An experimental study on damage detection of structures using a timber beam

Choi, Fook Choon; Li, Jianchun; Samali, Bijan; Crews, Keith

doi:10.1007/bf03027066

Cited by 17 publications

(3 citation statements)

References 4 publications

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“…Case 2 : The proposed method is used to detect damages for a steel beam in the laboratory with different damage scenarios (the diagram follows the arrow 1 and 3). To convert the damaged properties ( of the beams from measurements into output data of the network, the following equations are applied 21 . where are intact and damaged stiffness.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…Subsequently, Choi et al [ 12 ] proposed an improved damage detection algorithm to estimate the status of wooden material by comparing the modal strain energy derived from the first two flexural vibration modes. Similarly, the wavelet transform method [ 13 ], the transfer function method [ 14 , 15 ], and the mode shape reconstruction technique [ 16 ] were adopted to find the model shapes of timber, which revealed that the mode shapes before and after damage could be further utilized to identify the damage status and location in timber. However, it is difficult to establish real-time monitoring by using such vibration-based methods since external excitations are required in these methods.…”

Section: Introductionmentioning

confidence: 99%

A Feasibility Study on Timber Damage Detection Using Piezoceramic-Transducer-Enabled Active Sensing

Zhang

Huang

Zheng

2018

Sensors

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In recent years, piezoelectric-based transducers and technologies have made significant progress towards structural health monitoring and damage evaluation for various metal and concrete structures. Timber is still commonly used as a construction material in practical engineering; however, there is a lack of research on the health monitoring of timber-based structures using piezoelectric-based transducers and methods. This paper conducts a feasibility study on timber damage detection using surface-mounted piezoelectric patches, which enable the stress-wave-based active sensing approach. Typical damage modes in timber frame structures, such as surface cracks and holes, were investigated in this study. In the active sensing approach, one piezoceramic transducer is used as an actuator to generate stress waves, which propagate along the surface of the timber structure, and other piezoceramic transducers function as sensors to detect the propagating stress waves. Defects, such as a crack or a hole, induce additional attenuation to the propagating stress wave. Based on this attenuation, the proposed method can detect the defects using the wavelet-packet-based damage index, demonstrating its implementation potential for real-time timber damage detection.

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“…The network building process is conducted without the model updating process (the diagram follows the arrows 2 and 3).Case 2: The proposed method is used to detect damages for a steel beam in the laboratory with different damage scenarios (the diagram follows the arrow 1 and 3). To convert the damaged properties (𝐷) of the beams from measurements into output data of the network, the following equations are applied[15]. 𝐷 = (1 −…”

mentioning

confidence: 99%

A Novel Approach to Damage Assessment in Structures Based on Artificial Neural Network Working Parallel With a Hybrid Stochastic Optimization

Tran-Ngoc

Khatir

Le-Xuan

et al. 2021

Preprint

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Artificial neural network (ANN) is the study of computer algorithms that can learn from experience to improve performance. ANN employs backpropagation (BP) algorithms using gradient descent (GD)-based learning methods to reduce the discrepancies between predicted and real targets. Even though these differences are considerably decreased after each iteration, the network may still face major risks of being entrapped in local minima if complex error surfaces contain too numerous the best local solutions. To overcome those drawbacks of ANN, numerous researchers have come up with solutions to local minimum prevention by choosing a beneficial starting position that relies on the global search capability of other algorithms. This strategy possibly assists the network in avoiding the first local minima. However, a network often has many local bests widely distributed. Hence, the solution of choosing good starting points may no further be beneficial because the particles are probably entrapped in other local optimal solutions throughout the process of looking for the global best. Therefore, in this work, a novel ANN working parallel with the stochastic search capacity of evolutionary algorithms, is proposed. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is applied during the process of seeking the best solution, which effectively guarantees to assist the network of ANN in escaping from local minima. This strategy gains both benefits of GD techniques as well as the global search capacity of PSOGA that possibly solves the local minima issues thoroughly. The effectiveness of ANNPSOGA is assessed using both numerical models consisting of various damage cases (single and multiple damages) and a free-free steel beam with different damage levels calibrated in the laboratory. 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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An experimental study on damage detection of structures using a timber beam

Cited by 17 publications

References 4 publications

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

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

A Feasibility Study on Timber Damage Detection Using Piezoceramic-Transducer-Enabled Active Sensing

A Novel Approach to Damage Assessment in Structures Based on Artificial Neural Network Working Parallel With a Hybrid Stochastic Optimization

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