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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.25 No.2, 2024-03

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Analysis of Practical Use Cases and Proposal for Improvements of Machine Guidance System Utilized in Smart City Construction Projects

Kim, Sung Yeop ; Lee, Won Hyo ; Kang, Leen Seok

https://dx.doi.org/10.6106/KJCEM.2024.25.2.003

The purpose of this study is to analyze the effects of smart construction equipment applied at the construction site of the first smart city in the Korea and derive an application strategy for the utilization of smart construction equipment. To achieve this, aythors examined the practical effects and issues of safety systems and construction systems utilizing machine guidance (MG) technology, which is a representative smart construction equipment used in civil engineering construction sites. Both the MG safety system and MG construction system were found to be sufficiently effective in improving construction productivity. However, there are challenges that need to be addressed, such as the approval process for work results using MG systems, system changes due to frequent replacement of on-site equipment, and usability improvements for elderly on-site workers. The study presented some solutions that have been implemented on-site to address these issues. The utilization effects and issues presented in the study were analyzed through direct feedback from workers and managers who have utilized the MG technology on-site for a considerable period of time. These results can be used as preliminary data for the similar construction projects, considering the limited availability of empirical analysis data for equipment automation.

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A Study on the Success Factors Related to the Performance of Power Plant Engineering Projects

Suh, Jaeho ; Lee, Dongmyung

https://dx.doi.org/10.6106/KJCEM.2024.25.2.011

Power plant engineering industry obtains EPC plan project and delivers results about electricity, measurement, machinery, and piping and so on. Its works are taken by projects. Although power plant engineering composes 2~5% of whole EPC project cost, it’s one of the fundamentals because it affects process after planning step a lot. However, domestic power plant engineering companies’ project performance ability is insufficient and there’s a need for systematic performance. Thus, this study defined related factors of successful performance and analyzed the priority among them through analytical hierarchy process. All respondents recognized experience, knowledge, and communication as important factors. Administrators considered knowledge, experience, and communication. But hands-on workers considered experience, knowledge, human resources. Those who have experience in oversea project considered process, experience, human resources. However those who don’t have experience in oversea project considered knowledge, experience, and communication. Recognition of important factors varies by the position and work experience of members.

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A Study of the Influencing Factors for Decision Making on Construction Contract Types : Focused on DoD Construction Acquisitions with Firm Fixed Price and Cost Reimbursable in FAR

Son, Young-Hoon ; Kim, Kyung-Rai

https://dx.doi.org/10.6106/KJCEM.2024.25.2.023

This study analyzed the correlation between each of the 12 influencing factors in FAR 16.04 and the decision-making process for construction contract types, using data from a total of 2,406 DoD Construction Acquisitions spanning from 2008 to 2022. The study considered 12 independent variables, grouped into 4 Characteristics with 3 factors each. Meanwhile, all other contract types were categorized into two types: Firm-Fixed-Price (FFP) and Cost-Reimbursement Contract (CRC), which served as the dependent variables. The findings revealed that FFP contracts significantly dominated in terms of acquisition volume. In line with prevailing beliefs, logistic data analysis and Analytical Hierarchy Process (AHP) analysis of Relative Weights from Experts’ Survey demonstrated that independent variables like Uncertainty of the Scope of Work and Complexity found out to be increasing the likelihood of selecting CRC. The number of contractors in the market does indeed influence the possibilities of contract decision-making between CRC and FFP. Meanwhile, the p-values of the top 3 influencing factors on CRC from the AHP analysis?namely, Appropriateness of CAS, Project Urgency, and Cost Analysis?exceeded 0.05 in the binominal regression results, rendering it inconclusive whether they significantly influenced the construction contract type decision, particularly with respect to payment methods. This outcome partly results from the fact that a majority of respondents possessed specific experiences related to the USFK relocation project. Furthermore, influencing factors in construction projects behave differently than common beliefs suggest. As a result, it is imperative to consider the 12 influencing factors categorized into 4 Characteristics areas before establishing acquisition strategies for targeted construction projects.

