Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference

Ding, Zhenghao; Li, Jun; Hao, Hong

doi:10.1016/j.ymssp.2019.06.029

Cited by 94 publications

(39 citation statements)

References 49 publications

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“…Aiming to this hardship, Bayesian theory has been introduced in the field of SHM. For example, Ding et al [15] adopted Bayesian inference and sparse regularization technology to modify the traditional objective function of damage identification; the numerical example and experimental studies show that the robustness is enhanced under the condition of significant uncertainty effect. Hou et al [16] developed the spare Bayesian learning framework to simultaneously consider the uncertainties and varying temperature conditions; an experimental frame study demonstrated that the method is effective in locating and quantifying structural damage.…”

Section: Introductionmentioning

confidence: 99%

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Lei

Zhou

et al. 2021

Shock and Vibration

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The bearing of a bridge, as a critical component, is important in the force transformation of the superstructure; however, due to the service condition and repeated impact load, the bearing is prone to be damaged but difficult to detect the damage; the present research has few studies that focused on the damage detection of the structural bearing. Meanwhile, practical engineering is always surrounded by variational environmental conditions, and sometimes, the element and bearing damage both exist in the structure. Thus, these uncertain conditions all cause inaccurate damage identification results using the vibration-based damage detection method. In order to detect the damage of the structural bearing and improve the precision, firstly, the structural dynamic characteristic equation considering uncertain conditions has been deduced; then, a damage detection framework constructed by the Bayesian theory and perturbation method has been developed in this article; a numerical example of an 8-span concrete continuous beam and a practical example of I-40 steel-concrete composite bridge are utilized to validate the feasibility of the proposed method, and single type and two types of damage cases are studied. The outcomes demonstrate that the damage of structural elements and bearings can be detected with high accuracy. The proposed method is of great applicability and good potential.

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Section: Introductionmentioning

confidence: 99%

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Lei

Zhou

et al. 2021

Shock and Vibration

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“…Ding et al adopted tree seeds algorithm to identify structural damages with uncertainties based on frequency domain data. Later, Ding et al used Jaya algorithm to conduct damage identification, in which the objective function is modified by using sparse regularization technique and Bayesian inference. Kang et al proposed an improved particle swarm optimizer to identify structural parameters with and without considering noise contamination in the measurement data.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification by sparse deep belief network using uncertain and limited data

Ding

Hao

2020

Struct Control Health Monit

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Summary The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan‐based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.

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“…Also, GA tends to be trapped into local optimal, which causes the low inaccuracy of damage identification. Aiming to these drawbacks, some other optimization tools, such as Particle Swarm Optimization (PSO) [ 18 ], Cuckoo Search (CS) [ 19 ], Jaya algorithm [ 20 ], Artificial Fish Swarm Optimization (AFSWO) [ 21 ], and Artificial Bee Colony algorithm [ 22 ], were adopted in previous studies as well. Furthermore, based on the hybrid mechanism of different algorithms, better optimization performance can be achieved.…”

Section: Introductionmentioning

confidence: 99%

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization

Huang

Lei

2020

Sensors

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This article proposes a novel damage detection method based on the sensitivity analysis and chaotic moth-flame-invasive weed optimization (CMF-IWO), which is utilized to simultaneously identify the damage of structural elements and bearings. First, the sensitivity coefficients of eigenvalues to the damage factors of structural elements and bearings are deduced, the regularization technology is used to solve the problem of equation undetermined, meanwhile, the modal strain energy-based index is utilized to detect the damage locations, and the regularization objective function is constructed to quantify the damage severity. Then, for the subsequent procedure of damage detection, CMF-IWO is proposed based on moth-flame optimization and invasive weed optimization as well as chaos theory, reverse learning, and evolutional strategy. The optimization effectiveness of the hybrid algorithm is verified by five benchmark functions and a damage identification numerical example of a simply supported beam; the results demonstrate it is of great global search ability and higher convergence efficiency. After that, a numerical example of an 8-span continuous beam and an experimental reinforced concrete plate are both adopted to evaluate the proposed damage identification method. The results of the numerical example indicate that the proposed method can locate and quantify the damage of structural elements and bearings with high accuracy. Furthermore, the outcomes of the experimental example show that despite the existence of some errors and uncertain factors, the method still obtains an acceptable result. Generally speaking, the proposed method is proved that it is of good feasibility.

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Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference

Cited by 94 publications

References 49 publications

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

Structural damage identification by sparse deep belief network using uncertain and limited data

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization

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