• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.709-716
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• 2020

Recently, the drone's equipment development and software technology have dramatically improved. With such developments, the applicability is increasing in various fields that require rapid geospatial information, and in practice, regulations and systems have been established, and the fields in use are increasing. Also, in Korea, corporations and public institutions are actually using and researching drones in fields such as aircraft development, communication technology development, construction site use, and surveying. However, there are no fields where drones are actually used in road construction sites. Therefore, in this study, to utilize drones that have been actively used in the civil engineering and construction fields for road construction, we investigated the current status of the Korea Highway Corporation's field drone use research and classified the possibility of drone introduction by road construction. Finally, a method of using drones at road construction sites was proposed to prepare a method for using drones at road construction sites.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.107-113
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• 2004

The advent of Radio Frequency Identification (RFID) technology has created new opportunities for improving the material management function in the construction industry and is starting to provide major improvements in the efficiency of the material management process. The objective of the research reported herein was to introduce the benefits of RFID technology on material management in the construction industry. RFID technology is discussed and various aspects of this technology are investigated. Potential RFID applications on material management proposed by Construction Industry Institute (CII) Breakthrough Strategy Committee (BTSC) and Fully Integrated and Automated Technology (FIATECH) are spelled out Finally, business analysis of RFID to estimate its impact is provided. It is concluded that there is a significant potential for improvement of material management function in the construction industry through the use of RFID.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.77-85
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• 2015

The data are often missed and many errors of the data are generated in the input process for the post evaluation system on the construction projects, and the reliability of the data falls down much. Accordingly, the detailed analysis about missing and error of data was conducted to ensure reliability of the analysis results about post evaluation on the construction projects. As results in this study, a lot of input data were missed at the initial construction phase, and the data errors were found in the inaccuracy of reference reports, the lack of understanding about input data, and the failure of KRW unit.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1114-1121
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• 2022

Construction laborers and crews play a critical role in achieving a safe and productive construction site. Many past research studies used top-down approaches/perspectives for studying the impact of laborers' performance on overall construction site outputs with limited flexibility in accounting for laborers' various characteristics. However, the recent reap in computational advances allowed applications of bottom-up architectures, which can potentially incorporate heterogeneous characteristics of laborers' individual behavioral and decision-making features effectively. Accordingly, agent-based modeling (ABM), as a tool to leverage a bottom-up methodological approach, has been widely adopted by recent research. Existing literature investigated the influence of changes in laborers' behaviors and interactions on either construction sites' safety performance or productivity performance individually, leaving the tradeoff between safety and productivity in this context relatively unexplored. Accordingly, this study aims to develop an agent-based framework to study the tradeoff between project safety and productivity performances resulting from changes in laborers' behaviors after attending safety trainings. Our findings via simulations indicate that proper safety trainings can improve safety performance without negatively impacting productivity performance.

• Journal of Distribution Science
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• v.20 no.3
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• pp.23-31
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• 2022

Purpose: The purpose of this study is to analyze the Safety and Logistical Distribution Management system in Thailand's Highway Work Zone based on data from Logistics drivers. Accidents in highway construction zones have caused enormous casualties in Thailand yearly. Statistical data shows evidence of correlation between numbers of accidents and drivers' recklessness. Research design, data, and methodology: In this study, we conducted an in- depth interview with 414 logistics drivers and highway construction workers in Khon Kaen province, Thailand. The data was collected based on 63 questionnaires aiming at capturing factors contributing to the risk of safety and cause of accidents in logistic infrastructures such as Highway work zone. Results: The result reveals two significant factors affecting safety in highway work zone, which includes construction environment and safety management system. Moreover, the result shows that feeling of afraid and confused while driving within the construction zones significantly affecting driver's risk of having an accident. Conclusions: The findings of this study offer that a strategic planning and evaluation of the logistics drivers' satisfaction and construction workers' participation to mitigate highway accidents at construction zones and that drivers' knowledge and perception toward construction safety management plays a significant role in preventing highway accidents at the construction areas.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.450-457
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• 2022

