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.
This study aims to identify the defect generation rules between defects, to support effective defect prevention at construction sites. Numerous studies have been performed to identify the relations between defect causes, to prevent defects in construction projects. However, identifying the inter-causal pattern does not yet guarantee an ultimate grasp of what constitutes proper defect mitigation strategies, unless the underlying defect-to-defect generation rules are thoroughly understood too. Specifically, if a defect generated in a work process is ignored without taking necessary corrective action, then additional defects could be generated in its following works as well. Thus, to minimize defect generation, this study analyzes the defects in the sequence of a construction work. To achieve this, the authors collected 9054 defect data, and association rule mining is used to analyze the rules between the defects. Consequently, 216 rules are identified, and 152 rules are classified into 3 categories along with 4 experts (71 expected rules, 22 unexpected but explainable rules, and 59 unexpected and unexplainable rules). The generation rules between the defects identified in this study are expected to be used to regularize various defect types to determine those that require priority management.
Abstract:Program management is the structured and strategic process of managing multiple projects at a high level to maximize benefits. The essentials of programs include high costs and long implementation periods, and thus, the negative impacts caused by the failure of program management are more significant and greater than that of a project. Therefore, to achieve high program performance, it is essential for program management to be well defined during the early stages. However, the existing research is mainly focused on the performance prediction methodologies for projects, while the research pertaining to programs has concentrated on identifying the qualitative critical success factors (CSFs). Thus, this study developed a methodology for predicting the program performance. Forty-five CSFs were identified herein from literature review and expert interviews, then grouped through factor analysis. In addition, the Program Definition Rating Index (PgDRI) was developed by calculating the weights of the proposed CSFs through structured equation modeling in order to evaluate the quantitative program performance. For validation, the PgDRI was applied to three in-progress cases, and the PgDRI scores were compared with the actual performance of each case. The PgDRI developed in this study can contribute to the body of knowledge pertaining to program management by quantifying the performance management of a program. In addition, the PgDRI can be utilized in the performance management of a program in terms of the cost and schedule by allowing practitioners to apply the PgDRI repeatedly to the major decision-making processes during the early stages of a program.
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