A Fuzzy Krill Herd Approach for Structural Health Monitoring of Bridges using Operational Modal Analysis
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RESEARCH PAPER
A Fuzzy Krill Herd Approach for Structural Health Monitoring of Bridges using Operational Modal Analysis Saeid Jahan1 · Alireza Mojtahedi2 · Samira Mohammadyzadeh1 · Hamid Hokmabady2 Received: 14 December 2019 / Accepted: 25 September 2020 © Shiraz University 2020
Abstract Monitoring bridges is an important issue in the structural safety assessment. As a consequence of inaccessible sections of these structures, the utilization of nondestructive damage identification techniques seems to be vital for maintaining safety and integrity. In this study, a new hybrid Fuzzy Krill Herd approach is performed based on online responses to assess the global structural integrity of an in-service bridge. While the algorithms have been studied previously in the literature, their efficiency in structural health monitoring of a real bridge structure has yet to be investigated. The methodology is applied to two types of steel girders and concrete bridges in this manuscript. The finite element (FE) modeling is considered for further numerical investigation of dynamic characteristics and structural behavior of the bridges. To evaluate the efficiency of the proposed technique, a three-dimensional FE model and a developed simple two-dimensional girders models are simulated. The results indicate the capability of the fuzzy logic approach in obtaining accurate information in the presence of noisy input data or the data with missing values. Based on the results, increasing the number of the measurement modes and using the torsional modes lead to an accurate damage diagnosis process even in symmetric structures. Keywords Bridge structural health monitoring · Damage detection · Fuzzy Krill Herd algorithm · Uncertainty analysis · Operational modal analysis Abbreviations SHM Structural health monitoring FE Finite element FLS Fuzzy logic system GA Genetic algorithm FL Fuzzy logic FS Fuzzy system FIS Fuzzy inference system KHA Krill Herd algorithm FGA Fuzzy genetic algorithm FKHA Fuzzy Krill Herd algorithm BOA Bio-inspired optimization algorithm MD Measurement delta (features) ΔZ Measurement deltas D Damage parameter Gi Physical diffusion of the ith krill individuals * Alireza Mojtahedi [email protected] 1
Department of Structural Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz, Iran
Department of Water Resources Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz, Iran
2
Fi Foraging motion Ni The motion induced by other krill individuals 𝛼 Noise level parameter u Random and uniformly distributed number in the interval [− 1,1] SR Success rate NC Number of times when the system predicts correctly NT Number of the samples of the noisy data L Location of a swarm 𝜇 Fuzzy sets z Input of the fuzzy systems x Output of the fuzzy systems n Number of user-defined locations d User-defined number of measurements m Midpoint of fuzzy set 𝜎 Standard deviation 𝜔 Natural frequency Δ𝜔 Damage indicator E Young’s modulus of material
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Vol.:(0123456789)
Iranian Journal of Scien
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