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Modified analytical AI evolution of composite structures with algorithmic optimization of performance thresholds

  • ZY Chen (School of Science, Guangdong University of Petrochemical Technology) ;
  • Yahui Meng (School of Science, Guangdong University of Petrochemical Technology) ;
  • Huakun Wu (School of Computer Science, Guangdong Polytechnic Normal University) ;
  • ZY Gu (School of Science, Guangdong University of Petrochemical Technology) ;
  • Timothy Chen (Engineering and Applied Science, California Institute of Technology)
  • Received : 2024.05.30
  • Accepted : 2024.09.17
  • Published : 2024.10.10

Abstract

This study proposes a new hybrid approach that utilizes post-earthquake survey data and numerical analysis results from an evolving finite element routing model to capture vulnerability processes. In order to achieve cost-effective evaluation and optimization, this study introduced an online data evolution data platform. The proposed method consists of four stages: 1) development of diagnostic sensitivity curve; 2) determination of probability distribution parameters of throughput threshold through optimization; 3) update of distribution parameters using smart evolution method; 4) derivation of updated diffusion parameters. Produce a blending curve. The analytical curves were initially obtained based on a finite element model used to represent a similar RC building with an estimated (previous) capacity height in the damaged area. The previous data are updated based on the estimated empirical failure probabilities from the post-earthquake survey data, and the mixed sensitivity curve is constructed using the update (subsequent) that best describes the empirical failure probabilities. The results show that the earthquake rupture estimate is close to the empirical rupture probability and corresponds very accurately to the real engineering online practical analysis. The objectives of this paper are to obtain adequate, safe and affordable housing and basic services, promote inclusive and sustainable urbanization and participation, implement sustainable and disaster-resilient buildings, sustainable human settlement planning and management. Therefore, with the continuous development of artificial intelligence and management strategy, this goal is expected to be achieved in the near future.

Keywords

Acknowledgement

The authors are grateful for the research grants given to Ruei-Yuan Wang from the Projects of Talents Recruitment of GDUPT, Peoples R China under Grant NO. 2019rc098, and the research grants given to ZY Chen from the Projects of Talents Recruitment of GDUPT (NO. 2021rc002) in Guangdong Province, Peoples R China. as well as to the anonymous reviewers for constructive suggestions.

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