[논문]건축물 안전등급 산출을 위한 외관 조사 상태 평가 데이터 기반 DNN 모델 구축

이재민; 김상용; 김승호

doi:10.5345/jkibc.2021.21.6.665

건축물 안전등급 산출을 위한 외관 조사 상태 평가 데이터 기반 DNN 모델 구축
Development of a Building Safety Grade Calculation DNN Model based on Exterior Inspection Status Evaluation Data 원문보기

한국건축시공학회지 = Journal of the Korea Institute of Building Construction, v.21 no.6, 2021년, pp.665 - 676

이재민 (Department of Architecture, Yeungnam University) , 김상용 (Department of Architecture, Yeungnam University) , 김승호 (Department of Architecture, Yeungnam University College)

초록
AI-Helper

노후 건축물의 수가 증가함에 따라, 건물의 안전진단, 유지 보수에 대한 중요성이 증가하고 있다. 기존 외관 조사는 점검자의 주관적인 판단이 수반되어 평가 결과가 다르고 객관성과 신뢰성이 떨어진다. 따라서 본 연구는 기존 연구를 통해 기실시된 외관 조사 및 상태 평가 프로세스의 한계를 제시하였으며, UAV, Laser Scanner를 통해 3D Point Cloud 데이터를 수집하였다. 또한, Reverse Engineering 기술을 이용하여 3D 모델을 생성한 후 객관적인 상태평가 데이터를 취득하였다. 이후 기존의 정밀검사 데이터와 정밀 안전진단 데이터를 활용하여 DNN 구조를 생성하고, 고정밀도 측정 장치를 이용하여 얻은 상태평가 데이터를 적용하여 객관적인 건물안전등급을 산출하였다. 자동화된 프로세스는 20개의 노후된 건축물에 적용되며 동일 면적 건축물 기준 수작업으로 실시되는 안전진단의 시간에 비해 약 50% 감소하였다. 이후 본 연구에서는 안전등급 결과값과 기존값을 비교하여 안전등급 산출과정의 정확성을 검증하고 약 90%의 높은 정확도를 가진 DNN을 구축하였다. 이는 향후 노후 건물의 안전등급 산정의 신뢰성이 향상되고 비용과 시간을 절약해 경제성이 향상될 것으로 기대된다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (8)

그림 Figure 1. Obtaining Condition Assessment Data with Reverse Engineering 3D Model
표 Table 1. Status Assessment criteria
그림 Figure 2. Classified Condition Assessment Results
그림 Figure 3. DNN Data analysis process
그림 Figure 4. Reverse Engineering 3D Model-based Condition Assessment Data acquisition process
그림 Figure 5. Learned DNN Learning Result
표 Table 2. Acquired Condition Assessment Data
표 Table 3. Error in measurement Data and Actual Data

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표제어: PCR

동의어: Packet Collision Rate

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