A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks

Ho, Long Viet; Nguyen, Duong Huong; Mousavi, Mohsen; Roeck, Guido De; Bui-Tien, Thanh; Gandomi, Amir H.; Wahab, Magd Abdel

doi:10.1016/j.compstruc.2021.106568

Cited by 80 publications

(13 citation statements)

References 32 publications

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“…The vibration-based approach is a subcategory of SHM methods that rely on the fact that structural damage will affect the dynamic characteristics of a structure. The recent trends show an increasing interest in the use of Machine Learning (ML) for SHM systems [ 2 , 3 , 4 , 5 , 6 , 7 ]. Other methods that rely on Bayesian probabilistic techniques have also been presented in the past [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

Zacharakis

Giagopoulos

2022

Sensors

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The continuous development of new materials and larger and/or more complex structures drives the need for the development of more robust, accurate, and sensitive Structural Health Monitoring (SHM) techniques. In the present work, a novel vibration-based damage-detection method that contributes into the SHM field is presented using Metaheuristic algorithms coupled with optimal Finite Element Models that can effectively localize damage. The proposed damage-detection framework can be applied in any kind of detailed structural FE model, while requiring only the output information of the dynamic response of the structure. It can effectively localize damage in a structure by highlighting not only the affected part of the structure but also the specific damaged area inside the part. First, the optimal FE model of the healthy structure is developed using appropriate FE model updating techniques and experimental vibration measurements, simulating the undamaged condition. Next, the main goal of the proposed method is to create a damaged FE model that approximates the dynamic response of the damaged structure. To achieve this, a parametric area is inserted into the FE model, changing stiffness and mass to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed damage-detection framework. On this specific implementation of the framework, the Particle Swarm Optimization (PSO) algorithm is selected which has been used for a wide variety of optimization problems in the past. On the PSO’s search space, two parameters control the stiffness and mass of the damaged area while additional location parameters control the exact position of the damaged area through the FE model. For effective damage localization, the Transmittance Functions from acceleration measurements are used which have been shown to be sensitive to structural damage while requiring output-only information. Finally, with proper selection of the objective function, the error that arises from modeling a physical damage with a linear damaged FE model can be minimized, thus creating a more accurate prediction for the damaged location. The effectiveness of the proposed SHM method is demonstrated via two illustrative examples: a simulated small-scale model of a laboratory-tested vehicle-like structure and a real experimental CFRP composite beam structure. In order to check the robustness of the proposed method, two small damage scenarios are examined for each validation model and combined with random excitations.

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Section: Introductionmentioning

confidence: 99%

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

Zacharakis

Giagopoulos

2022

Sensors

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“…ANN is an intelligent computational technique inspired by the way that the human biological system employs to process data. With recent ground-breaking advances, ANN has been applied commonly to deal with complicated issues in different fields during the past decades 5 – 10 . However, it is acknowledged that ANN still has its fundamental drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Damage assessment in structures using artificial neural network working and a hybrid stochastic optimization

Tran-Ngoc

Khatir

Le-Xuan

et al. 2022

Sci Rep

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Artificial neural network (ANN) has been commonly used to deal with many problems. However, since this algorithm applies backpropagation algorithms based on gradient descent (GD) technique to look for the best solution, the network may face major risks of being entrapped in local minima. To overcome those drawbacks of ANN, in this work, we propose a novel ANN working parallel with metaheuristic algorithms (MAs) to train the network. The core idea is that first, (1) GD is applied to increase the convergence speed. (2) If the network is stuck in local minima, the capacity of the global search technique of MAs is employed. (3) After escaping from local minima, the GD technique is applied again. This process is applied until the target is achieved. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is employed. The effectiveness of ANNPSOGA is assessed using both numerical models and measurement. The results demonstrate that ANNPSOGA provides higher accuracy than traditional ANN, PSO, and other hybrid ANNs (even a higher level of noise is employed) and also considerably decreases calculational cost compared with PSO.

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“…Although all these mentioned studies, in general, showed high potential in damage localization, they did not indicate a quantitative relationship between the damage indices and the reduction in stiffness. To solve this problem, many authors combined damage indicators with an optimization process [23][24][25][26] or used a hybrid model between optimization and feedforward neural network (FNN) coupled with damage indicator [27,28]. The first approach identifies the damage by solving inverse problems or updating some model parameters until meeting a superior agreement between the FE model and the measured modal parameters.…”

Section: Introductionmentioning

confidence: 99%

An efficient stochastic-based coupled model for damage identification in plate structures

Trinh

Roeck

et al. 2022

Engineering Failure Analysis

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Mode shape-based method has shown its dominance in failure identification of beams. However, it is a challenge for fault detection in a plate structure. It requires combined information in two directions to determine the damage location. In this study, a damage index, namely mode shape derivative based damage identification (MSDBDI), is applied to localize damage in fixed-free plate structures. Two-dimensional (2D) displacement mode shapes and their derivatives are used to identify the MSDBDI index. It is a fact that this indicator is stuck in indicating the damage severity. Hence, a coupled model between an artificial neural network (ANN) and antlion optimizer (ALO), so-called ALOANN is used to overcome this drawback. In this method, ALO instead of a backpropagation algorithm is used to look for the best initial values of learnable parameters of ANN i.e. weights and biases through mean squared error (MSE). These obtained parameters are added to ANN for damage identification. The efficiency of the proposed approach is tested with two numerical studies of the plate structures with single and multiple damage scenarios. In the first application, damage scenarios in a plate-like structure are detected. In the second application, ALO first is used to build a FE model of a composite structure based on a vibration experiment. Then the slab of the updated model is assumed to be suffered several damage scenarios. In both applications, failures are localized by using damage index. Then, the proposed approach is used to quantify the corresponding extent by means of changes in frequencies and displacement mode shapes. Values of damage index are achieved from modal properties of one or three out of the first five modes of the two considered structures. A conventional ANN also is investigated for comparison. Results of damage identification indicate that the damage indicator coupled with ALOANN show better performance compared with using ANN alone even when a noise level is assigned to modal properties.

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A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks

Cited by 80 publications

References 32 publications

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

Damage assessment in structures using artificial neural network working and a hybrid stochastic optimization

An efficient stochastic-based coupled model for damage identification in plate structures

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