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

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,...

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Main Authors: Kuang Shi, Tingting Sun
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Buildings
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Online Access:https://www.mdpi.com/2075-5309/15/13/2216
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author Kuang Shi
Tingting Sun
author_facet Kuang Shi
Tingting Sun
author_sort Kuang Shi
collection DOAJ
description 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.
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spelling doaj-art-7bbd28a09fd44dc0b4de0dfe0c67ca622025-08-20T03:28:33ZengMDPI AGBuildings2075-53092025-06-011513221610.3390/buildings15132216Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired ApplicationKuang Shi0Tingting Sun1Scientific Research Institute, Hefei University of Technology, No. 193 Tunxi Road, Baohe District, Hefei 230009, ChinaSchool of Road Bridge & Harbor Engineering, Nanjing Vocational Institute of Transport Technology, No. 629 Longmian Avenue, Jiangning District, Nanjing 211188, ChinaThis 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.https://www.mdpi.com/2075-5309/15/13/2216structural damage identificationPID controloptimization algorithmfinite element model updatingobjective function
spellingShingle Kuang Shi
Tingting Sun
Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application
Buildings
structural damage identification
PID control
optimization algorithm
finite element model updating
objective function
title Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application
title_full Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application
title_fullStr Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application
title_full_unstemmed Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application
title_short Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application
title_sort structural damage identification using pid based search algorithm a control theory inspired application
topic structural damage identification
PID control
optimization algorithm
finite element model updating
objective function
url https://www.mdpi.com/2075-5309/15/13/2216
work_keys_str_mv AT kuangshi structuraldamageidentificationusingpidbasedsearchalgorithmacontroltheoryinspiredapplication
AT tingtingsun structuraldamageidentificationusingpidbasedsearchalgorithmacontroltheoryinspiredapplication