Xiyan Fan , Songtao Lv , Chengdong Xia , Dongdong Ge , Chaochao Liu , Weiwei Lu
{"title":"基于 BPNN 和 SVM 的交互条件下的沥青混合料强度预测","authors":"Xiyan Fan , Songtao Lv , Chengdong Xia , Dongdong Ge , Chaochao Liu , Weiwei Lu","doi":"10.1016/j.cscm.2024.e03489","DOIUrl":null,"url":null,"abstract":"<div><p>Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard strength test lacked consideration of multiple factors, resulting in an inability to accurately characterize the properties of the pavement. This paper proposed a strength prediction approach based on influence factor analysis using the back-propagation neural network (BPNN) and support vector machine (SVM). The strength dataset was processed to realize the physical analysis of factors influencing asphalt mixture strength. Stress state (Direct tensile with<span><math><mrow><mn>250</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>50</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>50</mn><mi>m</mi><mi>m</mi></mrow></math></span>, Uniaxial compression with <span><math><mrow><mn>100</mn><mi>m</mi><mi>m</mi><mo>×</mo><mi>Φ</mi><mn>100</mn><mi>m</mi><mi>m</mi></mrow></math></span>, Indirect tensile with<span><math><mrow><mn>63.5</mn><mi>m</mi><mi>m</mi><mo>×</mo><mi>Φ</mi><mn>100</mn><mi>m</mi><mi>m</mi></mrow></math></span>,Four-point bending with <span><math><mrow><mn>380</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>63.5</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>50</mn><mi>m</mi><mi>m</mi></mrow></math></span>), temperatures (35 ̊C, 25 ̊C, 15 ̊C, 0 ̊C, −15 ̊C, −25 ̊C), and load rates (0.02 MPa/s, 0.05 MPa/s, 0.1 MPa/s, 0.5 MPa/s) were selected as input features to train the BPNN and SVM. The strength prediction model for asphalt mixture under complex conditions was established by optimizing the parameters of algorithms. The performance of the BPNN and SVM was evaluated and compared by the root mean square error, determination coefficient, and mean absolute percentage deviation. The results show that the asphalt mixture specimen with different specifications under various stress states presents significant discrepancies. The maximum compressive strength is followed by the bending strength, then comes the indirect tensile strength, and the smallest is the direct tensile strength. The difference in the role of asphalt or aggregate is the main reason for the diversity in strength. The increase in temperature leads to asphalt softening, which reduces the strength of the asphalt mixture. The increased loading rate meant the loading time was short cause the strength increased. In addition, the predictive value of the strength under various conditions was consistent with the results of the experiments. The hidden neurons in the BPNN were set to 9, achieving the prediction accuracy is high (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.99</mn></mrow></math></span>). The penalty coefficient of the SVM was set to 500 and the kernel function parameter was set to 300, resulting in the error within 0.02 %. When comparing the performance metrics of BPNN and SVM, it becomes evident that SVM outperforms BPNN in terms of prediction accuracy. Specifically, SVM exhibits a coefficient of determination of 0.9983, a root mean square error of 0.208, and a mean absolute percentage deviation of 0.145, whereas BPNN demonstrates respective values of 0.9979, 0.233, and 0.067. This study lays a theoretical foundation for the digital and intelligent road construction.</p></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"21 ","pages":"Article e03489"},"PeriodicalIF":6.5000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214509524006405/pdfft?md5=9c4aa13d97178342c754b5202e0dca87&pid=1-s2.0-S2214509524006405-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Strength prediction of asphalt mixture under interactive conditions based on BPNN and SVM\",\"authors\":\"Xiyan Fan , Songtao Lv , Chengdong Xia , Dongdong Ge , Chaochao Liu , Weiwei Lu\",\"doi\":\"10.1016/j.cscm.2024.e03489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard strength test lacked consideration of multiple factors, resulting in an inability to accurately characterize the properties of the pavement. This paper proposed a strength prediction approach based on influence factor analysis using the back-propagation neural network (BPNN) and support vector machine (SVM). The strength dataset was processed to realize the physical analysis of factors influencing asphalt mixture strength. Stress state (Direct tensile with<span><math><mrow><mn>250</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>50</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>50</mn><mi>m</mi><mi>m</mi></mrow></math></span>, Uniaxial compression with <span><math><mrow><mn>100</mn><mi>m</mi><mi>m</mi><mo>×</mo><mi>Φ</mi><mn>100</mn><mi>m</mi><mi>m</mi></mrow></math></span>, Indirect tensile with<span><math><mrow><mn>63.5</mn><mi>m</mi><mi>m</mi><mo>×</mo><mi>Φ</mi><mn>100</mn><mi>m</mi><mi>m</mi></mrow></math></span>,Four-point bending with <span><math><mrow><mn>380</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>63.5</mn><mi>m</mi><mi>m</mi><mo>×</mo><mn>50</mn><mi>m</mi><mi>m</mi></mrow></math></span>), temperatures (35 ̊C, 25 ̊C, 15 ̊C, 0 ̊C, −15 ̊C, −25 ̊C), and load rates (0.02 MPa/s, 0.05 MPa/s, 0.1 MPa/s, 0.5 MPa/s) were selected as input features to train the BPNN and SVM. The strength prediction model for asphalt mixture under complex conditions was established by optimizing the parameters of algorithms. The performance of