{"title":"将机器学习融入岩土工程:基于锥入度测试数据的铁路轨道层设计新方法","authors":"Matthieu Bernard","doi":"10.3390/infrastructures9080121","DOIUrl":null,"url":null,"abstract":"The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"56 22","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating Machine Learning in Geotechnical Engineering: A Novel Approach for Railway Track Layer Design Based on Cone Penetration Test Data\",\"authors\":\"Matthieu Bernard\",\"doi\":\"10.3390/infrastructures9080121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":\"56 22\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/infrastructures9080121\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/infrastructures9080121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Integrating Machine Learning in Geotechnical Engineering: A Novel Approach for Railway Track Layer Design Based on Cone Penetration Test Data
The cone penetration test (CPT) has emerged as a cost-effective and time-efficient method for assessing soil conditions relevant to railway track infrastructure. The geotechnical data obtained from the CPT serve as crucial input for asset managers in designing optimal sublayers and form layers for track renewal works. To properly assess the condition of soil layers, various soil behavior type charts and machine learning models based on CPT data have been published to help engineers classify soils into groups with similar properties. By understanding the properties of the soils, an optimal substructure can be designed to minimize extensive maintenance and reduce the risk of derailment. However, when analyzing multiple CPTs, the diversity and non-uniformity of subsoil characteristics pose challenges in designing a new optimal trackbed. This study presents an automated approach for recommending thicknesses of sublayers and form layers in railway tracks based on CPT data, employing machine learning algorithms. The proposed approach was tested using CPT data from the Belgian railway network and showed very good agreement with results from traditional soil investigation interpretations and layer design. A Random Forest classifier, fine-tuned through Bayesian optimization with a cross-validation technique and trained on 80% of the datasets, achieved an overall accuracy of 83% on the remaining 20%. Based on these results, we can conclude that the proposed model is highly effective at accurately designing sub-ballast layers using CPT data.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.