{"title":"基于博尔顿失效准则的砂上条形基脚承载力评估混合人工神经网络模型","authors":"","doi":"10.1016/j.trgeo.2024.101347","DOIUrl":null,"url":null,"abstract":"<div><p>This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the <em>b</em> parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (<em>q<sub>u</sub></em>) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (<em>D<sub>R</sub></em>) significantly affects the variation in the bearing capacity factor (<em>N</em><sub>γ</sub>) at higher <em>Q</em> values, while lower <em>Q</em> values inhibit dilatancy due to soil crushing. The width of the strip footing (<em>B</em>) has a decreasing effect on <em>N</em><sub>γ</sub> at higher <em>Q</em> values, and the unit weight (<em>γ</em>) changes minimally impact <em>N</em><sub>γ</sub> within the range of 16–22 kN/m<sup>3</sup>. Additionally, an increase in the critical state friction angle (<em>ϕ<sub>cv</sub></em>) consistently increases <em>N</em><sub>γ</sub>, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (<em>Q)</em> as the most significant factor influencing the bearing capacity factor (<em>N</em><sub>γ</sub>).</p></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion\",\"authors\":\"\",\"doi\":\"10.1016/j.trgeo.2024.101347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the <em>b</em> parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (<em>q<sub>u</sub></em>) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (<em>D<sub>R</sub></em>) significantly affects the variation in the bearing capacity factor (<em>N</em><sub>γ</sub>) at higher <em>Q</em> values, while lower <em>Q</em> values inhibit dilatancy due to soil crushing. The width of the strip footing (<em>B</em>) has a decreasing effect on <em>N</em><sub>γ</sub> at higher <em>Q</em> values, and the unit weight (<em>γ</em>) changes minimally impact <em>N</em><sub>γ</sub> within the range of 16–22 kN/m<sup>3</sup>. Additionally, an increase in the critical state friction angle (<em>ϕ<sub>cv</sub></em>) consistently increases <em>N</em><sub>γ</sub>, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (<em>Q)</em> as the most significant factor influencing the bearing capacity factor (<em>N</em><sub>γ</sub>).</p></div>\",\"PeriodicalId\":56013,\"journal\":{\"name\":\"Transportation Geotechnics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-01\",\"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/S2214391224001685\",\"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/S2214391224001685","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}
引用次数: 0
摘要
本文采用博尔顿破坏准则,结合强度-膨胀关系,分析了密砂上条形基脚的承载力系数。研究利用基于下界和上界定理的有限元极限分析 (FELA),以平均边界解的形式呈现结果。通过使用 Bolton 模型,首先对 b 参数进行了校准,发现 b 参数应为 3.50 左右,以使有限元极限分析得出的极限承载力(qu)与之前研究的实验测试结果保持良好一致。分析了与博尔顿破坏标准相关的参数的影响,结果表明,在 Q 值较高时,相对密度(DR)的增加会显著影响承载力系数(Nγ)的变化,而 Q 值较低时,则会抑制土壤破碎引起的膨胀。在 Q 值较高时,条形基脚宽度 (B) 对 Nγ 的影响逐渐减小,在 16-22 kN/m3 范围内,单位重量 (γ)的变化对 Nγ 的影响很小。此外,临界状态摩擦角 (ϕcv) 的增加会持续增加 Nγ,突出了其与土壤抗剪强度的直接相关性。混合人工神经网络(ANN)模型集成了机器学习和四种优化算法:帝国主义竞争算法(ICA)、蚁狮优化(ALO)、基于教学学习的优化(TLBO)和具有跳跃时变加速度系数的新自组织分层粒子群优化器(NHPSO-JTVAC)。基于最佳隐神经元数量选择的混合 ANN 模型的等级比较分析表明,ANN-TLBO 模型在预测承载能力系数方面表现出色,得分高达 48 分。误差热图矩阵证实了这一结论,该矩阵显示,与其他混合 ANN 模型相比,该模型的误差百分比最小。重要性分析表明,颗粒破碎强度(Q)是影响承载力系数(Nγ)的最重要因素。
Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion
This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (qu) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (DR) significantly affects the variation in the bearing capacity factor (Nγ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on Nγ at higher Q values, and the unit weight (γ) changes minimally impact Nγ within the range of 16–22 kN/m3. Additionally, an increase in the critical state friction angle (ϕcv) consistently increases Nγ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (Nγ).
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.