Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Li, Xinke; Guo, Yongcai; Li, Yongming

doi:10.1109/access.2019.2961755

Cited by 31 publications

(9 citation statements)

References 33 publications

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“…A framework was developed for data-driven structural diagnosis and damage detection using SVM by Pan et al (2018). PSO-SVM classification model was employed to automatically identify longitudinal crack, transverse crack, surface corrosion, and pothole defect by Li et al (2020). The bridge deck's deformation is mainly the bending deformation under moving vehicle loads, and the bending strains can be easily measured with less error using strain sensors.…”

Section: Introductionmentioning

confidence: 99%

Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

Ren

Zhang

Zhu

et al. 2022

Advances in Structural Engineering

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A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.

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Section: Introductionmentioning

confidence: 99%

Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

Ren

Zhang

Zhu

et al. 2022

Advances in Structural Engineering

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“…Finally, the classification of real surface fault photos of bridge cables was implemented using our PSO-SVM classification model. The PSO-SVM model improved the classification performance of surface defects, according to the experimental data [138].…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

et al. 2022

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In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are making big changes to the way traditional structural health monitoring (SHM) is done. Also, the capacity, flexibility, and robustness of artificial intelligence (AI) techniques for solving complex real-world problems have led to an increasing interest in applying these methods to SHM systems of infrastructures in recent years. Therefore, an analytical evaluation of recent advancements in SHM for infrastructures appears to be important. The bridge is one of the significant transportation infrastructures where existing environmental and destructive variables can have a negative impact on the structure's life and health. The SHM system for bridges in different stages of their life cycle, such as construction, development, management, and maintenance, is seen as a complementary part of intelligent transportation systems (ITS). The main goal of this study is to look at how AI can be used to improve the current state of the art in data-driven SHM systems for bridges, including conceptual frameworks, advantages, and challenges, as well as existing approaches. This article presents an overview of the role of AI in data-driven SHM systems for bridges in the future. Finally, some potential research possibilities in AI-assisted SHM are also emphasized and detailed.

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“…The results showed that the proposed approach had higher classification accuracy than other existing algorithms. Li et al [19] used PSO to optimize the penalty factor and kernel function of SVM, and then used the PSO-SVM algorithm to classify bridge cable pictures, which has a good application in the detection of bridge cables.…”

Section: Related Workmentioning

confidence: 99%

Network Intrusion Detection Based on PSO-Xgboost Model

Jiang

et al. 2020

IEEE Access

156

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Network intrusion detection system (NIDS) is a commonly used tool to detect attacks and protect networks, while one of its general limitations is the false positive issue. On the basis of our comparative experiments and analysis for the characteristics of the particle swarm optimization (PSO) and Xgboost, this paper proposes the PSO-Xgboost model given its overall higher classification accuracy than other alternative models such like Xgboost, Random Forest, Bagging and Adaboost. Firstly, a classification model based on Xgboost is constructed, and then PSO is used to adaptively search for the optimal structure of Xgboost. The benchmark NSL-KDD dataset is used to evaluate the proposed model. Our experimental results demonstrate that PSO-Xgboost model outperforms other comparative models in precision, recall, macro-average (macro) and mean average precision (mAP), especially when identifying minority groups of attacks like U2R and R2L. This work also provides experimental arguments for the application of swarm intelligence in NIDS.

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Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Cited by 31 publications

References 33 publications

Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Network Intrusion Detection Based on PSO-Xgboost Model

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