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Edge Detection and ROI-Based Concrete Crack Detection

Park, Heewon ; Lee, Dong-Eun

https://dx.doi.org/10.6106/KJCEM.2024.25.2.036

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

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Determinants of Efficiency of Specialty Construction Companies Using DEA and Tobit Regression Models

Jung, Dae-Woon ; Son, Young-Hoon ; Kim, Kyung-Rai

https://dx.doi.org/10.6106/KJCEM.2024.25.2.045

This study analyzed the efficiency determinants of specialty construction companies by industry using the DEA model and the Tobit model. The analysis targets are 394 specialty construction companies as of 2022. As a result of analysis of efficiency determinants using 12 company characteristics as independent variables, the biggest problem for specialty construction companies was overall efficiency reduction due to rising labor costs. In addition, in a situation where construction companies' loan regulations are severe, the debt ratio was found to have a positive effect on efficiency. Company size had a different impact by industry, and the number of businesses held, credit score, and total capital turnover had an effect only on some industries. This study presents results that are an advance on existing research in that it strategically analyzes factors for improving the efficiency of specialty construction companies. However, it has limitations such as limiting the analysis to only specialty construction companies subject to external audit, insufficient number of companies subject to analysis by industry, and analyzing relative efficiency in the same category for each industry.

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A Study on Development of Construction Standard Production Rates and Cost Analysis for Off-Site Construction (OSC)-Based PC Structure Construction Costs - Comparison with RC Method -

Lee, Hanso ; Lee, Chiho ; Han, Heesu ; Lee, Jeongwook

https://dx.doi.org/10.6106/KJCEM.2024.25.2.056

A construction standard production rates system for the factory built and on-site installation phases of OSC (Off-Site Construction)-based precast concrete (PC) structures in apartment buildings was recently proposed to establish an objective cost standard (Lee et al., 2021). In addition, the Korean government has taken steps to improve the institutional foundation for the systematic calculation of PC construction costs such as revising construction standard production rates for major components that can be applied to the on-site installation phase of PC method. In this study, we analyzed the results of a field survey of apartment building PC structures and collected expert opinions to develop factory-built and on-site installation standard production rates that can be applied to apartment building PC method. We also propose directions for improving the standard production rates so that they can be applied to the site and component of apartment buildings by comparing them with the current standard production rates. This study also derived the cost characteristics and cost reduction measures of PC construction by calculating the construction costs using the developed rates and comparing the construction costs with the RC methods of apartment buildings of the same scale. The construction standard production rates for PC construction derived in this study are expected to contribute to the spread of PC construction by ensuring the objectivity and consistency of the results of PC methods cost estimation.

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Rebar Spacing Fixing Technology using Laser Scanning and HoloLens

Lee, Yeongjoo ; Kim, Jeongseop ; Lee, Jin Gang ; Kim, Minkoo

https://dx.doi.org/10.6106/KJCEM.2024.25.2.069

Currently rebar spacing inspection is carried out by human inspectors who heavily rely on their individual experience, lacking a guarantee of objectivity and accuracy in the inspection process. In addition, if incorrectly placed rebars are identified, the inspector need to correct them. Recently, laser scanning and AR technologies have been widely used because of their merits of measurement accuracy and visualization. This study proposes a technology for rebar spacing inspection and fixing by combining laser scanning and AR technology. First, scan data acquisition of rebar layers is performed and the raw scan data is processed. Second, AR-based visualization and fixing are performed by comparing the design model with the model generated from the scan data. To verify the developed technique, performance comparison test is conducted by comparing with existing drawing-based method in terms of inspection time, error detection rate, cognitive load, and situational awareness ability. It is found from the result of the experiment that the AR-based rebar inspection and fixing technology is faster than the drawingbased method, but there was no significant difference between the two groups in error identification rate, cognitive load, and situational awareness ability. Based on the experimental results, the proposed AR-based rebar spacing inspection and fixing technology is expected to be highly useful throughout the construction industry.

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