and temporary debris movement) can be more effective with a comprehensive understanding of dynamic features in interdependent and multiple recovery efforts. Further, it is expected that these analytical capabilities have the potential to support recovery planners both in developing effective special recovery programs and in providing improved institutional strategies. Policy implications and lessons learned from a past recovery case can also be helpful in providing specific guidelines for future disaster recovery planning.

In modular construction projects, unit production and onsite work are conducted concurrently, enabling shorter duration, lower cost, and improved quality. Because of the nature of the work, building design details should be determined early in the design phase, which requires information from participants. However, the design process for stick-built construction does not include such information, which leads to errors in design, such as omissions and conflicts of information from participants, causing reworking in the design phase. To reduce errors, an information flow should be identified representing when/what/how the information should be shared, and with whom. This paper proposes an integrated design process based on the information flow. To identify the flow, a precedence relationship between activities is represented using a dependency structure matrix (DSM). Then, the order of activities is rearranged using a partitioning algorithm. In this manner, unnecessary feedback and reverse information flow, which are related to errors, are reduced. Finally, the rearranged activities are proposed as an integrated design process. To validate the impact of the proposed process and methodology, interviews with experts were conducted. The validation results suggest that the project delivery method should also be considered in the early project phase in practical application.