{"title":"Natural soils’ shear strength prediction: A morphological data-centric approach","authors":"","doi":"10.1016/j.sandf.2024.101527","DOIUrl":null,"url":null,"abstract":"<div><div>The deformation characteristics and constitutive behavior of granular materials under normal forces acting on particles are dependent on the geometry of the grain structure, fabrics and the inter-particle friction. In this study, the influence of particle morphology on the friction and dilatancy of five natural sands was investigated using deep learning (DL) techniques. A Three-dimensional (3D) imaging technique using computed tomography was utilized to compute the morphology (roundness and sphericity) of collected natural sands. Triaxial tests were conducted on the five different natural sands at different densities and confinement stresses (<em>σ<sub>3</sub></em>). From the triaxial results, peak friction angle (<span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub><mrow><mo>)</mo></mrow></mrow></math></span>, critical state friction angle (<span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span>), and dilatancy angle (ψ) were obtained and modeled using conventional multiple linear regression (MLR) models and DL techniques. A total of 100 deep artificial neural networks (DANN) models were trained at different sizes of first and second hidden layers. The use of MLR resulted in R<sup>2</sup> of 0.709, 0.565, and 0.795 for <span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span> and <em>ψ</em>, respectively, while the best performed DANN (30 and 50 neurons for the 1st and 2nd hidden layers, respectively) had R<sup>2</sup> of 0.956 for all outputs (<span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span> and <em>ψ</em>) combined. Using the best-performed DANN model, the weight partitioning technique was used to compute an importance score for each parameter in predicting <span><math><mrow><msub><mi>φ</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>φ</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub></mrow></math></span> and <em>ψ</em>. The <em>σ<sub>3</sub></em> had the highest importance followed by relative density, roundness, and sphericity with a relative importance of more than 10%. In addition, sensitivity analysis was conducted to investigate the effect of each parameter on the shear parameters and ensure the robustness of the developed model.</div></div>","PeriodicalId":21857,"journal":{"name":"Soils and Foundations","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soils and Foundations","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038080624001057","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 deformation characteristics and constitutive behavior of granular materials under normal forces acting on particles are dependent on the geometry of the grain structure, fabrics and the inter-particle friction. In this study, the influence of particle morphology on the friction and dilatancy of five natural sands was investigated using deep learning (DL) techniques. A Three-dimensional (3D) imaging technique using computed tomography was utilized to compute the morphology (roundness and sphericity) of collected natural sands. Triaxial tests were conducted on the five different natural sands at different densities and confinement stresses (σ3). From the triaxial results, peak friction angle (, critical state friction angle (), and dilatancy angle (ψ) were obtained and modeled using conventional multiple linear regression (MLR) models and DL techniques. A total of 100 deep artificial neural networks (DANN) models were trained at different sizes of first and second hidden layers. The use of MLR resulted in R2 of 0.709, 0.565, and 0.795 for , and ψ, respectively, while the best performed DANN (30 and 50 neurons for the 1st and 2nd hidden layers, respectively) had R2 of 0.956 for all outputs (, and ψ) combined. Using the best-performed DANN model, the weight partitioning technique was used to compute an importance score for each parameter in predicting , and ψ. The σ3 had the highest importance followed by relative density, roundness, and sphericity with a relative importance of more than 10%. In addition, sensitivity analysis was conducted to investigate the effect of each parameter on the shear parameters and ensure the robustness of the developed model.
期刊介绍:
Soils and Foundations is one of the leading journals in the field of soil mechanics and geotechnical engineering. It is the official journal of the Japanese Geotechnical Society (JGS)., The journal publishes a variety of original research paper, technical reports, technical notes, as well as the state-of-the-art reports upon invitation by the Editor, in the fields of soil and rock mechanics, geotechnical engineering, and environmental geotechnics. Since the publication of Volume 1, No.1 issue in June 1960, Soils and Foundations will celebrate the 60th anniversary in the year of 2020.
Soils and Foundations welcomes theoretical as well as practical work associated with the aforementioned field(s). Case studies that describe the original and interdisciplinary work applicable to geotechnical engineering are particularly encouraged. Discussions to each of the published articles are also welcomed in order to provide an avenue in which opinions of peers may be fed back or exchanged. In providing latest expertise on a specific topic, one issue out of six per year on average was allocated to include selected papers from the International Symposia which were held in Japan as well as overseas.