Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine
In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretica...
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MDPI AG
2025-02-01
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| author | Yanhua Zhao Kai Zhang Aojun Guo Fukang Hao Jie Ma |
| author_facet | Yanhua Zhao Kai Zhang Aojun Guo Fukang Hao Jie Ma |
| author_sort | Yanhua Zhao |
| collection | DOAJ |
| description | In the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail to predict the erosion rate of concrete (CER) structures accurately. This issue arises from oversimplified assumptions and the failure to account for environmental variations and complex nonlinear relationships between parameters. Consequently, a single traditional model is inadequate for predicting the CER under wind gravel flow conditions in this region. To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R<sup>2</sup> value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. Ensuring that the training set comprises at least 80% of the total data volume is crucial for the SVM model’s prediction accuracy. Moreover, erosion time is identified as the most significant factor affecting the CER. An enhanced theoretical erosion model, derived from the Bitter and Oka framework and integrating concrete strength and erosion parameters, was formulated. It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. The SVM model, however, recorded a minimal average relative error of just −0.5%, markedly surpassing these improved theoretical models in terms of prediction accuracy. Theoretical models often rely on simplifying assumptions, such as linear relationships and homogeneous material properties. In practice, however, factors like concrete materials, wind gravel flow, and climate change are nonlinear and non-homogeneous. This significantly limits the applicability of these models in real-world environments. Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. This model is crucial for safeguarding concrete structures in wind gravel flow environments. |
| format | Article |
| id | doaj-art-0bb4ae466f794aa0a5d7c441f9272e4e |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-0bb4ae466f794aa0a5d7c441f9272e4e2025-08-20T03:12:20ZengMDPI AGBuildings2075-53092025-02-0115461410.3390/buildings15040614Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector MachineYanhua Zhao0Kai Zhang1Aojun Guo2Fukang Hao3Jie Ma4Civil Engineering Department, Lanzhou Jiaotong University, Lanzhou 730070, ChinaCivil Engineering Department, Lanzhou Jiaotong University, Lanzhou 730070, ChinaCivil Engineering Department, Lanzhou Jiaotong University, Lanzhou 730070, ChinaCivil Engineering Department, Lanzhou Jiaotong University, Lanzhou 730070, ChinaCivil Engineering Department, Lanzhou Jiaotong University, Lanzhou 730070, ChinaIn the Gobi region, concrete structures frequently suffer erosion from wind gravel flow. This erosion notably impairs their longevity. Therefore, creating a predictive model for wind gravel flow-related concrete damage is crucial to proactively address and manage this problem. Traditional theoretical models often fail to predict the erosion rate of concrete (CER) structures accurately. This issue arises from oversimplified assumptions and the failure to account for environmental variations and complex nonlinear relationships between parameters. Consequently, a single traditional model is inadequate for predicting the CER under wind gravel flow conditions in this region. To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. The support vector machine (SVM) model demonstrates superior predictive performance, evidenced by its R<sup>2</sup> value nearing one and a notable reduction in RMSE 1.123 and 1.573 less than the long short-term memory network (LSTM) and BP neural network (BPNN) models, respectively. Ensuring that the training set comprises at least 80% of the total data volume is crucial for the SVM model’s prediction accuracy. Moreover, erosion time is identified as the most significant factor affecting the CER. An enhanced theoretical erosion model, derived from the Bitter and Oka framework and integrating concrete strength and erosion parameters, was formulated. It showed average relative errors of 22% and 31.6% for the Bitter and Oka models, respectively. The SVM model, however, recorded a minimal average relative error of just −0.5%, markedly surpassing these improved theoretical models in terms of prediction accuracy. Theoretical models often rely on simplifying assumptions, such as linear relationships and homogeneous material properties. In practice, however, factors like concrete materials, wind gravel flow, and climate change are nonlinear and non-homogeneous. This significantly limits the applicability of these models in real-world environments. Ultimately, the SVM algorithm is highly effective in developing a reliable prediction model for CER. This model is crucial for safeguarding concrete structures in wind gravel flow environments.https://www.mdpi.com/2075-5309/15/4/614wind gravel flowconcretemachine learningsupport vector machineerosion model |
| spellingShingle | Yanhua Zhao Kai Zhang Aojun Guo Fukang Hao Jie Ma Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine Buildings wind gravel flow concrete machine learning support vector machine erosion model |
| title | Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine |
| title_full | Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine |
| title_fullStr | Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine |
| title_full_unstemmed | Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine |
| title_short | Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine |
| title_sort | predictive model for erosion rate of concrete under wind gravel flow based on k fold cross validation combined with support vector machine |
| topic | wind gravel flow concrete machine learning support vector machine erosion model |
| url | https://www.mdpi.com/2075-5309/15/4/614 |
| work_keys_str_mv | AT yanhuazhao predictivemodelforerosionrateofconcreteunderwindgravelflowbasedonkfoldcrossvalidationcombinedwithsupportvectormachine AT kaizhang predictivemodelforerosionrateofconcreteunderwindgravelflowbasedonkfoldcrossvalidationcombinedwithsupportvectormachine AT aojunguo predictivemodelforerosionrateofconcreteunderwindgravelflowbasedonkfoldcrossvalidationcombinedwithsupportvectormachine AT fukanghao predictivemodelforerosionrateofconcreteunderwindgravelflowbasedonkfoldcrossvalidationcombinedwithsupportvectormachine AT jiema predictivemodelforerosionrateofconcreteunderwindgravelflowbasedonkfoldcrossvalidationcombinedwithsupportvectormachine |