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Korean Journal of Construction Engineering and Management

Korean Journal of Construction Engineering and Management

ISO Journal Title : Korean J. Constr. Eng. Manag.
Open Access Journal Bimonthly
  • ISSN (Print) : 2005-6095
  • ISSN (Online) : 2465-9703
Online Submission

Current Issue : Vol.26 No.1, 2025-01

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The Effects of Business Environments on the Size of PPP (Public-Private Partnership) Investment in Middle-Income Countries

Jung, Suksoo ; Park, Hyeon ; Seo, Junwon

https://dx.doi.org/10.6106/KJCEM.2025.26.1.003

This study aims to identify the factors of business environment in middle-income countries that influence the investment size in Public-Private Partnerships (PPPs), with the objective of drawing implications to mitigate risks for Korean companies participating in overseas PPP projects. To achieve this, correlation and regression analyses were conducted on the investment amounts of PPP projects in 53 middle-income countries from 2017 to 2019, using variables published by the World Bank and The Heritage Foundation. The analysis reveals that PPP investment is influenced by GDP, government debt-to-GDP ratio, protection of property rights, and PPP contract management. These findings support the argument that countries should protect property rights and establish robust PPP frameworks to attract private investment in infrastructure development. Additionally, the analysis highlights GDP, rather than the GDP per capita, and government debt ratio as significant factors in promoting PPPs. This suggests that the ideal target countries for Korean companies to enter into PPP projects are those with relatively large economies and substantial investment needs to bridge the infrastructure gap through PPP initiatives.

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A Method to Quantify the Information Flow during BIM-enabled Design Detailing

Jang, Suhyung ; Lee Sungkyu ; Lee, Hyunjun ; Roh, Hyunsung ; Lee, Ghang

https://dx.doi.org/10.6106/KJCEM.2025.26.1.012

This paper introduces a novel method for quantifying changes in Building Information Modeling (BIM) object data during BIM-enabled design detailing process. A significant advantage of BIM is its ability to improve information flow, a crucial element in managing the design process. However, despite the importance of this capability, existing research lacks robust quantitative methods for monitoring the detailed stages of BIM authoring. To bridge this gap, we propose a systematic approach that involves three key steps: 1) defining the types of data changes, 2) developing a classifier to match and track BIM objects across consecutive models, and 3) applying a weighted Level of Development (LOD) framework to quantify the extent and significance of these changes. The proposed method was applied in a case study analyzing BIM models from the Schematic Design (SD), Design Development (DD), and Construction Documentation (CD) phases of a project. The results demonstrate that our approach effectively reflects and quantifies the progression of design changes between these phases. Moreover, the findings highlight the potential of this method to serve as a decision support tool, enhancing the management of the design process by providing detailed insights into the evolution and development of design details.

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Analysis on Correlation between Building Permit Area, Construction Commencement Area and Construction Orders on South Korea

Kim, Dong-Min ; Lee, Jin-Hyup ; Kim, Kyung-Hwan

https://dx.doi.org/10.6106/KJCEM.2025.26.1.022

The purpose of this study is to analyze the correlation among building permit area, construction commencement area, and construction orders using a VAR model. Additionally, it aims to forecast future values of construction orders. Despite recent positive indicators in construction orders, the actual growth rate may be moderated by escalating construction costs. Additionally, a notable disparity exists between building permit and commencement area, indicating divergent trends. This incongruence may be attributed to the overall surge in construction costs, potentially resulting in building permits not translating into actual commencements. The study suggests that this incongruence could significantly contribute to a declining construction industry. The results indicate a mutual feedback relationship among the three variables, suggesting that it is not a one-way correlation.

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Study on the Development and Comparison of ANN and MLR Models for Construction Budget Estimation - Focusing on the Construction Budget for Public Building Projects -

Park, Jee Young ; Kim, Han Soo

https://dx.doi.org/10.6106/KJCEM.2025.26.1.031

The construction budget is crucial for determining a project's feasibility, making systematic budget prediction essential for project owners. To ensure compliance with the initial budget during the design phase, it is necessary to manage the budget with a breakdown by work type. Traditional methods rely on estimators' experience and judgment using historical data, which can introduce uncertainty. The objective of the study is to develop and compare budget prediction models using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) techniques, focusing on public office building construction costs. The study evaluates the effectiveness of these models for budget prediction and identifies key characteristics. The results show that the models using ANN and MLR have error ranges of -36.3% to 58.0% and -27.7% to 32.8%, respectively, demonstrating their utility. Additionally, the MLR model exhibited more stable predictive performance for homogeneous data compared to the ANN model.

