Author(s)

Renchuan Qian ¹, Yanyun Qian ²

Affiliation(s)

¹ CEO Office, Wenzhou Data Management and Development Group Co., Ltd., Wenzhou, China
² School of International Education, Guangxi University of Science and Technology, Liuzhou, China

Corresponding Author

Renchuan Qian

ABSTRACT

With the rapid development of high-rise buildings, structural health testing has become a research hotpot in the field of civil engineering. Effective and rapid identification of the location and extent of possible damage to high-rise building structures has become particularly important. The purpose of this paper is to study the damage identification and safety evaluation of artificial neural networks in high-rise buildings. Based on the theory of artificial neural networks and structural damage recognition, this paper proposes a method of damage recognition based on artificial neural networks. In the experimental part, the modal strain energy difference (first-order mode) was used as the input of the BP neural network.A total of 9 networks were trained and tested. Through statistical analysis of the test results, it was shown that a single damage index was used as the BP neural network. The input of the network is a single hidden layer network. With enough training samples, the trained network can make accurate damage prediction for a new set of data. Experimental results show that this method can identify the location and degree of damage at the same time, and the accuracy rate is greatly improved. In this paper, through the training and testing of single injury, when the dimension is 121, the accuracy of positioning is 100%, and the accuracy of identifying injury is 92%.

KEYWORDS

Artificial Neural Network, Damage Identification, High-Rise Buildings, Structural Health Monitoring, Comprehensive Evaluation

CITE THIS PAPER

Renchuan Qian, Yanyun Qian, Damage Identification and Comprehensive Safety Evaluation of Artificial Neural Network for High-rise Buildings. Journal of Civil Engineering and Urban Planning (2022) Vol. 4: 58-71. DOI: http://dx.doi.org/10.23977/jceup.2022.040209.

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Sponsors, Associates, and Links

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• Journal of Sustainable Development and Green Buildings
  
  
• Landscape and Urban Horticulture
  
  
• Bridge and Structural Engineering
  
  
• Soil Mechanics and Geotechnical Engineering
  
  
• Journal of Municipal Engineering
  
  
• Heating, Ventilation and Air Conditioning
  
  
• Indoor Air Quality and Climate
  
  
• Computer Aided Architecture Design