Electrical contractors encounter problems such as limited construction sites, schedule interference, and inefficient communication with other contractors when they typically subcontract with general contractors. Electrical projects require effective and systematic project management strategies to overcome these problems and achieve the desired goal. In an electrical construction project, individual tasks are interconnected at different stages, including pre-construction planning (PCP) and project execution (PE). Therefore, analyzing the effect of task strings on the project success in terms of schedule and cost performance is necessary. The main objective of this study is to perform a static analysis to compare successful and failed projects with a focus on the cost and schedule performances, using the PCP and PE task strings in electrical construction projects. To achieve this, a continuous PCP-PE task strings implementation score was calculated for each PE group in terms of cost and schedule, and successful and failed projects on unweighted and weighted values were compared and analyzed by performing an independent sample t-test. Consequently, it was confirmed that the use of most task strings had a positive effect on the cost success at a confidence level of 95%, and that only the subcontractor management group had a positive effect on the schedule success. Hence, it was derived that the usage of task strings for these groups is recommended for cost success in electrical construction, and continuous PCP-PE task strings do not have a positive effect on schedule success; therefore, it is recommended to use the PCP-PE task strings only for specific groups for schedule success in electrical construction. Demonstrating the relationship between the PCP and PE tasks, the findings of this study are expected to help electrical contractors achieve a better performance using effective project management strategies.

Construction safety climates can reflect organizational safety behavior and commitment, employees’ safety perceptions and attitudes, and the supervisory and support environments. Maintaining a healthy safety climate can help prevent workers from fatal accidents and illnesses. To enhance the safety climate and, consequently, improve safety performance at a construction site, it is very important to analyze the elements that affect the safety climate and are significant for different types of construction work organizations. Therefore, the main goal of this study was to develop a multi-spectra perception model to investigate which factors were considered critical from four key perspectives: managers, superintendents, skilled laborers, and general helpers. To achieve this goal, a survey questionnaire was conducted to collect empirical data from one commercial building construction project. Based on a stepwise regression analysis, it was revealed that the most significant factors enhancing the safety climate are: from the managers’ perspective, a combination of improvement in the support environment and reduction in work pressure; for superintendents and skilled laborers, increasing worker competence; and for general laborers, increasing worker involvement. This research contributes to a better understanding of the significant factors and provides a measure for each important role in enhancing the safety climate at a job site.

In South Korea, industrial accidents continue to increase in frequency, with construction accidents accounting for more than a third of all industrial accidents. Specifically, by preventing fall accidents, the death rate from accidents can be reduced by 50%. Fall protection is required to prevent fall accidents, and investigating the reinforcement of the worker’s safety harness and hook fastening becomes imperative. This requires automation of computer vision confirmation of the safety harness and hook fastening. As the accident risk can be reduced by an effective safety culture in the system, it is necessary to monitor safety on site through a construction safety automation system. Therefore, the objective of this study is to develop a computer vision process for safety harness and hook for preventing fall accidents in South Korea’s construction industry. This study focuses on system scaffolds that are widely used at construction sites. The application of this methodology to sample sites and field cases established its applicability. The proposed computer vision application methodology will serve as the foundation for visualization research in the construction industry, and image recognition will help reduce the safety accident rate. Accidents caused by failure to use a safety harness and hook can be reduced in South Korea as well as globally. Additionally, this methodology is applicable to roof construction, tower crane installation and dismantling work, as well as steel tower installation and dismantling.

In recent years, the increasing gap between available funding and preservation needs has influenced district pavement engineers to select and prioritize projects to effectively use funding. However, currently, projects are often selected after an informal assessment, based on local conditions and local district engineers’ experience, in the absence of a statewide systematic process. The primary objective of this study is to determine network-level project sections for effective sustainable pavement management using logistic regression analysis. A large volume of inventory data, documented using pavement-management information systems (PMIS), was used to develop the logistic regression (LR) model for selecting candidate sections. The LR model was subsequently validated using a single 50/50 split sample method. The findings of this study will assist the Austin, Texas, USA district to select and evaluate candidate projects. Furthermore, the study will eventually contribute to improved efficiency in project selection and prioritization by reducing not only the amount of time necessary to review the district PMIS data to identify project candidates, but also the potential for human error.

This study identifies the risk indicators of building damage from typhoons and determines the correlations among this damage, typhoon information, geographic vulnerability, construction environment, and socioeconomic vulnerability. This fundamental research aids the development of a typhoon loss prediction model for building construction projects in South Korea. Extreme weather events have become increasingly prevalent around the world, with subsequent increases in related damages. Early attempts to meet the growing demands for a loss prediction model have been insufficiently comprehensive, and specifically in South Korea, research on risk indicators is needed that considers the geographic, building, and socioeconomic features. This research used the regional typhoon loss records from the annual report of the Ministry of Public Safety and Security (MPSS) to define the dependent variable of building damage. The results and findings of this study will inform the development of a typhoon loss prediction model in South Korea.

Since the enactment of the Occupational Safety and Health Act in 1981, the Korea Occupational Safety and Health Agency has endeavored to prevent fall accidents in the construction industry. However, many fatalities still occur in the South Korean construction industry. Meanwhile, the United States improved various systems and conducted studies to prevent fall accidents, significantly reducing such occurrences in the construction industry. The objective of this study is to present improvements to South Korea’s fall prevention technology by analyzing the laws and programs of the United States. To achieve this, this study has analyzed the United States’ fall prevention technology and derived improvements applicable in South Korea through an expert opinion survey. This study suggests to (1) set the height standard of a fall accident to 2 m, (2) adopt an active fall prevention system, (3) create a map of fallen fatalities, and (4) employ safety experts to support foreign workers. In the future, the results of this study are expected to be used as basic data for policies and programs related to fall accidents in the construction industry.