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Development and Optimization of Geospatial API-Based AR System for Field Application of Markless AR Technology

Jeong, Yujeong ; Suk, Chaehyun ; Jeon, Haein ; Yu, Youngsu ; Koo, Bonsang

https://dx.doi.org/10.6106/KJCEM.2025.26.1.045

To utilize BIM-based AR technology within actual construction sites, ensuring projection stability and alignment between physical structures and AR models is a critical technical element. However, most of the previous researches for applying AR technology in construction site management has been utilized marker-based projection methods. These method faces limitations due to complex site conditions that unsuitable for marker attachment and the time-consuming process of marker placement which hindering practical implementation. Hence, this study proposed the use of a markerless projection methodology based on Geospatial API, aiming to optimize projection performance. For this purpose, an AR system for site management was developed, incorporating and enhancing the Geospatial API as the projection technology. The projection technology was tested on an actual railway bridge structure to assess its convenience, stability and alignment. Despite encountering projection performance degradation, the issue was resolved by deriving optimal conditions for image data registration required by the Geospatial API. Consequently, projection stability and alignment were achievable enhancing overall projection performance. Therefore, utilizing a markerless projection approach could potentially improve the applicability of AR technology in congested construction sites compared to marker-based methods.

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Importance of Safety and Health Management System for Small and Medium-sized Construction Companies Based on AHP Analysis

Woo, Yun-Hee ; Yoo, MooYoung

https://dx.doi.org/10.6106/KJCEM.2025.26.1.057

As of January 27, the Act on the Punishment of Major Accidents extends to companies with fewer than 50 employees, requiring them to establish a safety and health management system. However, small and mediumsized enterprises (SMEs) with annual turnovers under 5 billion won are not required to appoint a safety manager under the OSH Act, making it challenging for them to manage safety independently. This study addresses this issue by comparing the KOSHA Guide C-86-2018 and the KOSH-MA Certification Business Handling Rules to classify safety and health management items for small construction companies. It evaluates the relative importance of these items by construction phase for SMEs that lack a dedicated safety manager or department. This prioritization helps allocate resources effectively and develop targeted risk management strategies. The ultimate goal is to enhance the safety management systems of SMEs by focusing on the construction phases and items that most significantly impact safety and health, providing a framework for improving their safety management practices despite having weaker organizational systems.

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Analysis of AI Model Performance for EMG-Based Human-Robot Handover State Recognition

Kim, Taeeun ; Yang, Kanghyeok

https://dx.doi.org/10.6106/KJCEM.2025.26.1.067

The study developed an approach to recognize handover tasks required for collaborative construction work with robots using a worker’s electromyography (EMG) signals. The study investigated the recognition performance based on different artificial intelligence algorithms. The handover task was divided into three stages (Pre-grasp, Half grasp, and Full grasp) depending on the degree of object grasp. The EMG signals of each grasp state were collected in a laboratory environment. The collected data were visualized in time and frequency domains, and recognition performance was evaluated using convolutional neural networks (CNN) and long short-term memory (LSTM) networks for each data domain to derive the optimal AI model. The analysis results showed that the CNNbased model exhibited superior performance with an accuracy of 0.99 for time domain data, while the LSTM-based model achieved better performance with an accuracy of 0.98 for frequency domain data. Furthermore, the leave-onesubject-out cross-validation approach demonstrated that the LSTM model achieved a notably higher performance with an accuracy of 0.69 compared to the CNN model. The results of the study serve as foundational research for developing technologies for human-robot collaboration in construciton are expected to contribute to improvement of the safety and the productivity through collaborative construction robots.

