In recent years, excavation work of high-rise buildings has been becoming increasingly complex in the congested urban areas and frequently exposed to substantial hazards, impediment to safety as well as financial loses. For these reasons, advanced sensor-based systems have been used to accurately monitor and diagnose the failure risk of an excavation work; however, it is often tricky to anticipate and assess the impacts of changeable variables, such as human factors, site conditions, material loadings, and mobile equipment, because of the lack of proper tools to explain the unforeseen phenomena. This study proposed a decision support model to concretize effectively experts’ and practitioners’ subjectivities and to quantify the failure risk. The model is fundamentally constructed on fuzzy analytic hierarchy process, which weights the environmental influences that can derive a failure. The outcomes are used as an input for fuzzy comprehensive operations to compute the quantitative failure risk. Three illustrative cases have been examined to explain the capabilities of the proposed model. The results have shown the possibility that the model can be helpful for developing safety precautions with a warning signal during the planning and controlling stages of a new or ongoing excavation work.

Over the past decade, Nuclear Power Plant (NPP) projects have been increasingly forced to cope with potential risks, which seriously impacted achieving the target completion time and cost. NPP projects have frequently exceeded the planned time and cost despite reliable estimates at the start of the project. This was attributed to unpredictable causes at the beginning and to shortage of proper tools to quantitatively analyze the inevitable uncertainties. To overcome these difficulties, computerized systems have been widely proposed in the construction industry as effective aids to proactively control the potential risks and to monitor continuously their status and variation. However, there are few risk management systems focused on NPP projects. In this study, we developed a computer-based risk management system for NPP projects, called Nuclear Risk Management System (NuRMS), which is helpful for furnishing project managers with warning signals as a project is progressing and for tracking the changes in risk variation at a desired confidence level. In developing the NuRMS, the Risk Breakdown Structure (RBS), the weights for risk categorization, computation of the Total Risk Score (TRS), and integration of risk analysis tools with relation-based and cost-loaded activity schedule were core tasks. To demonstrate the efficiency of the developed NuRMS, the system was operated using risk information derived from constructing the auxiliary buildings of Shin-Kori units 3 and 4 located in Korea, as an illustrative case. The results showed the system’s capacities and suggested that it may be applied to a real NPP engineering, procurement, and construction project. The developed NuRMS is expected to assist a project manager in monitoring, tracking, and controlling potential impacts of risk events during the overall implementation of NPP projects.

Several workplace injuries and fatalities are annually reported in the construction industry, and various efforts have been made to ensure safety. However, the construction industry still has higher casualty rates than other industries, and a preemptive safety management system that considers the safety risk factors at the construction site is deemed a necessity. Therefore, this study proposed a model that can quantitatively evaluate the safety of construction workers. A total of 18 safety risk factors consisting of three groups was derived from disaster-case data through a literature review, and was consulted to experts based on a survey to verify the suitability. Based on the results from the survey, 13 safety risk factors were selected, and the weights for each factor were derived. Moreover, an Analytic Network Process was used to derive the weights. Afterwards, a safety risk scale for each factor was derived based on the utility function from the accident case data provided by Korea Occupational Safety and Health Agency and opinions provided by expert. Finally, a safety assessment index for construction workers was proposed by combining the derived weights and safety risk scales. Accordingly, this study facilitates identifying construction workers with a high probability of suffering accidents in advance. Therefore, this study can contribute to preventing accidents at the construction site by supporting customized safety management for workers in hazardous environments.

The construction industry continues to face declining productivity due to its heavy reliance on labor and the repetitive, non-value-adding nature of supervision tasks. This study provides an exploratory, practitioner-based evaluation of how selected digital technologies, PDF-based documentation systems, object recognition algorithms, and 3D vision technology may contribute to productivity improvements in construction supervision. A total of 82 valid responses from field engineers were collected to examine perceived task substitution effects across major construction work types and management functions. The findings indicate that higher work-adoption rates of digital technologies are generally associated with improved supervisory productivity, with the strongest perceived benefits observed for PDF-based documentation in reinforced concrete and formwork tasks. However, other expected relationships, particularly those involving work responsibility, did not appear consistently in the practitioner data, suggesting that such perceptions may be influenced by task context and adaptation burden. This study offers a practical and context-specific framework for understanding how digital tools may support productivity enhancement in supervision work. While the results reflect tendencies based on a limited sample, they provide field-grounded insights that can inform the phased and targeted application of digital technologies in construction supervision and guide future empirical model development.

Construction supervision is a key task that can confirm and inspect the project execution and support the creation of successful results. For the efficient performance of construction supervision, collecting various information necessary for the supervisor’s decision-making is a prerequisite, and the use of digital technology has recently been proposed as an alternative. Accordingly, the purpose of this study is to present a roadmap for the step-by-step use of digital technology to improve the efficiency of construction supervision. This study was limited to the supervision of construction projects, which have a high proportion of contract volumes in the Korean construction industry and can directly affect people’s lives. This study reclassified Korea’s construction supervision works, collected data on the urgency and effectiveness of detailed works from various stakeholders, and derived a final roadmap using importance–performance analysis. The results of this study are expected to serve as basic guidelines that can be used when reviewing digital technologies that can be applied step by step to improve the efficiency of construction supervision work.