Xiangyang Li , Wenjun Liu , Changjing Xu , Ning Liu , Shuaike Feng , Xin Zhang , Yanbin Li , Jianwen Hao
{"title":"基于机器学习的路基土动态弹性模量可解释预测模型","authors":"Xiangyang Li , Wenjun Liu , Changjing Xu , Ning Liu , Shuaike Feng , Xin Zhang , Yanbin Li , Jianwen Hao","doi":"10.1016/j.trgeo.2024.101415","DOIUrl":null,"url":null,"abstract":"<div><div>The dynamic resilient modulus (<em>M<sub>R</sub></em>) of a subgrade soil is a fundamental parameter for evaluating the dynamic stability and service resilience of subgrade fillers and structures, as well as an instrumental input for calculating the mechanical response and fatigue life of a pavement structure. To accurately and reasonably characterise the <em>M<sub>R</sub></em> of subgrade soils, machine learning (ML) models were established using the support vector machine, random forest, and extreme gradient boosting algorithms based on a large-scale dataset including 3533 records of <em>M<sub>R</sub></em> tests conducted on subgrade soils. Meanwhile, the weighted<!--> <!-->plasticity index (WPI), initial moisture content (<em>w</em>), dry unit weight (γ<em><sub>d</sub></em>), confining stress (<em>σ</em><sub>c</sub>), deviator stress (<em>σ</em><sub>d</sub>), and numbers of freeze–thaw cycles (<em>N<sub>FT</sub></em>) were set as the input variables to predict the <em>M<sub>R</sub></em> using ML models, which considered the effects of wheel loads, physical properties and climate fluctuation on the subgrade soils during the service period. Subsequently, the Shapley additive explanations method was developed to explain the prediction model for the <em>M<sub>R</sub></em> of subgrade soils based on ML algorithms. The results quantitatively illustrated the explicit mapping relationship and internal influencing mechanism between the significant features of the influences and <em>M<sub>R</sub></em> of subgrade soils, which was consistent with prior experimental and physical cognition. In summary, the study findings provide meaningful guidelines for the structural design and life evaluation of pavement subgrade engineering.</div></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":"49 ","pages":"Article 101415"},"PeriodicalIF":4.9000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Explainable machine learning-based prediction model for dynamic resilient modulus of subgrade soils\",\"authors\":\"Xiangyang Li , Wenjun Liu , Changjing Xu , Ning Liu , Shuaike Feng , Xin Zhang , Yanbin Li , Jianwen Hao\",\"doi\":\"10.1016/j.trgeo.2024.101415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The dynamic resilient modulus (<em>M<sub>R</sub></em>) of a subgrade soil is a fundamental parameter for evaluating the dynamic stability and service resilience of subgrade fillers and structures, as well as an instrumental input for calculating the mechanical response and fatigue life of a pavement structure. To accurately and reasonably characterise the <em>M<sub>R</sub></em> of subgrade soils, machine learning (ML) models were established using the support vector machine, random forest, and extreme gradient boosting algorithms based on a large-scale dataset including 3533 records of <em>M<sub>R</sub></em> tests conducted on subgrade soils. Meanwhile, the weighted<!--> <!-->plasticity index (WPI), initial moisture content (<em>w</em>), dry unit weight (γ<em><sub>d</sub></em>), confining stress (<em>σ</em><sub>c</sub>), deviator stress (<em>σ</em><sub>d</sub>), and numbers of freeze–thaw cycles (<em>N<sub>FT</sub></em>) were set as the input variables to predict the <em>M<sub>R</sub></em> using ML models, which considered the effects of wheel loads, physical properties and climate fluctuation on the subgrade soils during the service period. Subsequently, the Shapley additive explanations method was developed to explain the prediction model for the <em>M<sub>R</sub></em> of subgrade soils based on ML algorithms. The results quantitatively illustrated the explicit mapping relationship and internal influencing mechanism between the significant features of the influences and <em>M<sub>R</sub></em> of subgrade soils, which was consistent with prior experimental and physical cognition. In summary, the study findings provide meaningful guidelines for the structural design and life evaluation of pavement subgrade engineering.</div></div>\",\"PeriodicalId\":56013,\"journal\":{\"name\":\"Transportation Geotechnics\",\"volume\":\"49 \",\"pages\":\"Article 101415\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214391224002368\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214391224002368","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Explainable machine learning-based prediction model for dynamic resilient modulus of subgrade soils
The dynamic resilient modulus (MR) of a subgrade soil is a fundamental parameter for evaluating the dynamic stability and service resilience of subgrade fillers and structures, as well as an instrumental input for calculating the mechanical response and fatigue life of a pavement structure. To accurately and reasonably characterise the MR of subgrade soils, machine learning (ML) models were established using the support vector machine, random forest, and extreme gradient boosting algorithms based on a large-scale dataset including 3533 records of MR tests conducted on subgrade soils. Meanwhile, the weighted plasticity index (WPI), initial moisture content (w), dry unit weight (γd), confining stress (σc), deviator stress (σd), and numbers of freeze–thaw cycles (NFT) were set as the input variables to predict the MR using ML models, which considered the effects of wheel loads, physical properties and climate fluctuation on the subgrade soils during the service period. Subsequently, the Shapley additive explanations method was developed to explain the prediction model for the MR of subgrade soils based on ML algorithms. The results quantitatively illustrated the explicit mapping relationship and internal influencing mechanism between the significant features of the influences and MR of subgrade soils, which was consistent with prior experimental and physical cognition. In summary, the study findings provide meaningful guidelines for the structural design and life evaluation of pavement subgrade engineering.
期刊介绍:
Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.