A Rapid Machine Learning-Based Damage Detection Technique for Detecting Local Damages in Reinforced Concrete Bridges

Salkhordeh, Mojtaba; Mirtaheri, Masoud; Rabiee, Najib; Govahi, Ehsan; Soroushian, Siavash

doi:10.1080/13632469.2023.2193277

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…In the United States, some Departments of Transportation use methodological frameworks to quantify the criticality of each asset and identify bridges whose intervention should be prioritized [ 1 ]. The occurrence of bridge failures or the need to diagnose critical assets in the aftermath of natural events, such as earthquakes, warrants the use of complementary approaches to supply timely information about any given bridge [ 2 ]. One element of concern is fatigue, especially in those bridges that typically have high live load to dead load ratios and high stress cycle frequencies [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Enshaeian,

Ghahremani,

Rizzo

2024

Sensors

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Three Pennsylvanian bridges were studied using finite element and wireless sensor technology. A detailed finite element model of each bridge was created using a commercial software in order to calculate the strains generated by a load that simulates the presence of a standard truck. Pristine and damage scenarios were simulated, and the computed strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test performed by companies not involved in the study presented in this article. The comparison demonstrated the accuracy of the model and the presence of a few non-critical anomalies in terms of load redistribution. In addition, the comparison proved the reliability of the wireless sensing system installed on the bridges, although some drift was observed. The structural monitoring program for the three bridges was also evaluated by processing more than two years of data streamed to a repository.

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Section: Introductionmentioning

confidence: 99%

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Enshaeian,

Ghahremani,

Rizzo

2024

Sensors

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“…Jeong et al [25] and Li et al [26] conducted a number of shaking table tests on PT rocking piers, further demonstrating their enhanced seismic performance and self-centering capacity during earthquakes. To enhance the precision of damage detection capabilities, Salkhordeh et al [27] proposed a machine learning-based framework to detect damage, specifically accounting for the impact of residual drift in reinforced concrete bridges.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of the Seismic-Induced Rocking Behavior of Unbonded Post-Tensioned Bridge Piers

Bao,

Xu,

Gao

et al. 2024

Buildings

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It is essential and convenient to use accurate and validated numerical models to simulate the seismic performance of post-tensioned (PT) rocking bridge piers, with a particular emphasis on accurately capturing rocking behavior. The primary contribution of this study is a comparison of the effectiveness of four commonly used numerical base rocking models (namely, the lumped plasticity (LP) model and the multi-contact spring (MCS) models with linear elastic (MCS-LE), bilinear elastic–plastic (MCS-EP) and nonlinear plastic (MCS-NP) material behavior, respectively) in modeling both the cyclic and seismic responses of PT rocking bridge piers. Also, this study validates the 3D contact stiffness equation for numerical models and assesses the differences between the dynamic and static stiffness values of the contact springs. Both quasi-static and shaking table tests of typical PT rocking piers are adopted to calibrate/validate these numerical models. These models describing the PT rocking piers’ seismic performance are formulated and calibrated, showing good agreement with test results for test specimens. Additionally, the suggested values of model spring stiffness for dynamic and quasi-static analyses are identified by parametric analysis. All base rocking models can predict the pier’s cyclic and seismic behavior after the calibration of contact spring stiffness values. The recommended contact stiffness for the dynamic analysis of PT rocking piers is smaller than that used for the quasi-static analysis. The results and findings provide a valuable reference and solution for the numerical simulation of PT rocking piers.

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“…Furthermore, they combined SVM models with wavelet analysis and a PSO algorithm [11][12][13]. For example, Salkhordeh et al [14] used SVM for detecting damage in concrete bridges. Ren et al [15] applied SVM to the time-dependent prediction of dams and achieved better results.…”

Section: Introductionmentioning

confidence: 99%

A Model Predicting the Maximum Face Slab Deflection of Concrete-Face Rockfill Dams: Combining Improved Support Vector Machine and Threshold Regression

Zhao,

Wang,

Zhang

et al. 2023

Water

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The deformation of concrete-face rockfill dams (CFRDs) is a key parameter for the safety control of reservoir and dam systems. Rapid and accurate estimation of the deformation characteristics of CFRDs is a top priority. To realize this, we proposed a new model for predicting the maximum face slab deflection (FD) of CFRDs, combining the threshold regression (TR) and the improved support vector machine (SVM). In this paper, based on the collected 71 real measurement data from engineering examples, we constructed an adaptive hybrid kernel function with high precision and generalization ability. We optimized the selection of the main parameters of the SVM by a particle swarm optimization (PSO) algorithm. Meanwhile, we clustered the deformation parameters according to the dam height by the TR. It significantly contributes to the accuracy and generalization of the model. Finally, a prediction model for the FD characteristics of CFRDs combining TR and improved SVM was developed. The new prediction model can overcome the nonlinear abrupt feature of the sample data and achieve high precision with R2 greater than 0.8 in the final testing set. Our model is more accurate with faster convergence compared to the previous model. This study provides a more accurate model for predicting maximum face slab deflection and lays the foundation for safety control and evaluation of dams.

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A Rapid Machine Learning-Based Damage Detection Technique for Detecting Local Damages in Reinforced Concrete Bridges

Cited by 12 publications

References 39 publications

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Numerical Investigation of the Seismic-Induced Rocking Behavior of Unbonded Post-Tensioned Bridge Piers

A Model Predicting the Maximum Face Slab Deflection of Concrete-Face Rockfill Dams: Combining Improved Support Vector Machine and Threshold Regression

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