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http://dx.doi.org/10.5345/JKIBC.2021.21.6.665

Development of a Building Safety Grade Calculation DNN Model based on Exterior Inspection Status Evaluation Data  

Lee, Jae-Min (Department of Architecture, Yeungnam University)
Kim, Sangyong (Department of Architecture, Yeungnam University)
Kim, Seungho (Department of Architecture, Yeungnam University College)
Publication Information
Journal of the Korea Institute of Building Construction / v.21, no.6, 2021 , pp. 665-676 More about this Journal
Abstract
As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.
Keywords
safety grade; 3D point cloud; reverse engineering; deep-learning; deep-neural network;
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