the BPNN and SVM was evaluated and compared by the root mean square error, determination coefficient, and mean absolute percentage deviation. The results show that the asphalt mixture specimen with different specifications under various stress states presents significant discrepancies. The maximum compressive strength is followed by the bending strength, then comes the indirect tensile strength, and the smallest is the direct tensile strength. The difference in the role of asphalt or aggregate is the main reason for the diversity in strength. The increase in temperature leads to asphalt softening, which reduces the strength of the asphalt mixture. The increased loading rate meant the loading time was short cause the strength increased. In addition, the predictive value of the strength under various conditions was consistent with the results of the experiments. The hidden neurons in the BPNN were set to 9, achieving the prediction accuracy is high (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.99</mn></mrow></math></span>). The penalty coefficient of the SVM was set to 500 and the kernel function parameter was set to 300, resulting in the error within 0.02 %. When comparing the performance metrics of BPNN and SVM, it becomes evident that SVM outperforms BPNN in terms of prediction accuracy. Specifically, SVM exhibits a coefficient of determination of 0.9983, a root mean square error of 0.208, and a mean absolute percentage deviation of 0.145, whereas BPNN demonstrates respective values of 0.9979, 0.233, and 0.067. This study lays a theoretical foundation for the digital and intelligent road construction.</p></div>\",\"PeriodicalId\":9641,\"journal\":{\"name\":\"Case Studies in Construction Materials\",\"volume\":\"21 \",\"pages\":\"Article e03489\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214509524006405/pdfft?md5=9c4aa13d97178342c754b5202e0dca87&pid=1-s2.0-S2214509524006405-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Case Studies in Construction Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214509524006405\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Case Studies in Construction Materials","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214509524006405","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Strength prediction of asphalt mixture under interactive conditions based on BPNN and SVM
Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard strength test lacked consideration of multiple factors, resulting in an inability to accurately characterize the properties of the pavement. This paper proposed a strength prediction approach based on influence factor analysis using the back-propagation neural network (BPNN) and support vector machine (SVM). The strength dataset was processed to realize the physical analysis of factors influencing asphalt mixture strength. Stress state (Direct tensile with, Uniaxial compression with , Indirect tensile with,Four-point bending with ), temperatures (35 ̊C, 25 ̊C, 15 ̊C, 0 ̊C, −15 ̊C, −25 ̊C), and load rates (0.02 MPa/s, 0.05 MPa/s, 0.1 MPa/s, 0.5 MPa/s) were selected as input features to train the BPNN and SVM. The strength prediction model for asphalt mixture under complex conditions was established by optimizing the parameters of algorithms. The performance of the BPNN and SVM was evaluated and compared by the root mean square error, determination coefficient, and mean absolute percentage deviation. The results show that the asphalt mixture specimen with different specifications under various stress states presents significant discrepancies. The maximum compressive strength is followed by the bending strength, then comes the indirect tensile strength, and the smallest is the direct tensile strength. The difference in the role of asphalt or aggregate is the main reason for the diversity in strength. The increase in temperature leads to asphalt softening, which reduces the strength of the asphalt mixture. The increased loading rate meant the loading time was short cause the strength increased. In addition, the predictive value of the strength under various conditions was consistent with the results of the experiments. The hidden neurons in the BPNN were set to 9, achieving the prediction accuracy is high (). The penalty coefficient of the SVM was set to 500 and the kernel function parameter was set to 300, resulting in the error within 0.02 %. When comparing the performance metrics of BPNN and SVM, it becomes evident that SVM outperforms BPNN in terms of prediction accuracy. Specifically, SVM exhibits a coefficient of determination of 0.9983, a root mean square error of 0.208, and a mean absolute percentage deviation of 0.145, whereas BPNN demonstrates respective values of 0.9979, 0.233, and 0.067. This study lays a theoretical foundation for the digital and intelligent road construction.
期刊介绍:
Case Studies in Construction Materials provides a forum for the rapid publication of short, structured Case Studies on construction materials. In addition, the journal also publishes related Short Communications, Full length research article and Comprehensive review papers (by invitation).
The journal will provide an essential compendium of case studies for practicing engineers, designers, researchers and other practitioners who are interested in all aspects construction materials. The journal will publish new and novel case studies, but will also provide a forum for the publication of high quality descriptions of classic construction material problems and solutions.