A. T. Tanga, G. L. Silva Araújo, F. Evangelista Junior
{"title":"A comparison between geomembrane-sand tests and machine learning predictions","authors":"A. T. Tanga, G. L. Silva Araújo, F. Evangelista Junior","doi":"10.1680/jgein.23.00016","DOIUrl":null,"url":null,"abstract":"The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary depending on the type and properties of both the geosynthetic and the soil. This paper introduces a machine learning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with fourteen influencing parameters recorded for each interface, influencing the shear strength outcome. In the analysis, Pearson's correlation coefficient is employed to measure the linear interdependence between each pair of input-input and input-output variables. Following the linear regression analysis, an optimized random forest is utilized to project the interface friction angle. The random forest algorithm divides the selected data into training and testing sets, and only 3% of the training set and 6% of the testing set exceed ±5° from the actual records. The coefficient of determination (R2) indicates strong agreement between the predicted and laboratory study friction angles, with R2 = 0.93 for the training set and R2 = 0.92 for the testing set. Consequently, the random forest algorithm demonstrates effectiveness in predicting interface friction angles.","PeriodicalId":12616,"journal":{"name":"Geosynthetics International","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geosynthetics International","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1680/jgein.23.00016","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary depending on the type and properties of both the geosynthetic and the soil. This paper introduces a machine learning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with fourteen influencing parameters recorded for each interface, influencing the shear strength outcome. In the analysis, Pearson's correlation coefficient is employed to measure the linear interdependence between each pair of input-input and input-output variables. Following the linear regression analysis, an optimized random forest is utilized to project the interface friction angle. The random forest algorithm divides the selected data into training and testing sets, and only 3% of the training set and 6% of the testing set exceed ±5° from the actual records. The coefficient of determination (R2) indicates strong agreement between the predicted and laboratory study friction angles, with R2 = 0.93 for the training set and R2 = 0.92 for the testing set. Consequently, the random forest algorithm demonstrates effectiveness in predicting interface friction angles.
期刊介绍:
An online only, rapid publication journal, Geosynthetics International – an official journal of the International Geosynthetics Society (IGS) – publishes the best information on current geosynthetics technology in research, design innovation, new materials and construction practice.
Topics covered
The whole of geosynthetic materials (including natural fibre products) such as research, behaviour, performance analysis, testing, design, construction methods, case histories and field experience. Geosynthetics International is received by all members of the IGS as part of their membership, and is published in e-only format six times a year.