Total volumes of construction orders and the domestic construction markets are predicted to continue shrinking. To compensate, Korean construction companies have been trying to make inroads into the global construction market. Companies must determine their competitiveness and establish strategies to expand into overseas construction markets. The General Electric/McKinsey Matrix, introduced in the early Seventies, is a specific framework for evaluating investment opportunities and is still widely used to analyze competitiveness scenarios. In this study, we analyze the construction industry in 22 countries. We describe the procedure for creating the matrix and present each country with details of various construction environments. The results are relative to the two typical matrix dimensions: the competitiveness of a construction business and the attractiveness of the construction industry. This model can be used to conduct a comprehensive analysis of global construction markets and policies, thereby providing policy-making recommendations to countries or companies. This study fills a gap and describes the state of the industry, enriches the literature on the subject, and creates new points of inquiry. The results, although based on a limited selection of countries, provide an accurate demonstration of the methodology and highlight the ranking of countries.

Air pollution caused by the construction industry in the form of particulate matter (PM) has increased to an alarming level. The effects on the health of construction workers are found to be hazardous despite the current advancement in construction methods and practices. In particular, the efficiency of existing control measures for reducing PM from various construction activities has not been improved to the desired level. This study investigated the factors that influence the efficiency of a sprinkler system-based control measure when water spraying and dust suppressant solutions are used. The real-time PM exposure was measured during hollow-block cutting activity using Alphanese OPC-N3 sensors in dust chamber. The dust suppressant suppresses dust particles by initially forming a solidified film on the particle surface, and the high cohesion of this film enhances the suppression rate by promoting dust particle coagulation. It was observed that when using a dust suppressant, the PM concentration at 100 bar exceeded concentrations at other pressures, resulting in increased efficacy in reducing PM₁₀. Additionally, water spraying at 115 bar was determined to be the optimal control measure for achieving a significant percentage of PM reduction in a shorter period. These findings can be highly beneficial if the water sprinkler system can be modified into a smart mobility-based sprinkler system either ground-based or drone-based at construction sites in improving PM reduction efficiency particularly on high PM emitting activities.

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.