• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.11-17
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• 2022

Design-bid-build (DBB) is the most common project delivery method among highway projects. State Highway Agencies (SHAs) usually apply a low-bid approach to select contractors for their DBB projects. In this approach, the Federal Highway Agency suggests SHAs heighten contractors' competition to lower bid prices. However, these attempts may become ineffective due to collusive bidding arrangements among certain contractors. One common strategy is the rotation of winning bidders of a group of contractors who bid on many of the same projects. These arrangements may also be specific to a particular region or vary in time. Despite the practices' adverse effects on bidding outcomes, an effective model to detect red-flag bidding patterns is lacking. This study fills the gap by proposing a novel framework that utilizes pattern mining techniques and statistical tests for unusual pattern detection. A case study with historical data from an SHA is conducted to illustrate the proposed framework.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.192-194
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• 2015

The growing size and complex process in construction project recently leads to increase risk and the losses as well. Even though researchers have identified the major risk indicators, there is lack of comprehensive and quantitative research for identifying the relationship between the risk indicators and economic losses associated with construction projects. To address this shortage of research, this study defines risk indicators and create a framework to assess the influence of economic losses from the indicators. An insurance company's claim payout record was accepted as the dependent variable to reflect the real economic losses. Based on the claims, we categorized the causes and results of accidents. To establish framework, built environment vulnerability indicators and geographical vulnerability indicators were employed as the risk indicators. A Pearson correlation analysis was adopted to validate the relationship with loss ratio and risk indicators. Consequently, this framework and its results may offer significant references for under writers of insurance companies and loss prevention activities.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.433-436
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• 2013

This study suggests IFC-based bridge information modeling methods and its application model in BIM environment. Data model extension for bridge structure was achieved using user-defined property sets based on IFC framework. First, identification information was added. Bridge members are identified through physical and spatial semantic information added as property sets. Instances for semantic information were assigned according to standardized rules. Second, CO2 related factors were added for application information model. It can play a role to calculate and manage the quantity of CO2 emission. Third, properties for temporary structure to estimate and manage the construction cost were added. Finally, we investigated proposed methods through implementing the application information model of bridges.

• 대한토목학회논문집
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• 제37권2호
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• pp.419-425
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• 2017

다양한 시설의 건설, 다수 이해관계자의 참여, 긴 사업기간 등의 복잡성으로 인해 대규모 건설프로젝트는 이를 관리함에 있어 어려움이 있다. 효과적인 대규모 프로젝트관리를 위해서는 합리적인 진도율 측정이 중요하고, 이를 위한 다양한 방법들이 제안되었다. 하지만 기존의 방법들은 단일 또는 일부 시설의 건설 프로젝트로 한정하였기 때문에 다양한 시설들을 건설하는 대규모 프로젝트에 적용하는 데 한계가 있다. 따라서 본 연구는 대규모 건설프로젝트의 특성을 반영한 진도율 측정 프레임워크를 제안함을 목표로 한다. 프레임워크는 작업분류체계 개발, 주요시설별과 하위시설별 가중치 산정, 통합 진도율 산정으로 총 4가지 단계로 이루어져 있다. 본 프레임워크를 검증하기 위해서 세종시를 대상으로 사례연구를 수행하였고, 활용 가능성을 검증할 수 있었다. 본 연구를 바탕으로 대규모 건설프로젝트의 진도율을 보다 합리적으로 측정할 수 있고 효과적인 프로젝트관리가 이루어질 것으로 기대된다.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.153-160
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• 2009

The demand for super tall building construction is increasing worldwide. There has been a constant request for achieving early payback on investment by shortening the construction time. This pertains especially for the case of huge investment projects such as super tall building construction. It is very important to shorten the construction time for the building framework, which requires substantial construction time and cost, and this is directly related to the establishment of an optimum lift plan for construction. When there is a problem in the selection of the lift equipment, it is almost impossible to revise the selection, resulting in a possible failure of the project. Thus, the purpose of this study is to analyze the function and logic for the development of the process for the selection of lift equipment for super tall building projects and further development of making the analyzed process into a system. In line with this research objective, the process of selecting the optimum lift equipment by domestic construction company was investigated and analyzed as well as collecting the actual field data. The actual data were obtained by sensors installed on tower cranes at three construction sites with the help from the construction company.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• 국제학술발표논문집
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• The 7th International Conference on Construction Engineering and Project Management Summit Forum on Sustainable Construction and Management
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• pp.52-61
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• 2017

Considering its significant impact on the cost and schedule of construction projects, formwork as one part of temporary facility categories in construction should be arranged precisely. Current practice in the formwork planning is often conducted manually and repetitively, causing low efficiency and time waste. This study proposes an automated framework to generate more accurate and detailed formwork plans by utilizing information from building information modeling (BIM) considering the adequate geometric and semantic information provided by the BIM model. The dimensions and quantities information of elements in a building can be extracted automatically. Then, a rule is prepared for calculating the required forms erected around elements based on the contact areas. Finally, an algorithm of integrating first fit decreasing (FFD) with coordinated bottom left (CBL) is applied to automatically generate the formwork plan. The BIM-based automated planning framework is demonstrated by an illustrative example. The results show that the proposed framework can generate the formwork plan accurately and automatically, and significantly improve the efficiency in the formwork plan and reuse.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.237-242
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• 2020

Construction noise is among the most critical stressors that adversely affect the quality of life of the people residing near construction sites. Many countries strictly regulate construction noise based on sound pressure levels, as well as timeslots and type of construction equipment. However, individuals react differently to noise, and their tolerance to noise levels varies, which should be considered when regulating construction noise. Although studies have attempted to analyze individuals' stress responses to construction noise, the lack of quantitative methods to measure stress has limited our understanding of individuals' stress responses to noise. Therefore, the authors proposed a quantitative stress measurement framework with a wearable electroencephalogram (EEG) sensor to decipher human brain wave patterns caused by diverse construction stressors (e.g., worksite hazards). This present study extends this framework to investigate the feasibility of using the wearable EEG sensor to measure individuals' emotional stress responses to construction noise in a laboratory setting. EEG data were collected from three subjects exposed to different construction noises (e.g., tonal vs. impulsive noises, different sound pressure levels) recorded at real construction sites. Simultaneously, the subjects' perceived stress levels against these noises were measured. The results indicate that the wearable EEG sensor can help understand diverse individuals' stress responses to nearby construction noises. This research provides a more quantitative means for measuring the impact of the noise generated at a construction site on neighboring communities, which can help frame more reasonable construction noise regulations that consider various types of residents in urban areas.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.653-658
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• 2011

Construction works of civil infrastructure projects generate a considerable amount of carbon emissions by utilizing a set of energy-intensive equipment and causing traffic congestion. However, the voluntary efforts of the contractor to mitigate these emissions are at an early stage. To address this issue, this paper explores the opportunities to take carbon emissions that would be caused from construction works into consideration in contracting methods and procedures. The opportunities for reducing carbon emissions from construction activities themselves are examined under the framework of Performance Contracting for Construction (PCfC), and carbon emissions from traffic congestion are attempted to be incorporated into the Road User Cost (RUC) calculation. This paper also identifies and discusses major challenges that must be confronted when considering the mitigation of these emissions in contracting methods and procedures.