Damage Identification of Bridge Structures Considering Temperature Variations-Based SVM and MFO

Huang, Minshui; Lei, Yongzhi; Li, Xifan; Gu, Jimmy

doi:10.1061/(asce)as.1943-5525.0001225

Cited by 39 publications

(25 citation statements)

References 40 publications

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“…In addition, the environmental temperature variations have also been a serious impact on a civil structure, which can cause the fluctuation of the measured modal parameters [31,32]. In this paper, the temperature variation is transformed into the reduction of Young's modulus, which can be calculated as follows [33]:…”

Section: Structural Dynamic Characteristic Equation Consideringmentioning

confidence: 99%

“…Referring to the report of the modal experiment and collected modal characteristics, the finite element model has been programmed using MATLAB [22,32,42], which is described in Figure 17. In this model, the concrete deck and the web of the plate girder are both simulated by the 4-node shell element, and other components, such as the flanges of In addition, the detailed material properties can be referred to the paper [42].…”

Section: E Introduction Of the Examplementioning

confidence: 99%

See 1 more Smart Citation

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: Structural Dynamic Characteristic Equation Consideringmentioning

confidence: 99%

Section: E Introduction Of the Examplementioning

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

Self Cite

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“…In recent years, utilizing meta-heuristics emerged as an efficient mechanism to improve the prediction accuracies of machine learning models through their iterative training, such as using support vector machines and a moth-flame optimizer to detect damages in bridges [26], using an adaptive neuro-fuzzy inference system and a particle swarm optimizer to predict monthly river streamflow [27], and using an artificial neural network and a particle swarm optimizer to estimate landslide susceptibility mapping [28]. Hence, the developed deterioration model relies on the accommodation of Gaussian process regression and the grey wolf optimization algorithm to forecast the future deterioration pattern of tunnel elements.…”

Section: Research Frameworkmentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

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“…Real civil constructions are susceptible to changing environmental and operational conditions like temperature. These topics have always been under investigation and continuous development of structural health monitoring in light of environmental changes and have become a popular research subject in the scope of civil engineering [14,15]. Wang et al [16] examined the influences of ambient temperature changes on reinforced concrete (RC) slabs' modal properties.…”

Section: Introductionmentioning

confidence: 99%

The Coupled Effect of Temperature Changes and Damage Depth on Natural Frequencies in Beam-Like Structures

Al-hababi¹,

Alkayem²,

Cui³

et al. 2022

Structural Durability &Amp; Health Monitoring

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A significant amount of research is concerned with dynamic modal parameters for damage detection of structural conditions due to their simplicity in use and feasibility. However, their use for damage detection should be performed with special attention, particularly in operational and environmental conditions subjected to temperature changes. Beams in construction industries experience different loading types, such as temperature changes leading to crack initiation and propagation. Changed physical and dynamic properties such as natural frequencies and mode shapes indicate that damage has occurred within the structures. In this study, vibration analysis of cantilever and cantilever simply supported beams has been carried out on intact and damaged beams to investigate the coupled effect of temperature changes and damage depth on natural frequencies. A numerical analysis of beams is completed using ANSYS software. The results of numerical simulation are validated using two other studies from literature. Numerical results revealed that in order to perform a successful damage assessment using the frequency shift, the vibration modes should be selected properly. In addition, an increase in temperature results in a decrease in structural frequencies. The assessment of the effect of damage depth on natural frequencies also confirms that when damage depth is increased, there is a significant decrease in natural frequency responses.

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Damage Identification of Bridge Structures Considering Temperature Variations-Based SVM and MFO

Cited by 39 publications

References 40 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

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

The Coupled Effect of Temperature Changes and Damage Depth on Natural Frequencies in Beam-Like Structures

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