Building and infrastructure construction projects can be viewed as a complex system consisting of many subsystems. Over the last two decades, considerable researches that use system dynamics (SD) as an analytical and modeling approach exist to address construction project management issues. However, only few critical reviews have been conducted to provide an in-depth understanding of SD application in construction project management. Moreover, many studies have failed to apply SD accurately. Therefore, the present study aims to gain an understanding of the current state of play and future directions in applying SD method in construction project management research, by undertaking a comprehensive review of 105 relevant articles published from 1994 to 2018. These articles are analyzed in terms of annual publication rate, key papers and their contribution, critical issues in SD application, and research topics. A significant increase in the number of publications in the last five years has been observed. When applying SD method to model construction system, the following aspects must be carefully considered: Model boundary, model development, model test, and model simulation. In addition, SD has been applied in a wide range of research topics, including (1) sustainable construction; (2) design error, rework, and change management; (3) risk management; (4) resource management; (5) decision making; (6) hybrid modeling; (7) safety management; (8) PPP project; and (9) organization performance. Based on the review findings, this study discusses three future research directions, namely, integration of SD with other methods, uncertainty analysis, and human factor analysis. This study can help researchers gain an in-depth understanding of the critical issues in the application of SD in construction management and the state-of-the-art of SD research.

Construction is considered among the most dangerous industries and is responsible for a large portion of total worker fatalities. A construction worker has a probability of 1-in-200 of dying on the job during a 45-year career, mainly due to fires, falls, and being struck by or caught between objects. Hence, employers must ensure their workers wear personal protective equipment (PPE), in particular hardhats, if they are at risk of falling, being struck by falling objects, hitting their heads on static objects, or coming in proximity to electrical hazards. However, monitoring the presence and proper use of hardhats becomes inefficient when safety officers must survey large areas and a considerable number of workers. Using images captured from indoor jobsites, this paper evaluates existing computer vision techniques, namely object detection and color-based segmentation tools, used to rapidly detect if workers are wearing hardhats. Experiments are conducted and the results highlight the potential of cascade classifiers, in particular, to accurately, precisely, and rapidly detect hardhats under different scenarios and for repetitive runs, and the potential of color-based segmentation to eliminate false detections.