Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application

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Publicado en:Buildings vol. 15, no. 13 (2025), p. 2216-2234
Autor principal: Shi, Kuang
Otros Autores: Sun, Tingting
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MDPI AG
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024 7 |a 10.3390/buildings15132216  |2 doi 
035 |a 3229142214 
045 2 |b d20250701  |b d20250714 
084 |a 231437  |2 nlm 
100 1 |a Shi, Kuang  |u Scientific Research Institute, Hefei University of Technology, No. 193 Tunxi Road, Baohe District, Hefei 230009, China; whutsk123@163.com 
245 1 |a Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a This study employs a PID (Proportion, Integral, Differential)-based search algorithm (PSA) to achieve structural damage identification (SDI), localization, and quantification. We developed finite element programs for a 10-element simply supported beam, a 21-element truss, and a 7-story steel frame, assigning damage factors to each element as design variables. The Relative Frequency Change Rate (RFCR) and Modal Assurance Criterion (MAC) were calculated as objective functions for PSA iteration; comparative studies were then conducted against Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of damage identification accuracy, computational efficiency, and noise robustness. Results demonstrate that PSA achieves exceptional damage localization accuracy within 1% error in severity under noise-free conditions. With 1–3% noise, PSA maintains precise damage localization despite minor severity estimation errors, while other algorithms exhibit false positives in intact elements. Within the fixed number of iterations, PSA outperforms GA and PSO in computational efficiency. Although SA shows faster computation, it significantly compromises identification accuracy and fails in damage detection. The regularization term enables PSA to maintain noise-resistant damage identification even in a 70-element frame structure, demonstrating its potential for robust damage assessment across diverse structural types, scales, and noisy environments. 
653 |a Particle swarm optimization 
653 |a Accuracy 
653 |a Algorithms 
653 |a Identification 
653 |a Nondestructive testing 
653 |a Damage detection 
653 |a Damage assessment 
653 |a Frame structures 
653 |a Computer applications 
653 |a Localization 
653 |a Modal assurance criterion 
653 |a Damage localization 
653 |a Vibration 
653 |a Efficiency 
653 |a Comparative studies 
653 |a Regularization 
653 |a Control algorithms 
653 |a Genetic algorithms 
653 |a Neural networks 
653 |a Computational efficiency 
653 |a Steel frames 
653 |a Search algorithms 
653 |a Simulated annealing 
653 |a Optimization algorithms 
700 1 |a Sun, Tingting  |u School of Road Bridge & Harbor Engineering, Nanjing Vocational Institute of Transport Technology, No. 629 Longmian Avenue, Jiangning District, Nanjing 211188, China 
773 0 |t Buildings  |g vol. 15, no. 13 (2025), p. 2216-2234 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3229142214/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3229142214/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3229142214/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch