Defects can have a significant impact on construction performance. A defect is not usually an outcome of a single cause, but rather occurs when multiple interrelated causes combine, forming a defect’s pathway. Because defects may occur through numerous pathways, the risks of causes can vary in terms of the number (i.e., frequency) of pathways that they take part in and the impact (i.e., magnitude) of the contribution they provide for the formation of these pathways. Several studies have strived to identify generic defect causes; however, research on the analysis of the risk of causes in order to develop further defect management efforts is lacking. To address this deficiency, this study aims to develop a methodology to identify and quantify the risk of defect pathways. Specifically, the study uses a fault-tree approach to construct the taxonomy of the defect causes and uses risk importance measures to assess the identified defect causes in terms of frequency and magnitude. Applying the developed m...

The applicability and effectiveness of case-based reasoning (CBR) have been demonstrated in various construction management areas, including bidding, international market selection, safety hazard identification, construction litigation, and preliminary cost estimation. Despite its strong potential as a decision-supporting tool in construction, only a few studies have applied CBR for estimating the preliminary duration that is necessary for the overall success of a construction project. Based on this recognition, this paper develops a CBR model capable of accurately estimating the construction duration in the preliminary stage. The applicability and effectiveness of the developed model are tested through application to 83 multihousing projects. The test results confirm the strong potential of CBR to provide an accurate estimate of construction duration with limited information available in the preliminary stage. This paper is significant in that it provides practitioners with a reliable decision-su...

A construction defect can cause schedule delay, cost overrun and quality deterioration. In order to minimize these negative impacts of construction defects, this paper aims to analyze the causality of construction defects. Specifically, association rule mining (ARM) is used to quantify the interrelationships between defect causes, and social network analysis (SNA) is utilized to find out the most influential causes triggering generation of construction defects. The suggested approach was applied to 2949 defect instances in finishing work. Through this application, it was confirmed that the proposed approach can systematically identify and quantify causality among defect causes.

The construction industry continues to record a high number of accidents compared to other industries. Furthermore, the ramifications of construction accidents are growing in terms of both economic loss and loss of life with trends toward larger-scale, more complex projects. For this reason, there is an increasing awareness of the importance of safety management in the construction industry, and the need for more effective safety management techniques. This paper introduces a real-time location-based construction labor safety management system that tracks and visualizes workers’ locations in real-time and sends early warnings to endangered workers. The system is developed by integrating: a real-time locating system (RTLS) for tracking of workers’ location; a location monitoring system for mapping the workers location on a computerized building model; and alarm technology for sending early warnings. The developed system has been applied to an apartment project and an RTLS technology test center in Korea, and proved to be effective in tracking and monitoring workers in real-time and preventing construction accidents. It is envisioned that the developed system will enable proactive construction safety management in South Korea and the methodologies developed in this study will be applicable to other contexts with minimal customization.

The construction industry is one of the most hazardous industries in many countries. Many studies have asserted that industrial accidents could be prevented by eliminating their root causes. However, given that accident occurrence processes are considerably complex and often invisible, it is difficult to identify and eliminate the root causes. Based on this recognition, this paper aims to analyze the causality of construction accidents on the basis of direct causes that are classified into unsafe actions (UA) and unsafe conditions (UC). A logistic regression is applied to examine associations between UAs and UCs and their significances in triggering construction accidents. Then, a Delphi method is applied to determine the relationships between direct and root causes of construction accidents. This study contributes to an improved understanding of the complex causal process of construction accidents, which is a necessary stepping-stone to prevent construction accidents. Meanwhile, only one-to-one combinations of UCs and UAs are considered in this paper. Thus, follow-up studies to examine the impact of one-to-many or many-to-many combinations are needed.