Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine
To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/11/2730 |
| _version_ | 1797459921721098240 |
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| author | Liming Jiang Qizhi Tang Yan Jiang Huaisong Cao Zhe Xu |
| author_facet | Liming Jiang Qizhi Tang Yan Jiang Huaisong Cao Zhe Xu |
| author_sort | Liming Jiang |
| collection | DOAJ |
| description | To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model’s exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance. |
| first_indexed | 2024-03-09T16:57:50Z |
| format | Article |
| id | doaj.art-9475f1caf2624d47b80925b1266f5459 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T16:57:50Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-9475f1caf2624d47b80925b1266f54592023-11-24T14:33:09ZengMDPI AGBuildings2075-53092023-10-011311273010.3390/buildings13112730Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning MachineLiming Jiang0Qizhi Tang1Yan Jiang2Huaisong Cao3Zhe Xu4State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaState Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaChongqing Yuhe Expressway Co., Ltd., Chongqing 400799, ChinaGuangxi Nanbai Expressway Co., Ltd., Nanning 530029, ChinaTo address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model’s exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.https://www.mdpi.com/2075-5309/13/11/2730bridge engineeringinspection datadeterioration condition predictionwhale optimization algorithmextreme learning |
| spellingShingle | Liming Jiang Qizhi Tang Yan Jiang Huaisong Cao Zhe Xu Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine Buildings bridge engineering inspection data deterioration condition prediction whale optimization algorithm extreme learning |
| title | Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine |
| title_full | Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine |
| title_fullStr | Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine |
| title_full_unstemmed | Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine |
| title_short | Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine |
| title_sort | bridge condition deterioration prediction using the whale optimization algorithm and extreme learning machine |
| topic | bridge engineering inspection data deterioration condition prediction whale optimization algorithm extreme learning |
| url | https://www.mdpi.com/2075-5309/13/11/2730 |
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