Smart Structures and Systems

  • 제30권2호
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  • Pages.145-157
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Structural live load surveys by deep learning

  • Li, Yang (College of Civil Engineering, Tongji University) ;
  • Chen, Jun (College of Civil Engineering, Tongji University)
  • 투고 : 2021.02.22
  • 심사 : 2022.05.06
  • 발행 : 2022.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

키워드

과제정보

This research project was financially supported by National Natural Science Foundation of China (Grant No. 52178151); State Key Laboratory for Disaster Reduction of Civil Engineering (Grant No. SLDRCE19-B-22); and Shanghai TCM Chronic Disease Prevention and Health Service Innovation Center (Grant No. ZYJKFW201811009).

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