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RISS 인기검색어
- 검색키워드
- 저자 : Danbing Long (검색결과 4 건)
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Pin-Chan Lee,Junhao Wei,Hsin-I Ting,Tzu-Ping Lo,Danbing Long,Luh-Maan Chang 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.10
Construction has long been seen as a high-risk industry. Many studies conduct risk management by controlling construction risk indicators, but few studies associate risk indicators with time and space to propose long-term risk management methods. Therefore, this study proposes a dynamic analysis and visual tracking method based on behavior-based safety (BBS). This study establishes a BBS observation checklist and records workers’ unsafe behavior. The risk level of unsafe behavior is then determined by grey clustering model. When a high risk occurs, an improved grey correlation model is used to track key indicators that affect risk. In order to achieve visual risk management, this study develops semantic logic to predefine the relationship between components and space. In schedule simulation of BIM, the key indicators of BBS are transformed into an executable visual inspection between work items, components, and space through ARC. This method makes it easier for construction managers to combine time and space to manage safety risk and to adopt appropriate strategies in a timely manner to improve the efficiency of safety management.
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Pin-Chan Lee,Li-Long Zheng,Tzu-Ping Lo,Danbing Long 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.3
Risk management of deep excavation is always an important issue. One of the core problems is to accurately simulate and predict the time series of displacement collected from the site sensors to monitor the risk variation. Meanwhile, the applications of building information modelling (BIM) and geographic information system (GIS) can integrate the construction structures into the surrounding environment, visualizing various information and supporting decision making for risk treatment. Therefore, this paper proposes a risk management system to monitor the risk variation for deep excavation based on optimized grey Verhulst model (GVMm), BIM-3DGIS framework and risk monitoring. The grey Verhulst model (GVM) has demonstrated well performance on saturation curve, such as displacement of deep excavation. This paper establishes the GVMm by improving GVM to predict the displacement more precisely. BIM-3DGIS framework is also built by integrating BIM and 3DGIS in the application level for the efficiency of system operation and the interaction with the risk management platform. BIM-3DGIS framework, working with the risk management platform, can monitor the risk variation of deep excavation effectively and provide visual decision-making supports. A real case of deep excavation is used as an illustrative example to verify the practicability. The results show that the prediction precision of GVMm is better than that of GVM. The application scenarios also demonstrate the effectiveness of the risk management system.
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Pin-Chan Lee,Wei Xie,Tzu-Ping Lo,Danbing Long,Xiaofei Tang 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.8
The maintenance of historic buildings requires a systematic approach to construct and reuse maintenance knowledge. Maintenance knowledge of historic buildings has extensive and specific knowledge, but a small number of experts provide most of experience. With the rapid development of building information modelling (BIM), it can facilitate maintenance management of historic buildings. Recently, research on connecting ontology and BIM was discussed to promote building knowledge management (BKM). However, BKM is less applied in historic buildings and connection strength is also less discussed. Connection strength of knowledge mapping can increase the performance of knowledge retrieval. Therefore, this study aims to build connection between maintenance ontology of historic buildings and BIM, and also proposes a cloud model-based knowledge mapping method to evaluate the connection strength. This study uses FMEA (failure mode and effects analysis) to connect ontology and BIM, and the grey relational analysis to evaluate the connection strength. Meanwhile, cloud model is integrated into FMEA to better deal with uncertain information to increase the reliability. This study adopts a real case to valid the practicability. The results show the proposed method can evaluate the connection strengths with uncertain information and obtain the maintenance knowledge more efficiently.
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An Efficient Design Support System based on Automatic Rule Checking and Case-based Reasoning
Pin-Chan Lee,Tzu-Ping Lo,Ming-Yang Tian,Danbing Long 대한토목학회 2019 KSCE Journal of Civil Engineering Vol.23 No.5
A well building design support system can not only meet the rules but also automatically recommend the appropriate alternatives for designers, but most modifications now are conducted in the manual way. Although the method of automatic rule checking can effectively identify the compliance of rules in Building Information Modeling (BIM) models, recommendation supports are still lacked in applications. This paper aims to propose a design support system, using automatic rule checking to identify the compliance of rules and adopting case-based reasoning to provide recommendations via ontology and semantics. The AHP-TOPSIS (Analytic hierarchy process-Technique for Order Preference by Similarity to an Ideal Solution) method is used to give reliable recommendations rank. A real case is adopted as an illustrative example. Results show that the proposed system can increase the design efficiency in both design checking and modifying. Similar applications can be extended to other fields and rules.
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