• Proceedings of the Korean Institute of Building Construction Conference
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• 2003.11a
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• pp.85-89
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• 2003

With the social demands for tall building, the level of techniques for construction become high and there are various building materials. Also, the construction system become very complicated with the increase of worker. In spite of above facts, the method of construction management depends on the experimental capacity of a construction manager, and there are many unreasonable elements. These elements prevent well-going in construction process. To solve this problem, it is necessary to make a new system of construction management which is adequate for the condition of company and the contents of construction. In this study, the characteristic of production system and the present condition of construction management were investigated with the help of survey. Thus, the new method of construction management which can clear the previous problems was proposed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2003.05a
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• pp.85-89
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• 2003

With the social demands for tall building, the level of techniques for construction become high and there are various building materials. Also, the construction system become very complicated with the increase of worker. In spite of above facts, the method of construction management depends on the experimental capacity of a construction manager, and there are many unreasonable elements. These elements prevent well-going in construction process. To solve this problem, it is necessary to make a new system of construction management which is adequate for the condition of company and the contents of construction. In this study, the characteristic of production system and the present condition of construction management were investigated with the help of survey. Thus, the new method of construction management which can clear the previous problems was proposed.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.242-249
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• 2009

High-rise construction sites, especially those situated in spatially constrained urban areas, have difficulties in timely delivery of materials. Modern techniques such as Just-in-time delivery, and use of information technology such as Project Management Information System (PMIS), are targeted to improve the efficiency of the logistics. Such IT-driven management techniques can be further benefited from state-of-the-art devices such as Radio Frequency Identification (RFID) tags and Ubiquitous Sensor Networks (USN), which has resulted in notable achievements in automated logistics management at the construction sites. Based on those achievements, this research develops USN hardware toolkits for construction lifts, which aims to be automated the vertical material delivery by sensing the material information and routing it automatically to the right place. The gathered information from the sensors can also be used for monitoring the overall status. The developed system will be tested in the actual high-rise construction sites to assess the system's feasibility. The proposed system is being implemented using Zigbee communication modules and RFID sensor networks which will communicate with the intelligent palette system (previously developed by the authors). To support the system, a lift-mountable intelligent toolkit is under development. Its feasibility test will be conducted by applying the implemented system to a test bed and then analyzing efficiency of the system and the toolkit. The collected test data will be provided as a basis of autonomous vertical transport equipment development. From this research, efficient management of the material lift is expected with increased accuracy, as well as better management of overall construction schedule benefited from the system. Further research will be expected to develop a smart construction lift, which will eliminate the need for human supervision, thus enabling a real 'autonomous' operation of the system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2001.11a
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• pp.124-129
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• 2001

With the social demands for tall building, the level of techniques for construction become high and there are various building materials. Also, the construction system become very complicated with the increase of worker. In spite of above facts, the method of construction management depends on the experimental capacity of a construction manager, and there are many unreasonable elements. These elements prevent well-going in construction process. To solve this problem, it is necessary to make a new system of construction management which is adequate for the condition of company and the contents of construction. In this study, the characteristic of production system and the present condition of construction management were investigated with the help of survey. Thus, the new method of construction management which can clear the previous problems was proposed.

• Journal of Construction Engineering and Project Management
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• v.7 no.1
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• pp.37-49
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• 2017

The Jinshanling section of the Great Wall of China is a series of fortifications in northern China that was constructed for strategic military defenses. This section was first built in the beginning of the Ming Dynasty in AD 1368 and then underwent major construction, reconstruction and renovation during the late Ming Dynasty, approximately in AD 1569. The Jinshanling section is 10.5 km long, a very short section compared with the entire 21,200 km wall. The wall section is located in Luanping County, Hebei province, China. This research paper focuses on the construction methods and materials of the wall and the towers in the area. The research methodology includes site visits, knowledge acquisition of experts and 3D graphic modeling. This study reveals that the materials selected for the structure include rubbles and rammed earth, bricks, stones, timber, and mortar. The erection sequence of the wall and the towers was a bottom-up fashion using various ancient construction techniques, such as the fire-setting rock blasting techniques and the surveying techniques from the Sea Island Mathematical Manual.

• 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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• 2020.12a
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.188-197
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• 2020

Due to hazardous working environments at complex, unstructured, and dynamic construction sites, workers frequently face potential safety and health risks throughout the construction process. In this regard, addressing safety challenges remains one of the top priorities. Construction workers' ability to identify and assess risks is acquired through training, which is one of the primary key factors to determine their safety and wellbeing in hazardous working environments. As such, safety managers constantly focus on the effectiveness of the training materials provided to the workers. However, the construction workers are considerably at greater risk of injuries and fatalities compared to the workers in other industries. In this regard, further studies are required to build up a body of knowledge on the conventional safety training approaches as well as their evaluation techniques in order to boost up the adoption by the practitioners in a widespread manner. This paper provides a systematic review of the current safety training approaches and the various techniques for measuring their effectiveness. The attributes of the current safety training methods for construction workers and their evaluation techniques are identified and analyzed. Results indicated that: 1) immersive environment-based training methods are effective than the traditional safety training methods; 2) this effectiveness can be empirically supported by evaluation strategies, but the current techniques are subjective, intrusive, and error-prone. This research offers fresh opportunities to investigate the training strategies by objectively monitoring the physiological responses of construction crews. The results of this study can be used by researchers and practitioners to identify and determine optimal safety training programs that could potentially become ubiquitous in the construction industry.

• Magazine of RCR
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• v.4 no.4
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• pp.41-47
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• 2009

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.268-270
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• 2013

The construction project can be defined as a network combined between elements which are engineering, procurement, construction, start-up, and has a plenty of subjects and partners. For the successful project management in construction industry, it is necessary to adopt various management methods, such as lean production, information network system, SCM(Supply Chain Management), which can increase the efficiency of project management. During recent years, even though various management techniques have been applied, the SCM system has not formulated in construction industry. Especially, the Nuclear Power Plant Construction which requires high regulations of the safety and security has not applied SCM. Therefore, this study aimed to propose technology area of SCM in Korean Nuclear Power Plant Construction.