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Synthetic Video Generation Process Model for Enhancing the Activity Recognition Performance of Heavy Construction Equipment - Utilizing 3D Simulations in Unreal Engine Environment -

Shin, Yejin ; Seo, Seungwon ; Koo, Choongwan

https://dx.doi.org/10.6106/KJCEM.2025.26.1.074

There has been a growing interest in AI (Artificial Intelligence)-based smart management for heavy construction equipment, aiming at real-time monitoring of safety, productivity, and environmental impact. In addition, deep learning-based computer vision technologies have advanced to identify the activities of construction equipment through visual information from CCTV (Closed-Circuit Television) at construction sites. Ensuring the performance of such vision technologies requires a substantial amount of training video datasets collected from construction sites; however, there are limitations in gathering datasets across diverse scenarios due to the nature of construction environments. To address this challenge, this study aimed to develop a synthetic video generation process model to enhance the activity recognition performance of heavy construction equipment. The proposed process model can closely simulate real videos of construction equipment using 3D simulation in game engine. This study validated the stepwise performance improvement of the proposed process model using the 3D ResNet-18 model for excavator activity recognition. The performance of the final stage, measured by the weighted F1-score, showed a 90.89% performance, marking an approximate 25% improvement compared to the first stage (66.02%). This performance is very similar to the activity recognition performance for real videos (90.12%). The confusion matrix demonstrated that the recognition performance and patterns for both real and synthetic videos were considerably similar. The synthetic videos produced through the proposed process model can be utilized as training datasets and serve as a foundational model for simulating excavator operations.

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Comparative Analysis of Advanced Work Packaging (AWP) Implementation by Construction Companies in South Korea

Kang, Youngcheol ; Son, Hyoseok ; Jang, Jaewon ; Leem, Sungjoon ; Jung Hyunho ; Kim, Gyugon ; Kwon, Woosang ; Choi, Eunho ; Chae, Jeonghyeun ; Park, Sang Eon

https://dx.doi.org/10.6106/KJCEM.2025.26.1.083

The purpose of this study is to assess the current status of Advanced Work Packaging (AWP) implementation among construction companies in South Korea. AWP has become increasingly important in the plant industry, a major sector of South Korea’s global construction industry. Strengthening the AWP capabilities is essential for enhancing competitiveness in the international construction market. However, the current lack of understanding regarding the status of AWP implementation in South Korea hinders the formulation of effective strategies for AWP execution. Based on interviews and surveys with six major construction companies in South Korea, this study assesses the current status of AWP implementation in South Korea. An analysis of a subset of 49 survey questions related to AWP implementation - categorized into three groups: AWP experience and organization, work processes and information integration, and AWP maturity model factors ? revealed significant differences in AWP implementation across companies. While some companies had implemented AWP in five or more projects, others had no prior experience with AWP implementation. There was a significant disparity in AWP experience among the participating companies. However, it was evident that all companies involve in the study were keenly interested in the potential benefits of AWP implementation. The findings of this study can serve as valuable foundational information for companies in formulating AWP implementation strategies in the future. Additionally, they can provide essential groundwork for academic research focusing on AWP-related studies.

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A Study on the 3D Model Database Using the BIM Information Classification System of Nuclear Power Plant

Kim, Woojoong ; Kim, Kyo Hoon ; Byon, Sujin ; Lee, Min Jae

https://dx.doi.org/10.6106/KJCEM.2025.26.1.098

3D models applied to the domestic nuclear power plant industry are mainly 3D models for interference review at the design stage, and as construction is completed, the need for 3D models that consider the operation stage has emerged. Accordingly, it is necessary to develop a 3D model for an operating power plant based on the 3D model at the design stage, and a BIM-based information classification system was developed to link information at the operation stage. The nuclear power plant BIM-based 3D model information classification system diversifies the existing PBS classification system and separates the spatial classification system (Building Breakdown Structure) and the system classification system (System Breakdown Structure) to form a classification system and an object classification system (Object Breakdown Structure). was additionally configured. Currently, 3D models that are produced and managed based on physical files act as a significant limitation in the use of 3D models at the operation stage, causing the problem that 3D models are excluded at power plant sites and 2D drawings are mainly used. In order to solve these problems, a method to diversify the 3D model search system must be developed, and to do so, creating a 3D model database that takes object composition into account at the operation stage is an essential method. Therefore, in this study, Propose a method of creating a 3D model database based on the nuclear power plant BIM information classification system to utilize the nuclear power plant 3D model in the operational stage. As a result, it became possible to manage 3D models of nuclear power plants, which were previously managed based on files, as a database, and by applying an information classification system, it became possible to link detailed information down to the facility and component units from an operation and maintenance perspective.

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