The construction industry is demanding, dynamic, and complex making it difficult for workers to recognize hazards. The nature of construction tasks exposes workers to several critical risk factors, such as a high rate of exertion and fatigue. Recent studies suggest that fatigue may impact hazard recognition in the construction industry. However, most studies rely on subjective measures when assessing the relationship between physical fatigue and hazard recognition, limiting such studies' efficacy. Thus, this study examined the relationship between physical fatigue and hazard recognition using a controlled experiment. Worker fatigue levels were captured using physiological data and a subjective exertion scale. The findings confirmed that physical exertion plays a significant role in hazard recognition skills (p < 0.05). This research contributes to theory and practice by providing a process for objectively assessing the influence of physical fatigue on worker safety and providing construction professionals with some critical insight needed to improve workplace safety.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.336-343
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• 2022

Industrial robotic arms are widely adopted in numerous industries for manufacturing automation under factory settings, which eliminates the limitations of manual labor and provides significant productivity and quality benefits. The U.S. modular construction industry, despite having similar controlled factory environments, still heavily relies on manual labor. Thus, this study investigates the U.S., Canada, and Europe-based leading modular construction companies and research labs implementing industrial robotic arms for manufacturing automation. The investigation mainly considered the current research scope, industry state, and constraints, as well as identifying the types and specifications of the robotic arms in use. First, the study investigated well-recognized modular building associations, the Modular Building Institute (MBI), and renowned architecture design magazine, Dezeen to gather industry updates. The authors discovered one university lab and a few companies that adopted Switzerland-based robotic arms, ABB. Researching ABB robotics led to the discovery of ABB's competitor, Germany-based KUKA robotic arms. Consequently, research extended to the companies and labs adopting KUKA models. In total, this study has identified seven modular companies and four research labs. All companies employed robotic arms and gantry robot combinations in a production-line-like system for partial automation, and some adopted design standardization for optimization. The common goal among the labs was to achieve greater flexibility and full automation with robotic arms. This study will help companies better implement robotic arm automation by providing recommendations from investigating its current industry status.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.301-312
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• 2017

Quality evaluation and control represent increasingly important concerns for construction quality management. There is an evident need for a standard data model to be used as the basis for computer-aided quality management. This study focuses on how to realize evaluation of construction quality based on BIM and database technology. In this paper, the reinforced concrete main structure is taken as an example, and the BP neural network evaluation model is established by inquiring current construction quality acceptance specification and evaluation standard. Furthermore, IFC standard is extended to integrate quality evaluation information and realize the mapping of evaluation information in BIM model, contributing to the visualization and transfer sharing of evaluation information. Furthermore, the conceptual entity model is designed to build quality evaluation database, and this paper select MySQL workbench system to achieve the establishment of the database. This study is organized to realize the requirement of visualization and data integration on construction quality evaluation which makes it more effective, convenient, intuitive, easy to find quality problems and provide more comprehensive and reliable data for the quality management of construction enterprises and official construction administratiors.

• Steel and Composite Structures
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• v.21 no.1
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• pp.177-194
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• 2016

The cable-supported ribbed beam composite slab structure (CBS) is a new type of pre-stressed hybrid structure. The standard construction method of CBS including five steps and two key phases are proposed in this paper. The theoretical analysis and experimental research on a 1:5 scaled model were carried out. First, the tensioning construction method based on deformation control was applied to pre-stress the cables. The research results indicate that the actual tensile force applied to the cable is slightly larger than the theoretical value, and the error is about 6.8%. Subsequently, three support dismantling schemes are discussed. Scheme one indicates that each span of CBS has certain level of mechanical independence such that the construction of a span is not significantly affected by the adjacent spans. It is shown that dismantling from the middle to the ends is an optimal support dismantling method. The experimental research also indicates that by using this method, the CBS behaves identically with the numerical analysis results during the construction and service.