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.

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.

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.

Over the past two decades, the South Korean government has been regulating the supply and prices of multi-family housing (MFH) projects to stabilize the national population. Recently, active research has been conducted on the construction costs for basic type (CCBT) calculation to formulate appropriate policies. However, related previous studies have focused on improving the predictability of the construction cost in early stages based on existing house sale prices. In contrast, the CCBT calculation approach mainly requires policy implementation in practical fields, without considering the requirements of academics. Therefore, it is necessary to academically discuss a different approach for the estimation of sale prices of new MFH in the construction stage. This study aimed to calculate the CCBT to determine the appropriate sale price for new MFH. We selected four sample projects to calculate the CCBT, and a weighted average method was applied to correct regional deviations. Case application, which is a comparison between the CCBT-based sale price and actual case-based sale price, produced cost values in the range of 98–104%, and they included additional expenses. The results of this study demonstrate an extremely high level of cost estimation accuracy according to the Association for the Advancement of Cost Engineering study. Furthermore, this study can facilitate the stabilization of national housing by determining an appropriate sale price and can contribute to cost management research conducted during the construction phase.

Road traffic crashes remain a significant concern for public health and transportation safety, creating an ongoing need to evaluate and improve road safety performance. Road safety performance is often evaluated using fatality-to-exposure rates such as fatalities per population or vehicle miles traveled (VMT); however, the choice of exposure measures can lead to different assessments of relative performance. The selection of appropriate benchmarks is also crucial for translating relative performance into meaningful reference levels for safety target setting. This study proposes an integrated framework for multidimensional road safety benchmarking and benchmark-informed target setting across the U.S. states. The framework was developed to address two complementary questions: how a jurisdiction performs relative to relevant benchmarks and how the resulting benchmark-derived fatality levels can inform future target setting. Using traffic safety data, states were first grouped based on multidimensional fatality-to-exposure profiles incorporating population, licensed drivers, registered vehicles, and VMT. The resulting groups were then used to define progressively broader comparison sets for staged benchmarking. Within each applicable comparison set, data envelopment analysis (DEA) was used to assess the relative performance, identify reference jurisdictions, and derive benchmark-based fatality reference levels, while aggressive cross-efficiency provided additional discrimination among jurisdictions. The analysis showed that the relative road safety performance, the identification of DEA-efficient states, and benchmark-derived fatality reference levels changed as the comparison set progressively expanded. These stage-specific reference levels provide empirical information that can inform target selection rather than prescribed targets or forecasts. The proposed framework connects multidimensional road safety performance assessment with benchmark-informed target setting, providing an integrated basis for evaluating current performance and supporting future safety-management decisions.