International Journal for Numerical and Analytical Methods in Geomechanics最新文献

{"title":"A Novel Prediction Model for Debris Flow Mean Velocity Based on Small Sample Data Taking Jiangjia Gully Watershed as an Example","authors":"He Wei Kuang, Zhi Yong Ai, Gan Lin Gu","doi":"10.1002/nag.3850","DOIUrl":"https://doi.org/10.1002/nag.3850","url":null,"abstract":"Among all the factors affecting the destructiveness of debris flow, the mean velocity is one of the most important characteristics. In this paper, we aim to apply a particle swarm optimization (PSO) based on the relevance vector machine (RVM) to predict the mean velocity. The PSO is used to optimize kernel parameters inside the RVM, whereas the RVM is responsible for completing the prediction task. Through sample training, a nonlinear relationship can be obtained, enabling a rapid prediction of the mean velocity for new samples. The debris flow dataset of Jiangjia Gully is used to evaluate the performance of PSO‐RVM in this study. Besides, we further compare the prediction results of PSO‐RVM with other prominent approaches, for example, the support vector machine (SVM), BP neural network (BP), and the RVM. The results show that the mean relative error (MRE) of PSO‐RVM is only 0.69%. In addition, BP yields the highest MRE (27.61%), and the MRE (2.75%) corresponding to the RVM is lower than that (5.98%) yielded by the SVM. For the root mean square error (RMSE) and Theil's inequality coefficient (TIC), the PSO‐RVM method still generates much lower RMSE (6.48%) and TIC (0.179%) values than the other three methods. Overall, compared with current debris flow prediction models, the PSO‐RVM achieves high prediction accuracy, fewer optimization parameters, and low computational complexity. Finally, a sensitivity analysis is conducted to explore the dominative factors of debris flow.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models","authors":"Yonas Tilahun, Xiao Qinghua, Argaw Asha Ashongo, Xiangyu Han","doi":"10.1002/nag.3846","DOIUrl":"https://doi.org/10.1002/nag.3846","url":null,"abstract":"This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (<jats:italic>M</jats:italic><jats:sub>DD</jats:sub>) and optimum moisture content (<jats:italic>O</jats:italic><jats:sub>MC</jats:sub>), which are critical for stabilizing construction foundations. Traditional methods for determining <jats:italic>M</jats:italic><jats:sub>DD</jats:sub> and <jats:italic>O</jats:italic><jats:sub>MC</jats:sub> are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy (<jats:italic>E</jats:italic>). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for <jats:italic>M</jats:italic><jats:sub>DD</jats:sub> and <jats:italic>O</jats:italic><jats:sub>MC</jats:sub>, outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that <jats:italic>M</jats:italic><jats:sub>DD</jats:sub> and <jats:italic>O</jats:italic><jats:sub>MC</jats:sub> were most influenced by plastic limit (PL), compaction energy (<jats:italic>E</jats:italic>), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between <jats:italic>M</jats:italic><jats:sub>DD</jats:sub>, <jats:italic>O</jats:italic><jats:sub>MC</jats:sub>, and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Simulation and Experimental Analysis on Wearing Mechanisms of Gypsum and Concrete Samples in Pin‐on‐Disk ASTM Abrasion Testing","authors":"Jinwei Fu, Vahab Sarfarazi, Hadi Haeri, Behzad Tolaminejad, Soheil Abharian, Haleh Rasekh, Manoj Khandelwal, Mohammad Fatehi Marji","doi":"10.1002/nag.3848","DOIUrl":"https://doi.org/10.1002/nag.3848","url":null,"abstract":"Mechanical excavation machines, like continuous miners and road headers, have been broadly used in tunneling and underground and surface mines. The disc cutters are seated on the different cutter heads’ to cut different parts of the tunnel face. With the increase in the cutters’ size and power, the cutting disc cutters’ capacity has been extended to cut moderate and tough rock types. This experimental and numerical research includes the application of, “Pin‐on‐Disk” ASTM abrasion testing, in which the failure mechanism of an interface between both the rock‐like samples and (WC–Co) tungsten carbide has been investigated under different confining pressures. The research aims to investigate the wear mechanism of gypsum and concrete samples. The Particle Flow Code in three dimensions (PFC3D) was used for test simulations concurrently with the experimental setup. A drilling pin with a diameter of 0.4 m was positioned above the model. The pin was inserted into the model at speeds of 0.01 mm/s at depths of 1, 3, and 5 m. A total of nine lab tests were conducted. The tensile strength of the material was 2.5 MPa. The results show that the values of volume lost for the gypsum and concrete discs were detected as a function of sliding length, fitting to non‐linear behavior. The wearing depth increased by increasing the loading force. Under constant loading force, the gypsum sample wears more than the concrete sample because gypsum is less strong than concrete. The PFC generates useful findings that experimental tests cannot provide.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spring Model for Pullout Behavior of Curved, Flexible Structures Embedded in Soil","authors":"Matthew Burrall, Jason T. DeJong, Alejandro Martinez, Tae‐Hyuk Kwon","doi":"10.1002/nag.3845","DOIUrl":"https://doi.org/10.1002/nag.3845","url":null,"abstract":"The shape and flexibility of embedded structures, such as tree roots, piles, and anchors, have important impacts on the pullout behavior. However, the rate and manner of mobilization of soil resistances along such structures has not been rigorously explored across a wide range of shapes and structural properties. A spring model for computing compatible displacements of the structure and soil for curved, flexible structures is defined, validated against commonly used methods for computing pile pullout behavior, and then parametrically explored to demonstrate how resistances are mobilized along the length of such structures. The present model allows description of combined axial and transverse loading of these nonlinear structures. The simulation results for the case of normally consolidated clay show that the curvature of a structure causes the distribution of bearing resistance to extend further along the structure than for linear cases. The requirement of equilibrium of the structure produces a coupling between the mobilized bearing and tensile resistance in terms of rate of development and magnitude. Thus, the choices of structure shape impact the magnitude and distribution of mobilized resistance of embedded flexible structures. Implications for anchorage of tree root structures and principles of bioinspired design of anchorage systems are discussed.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Backward Erosion, Pipe Formation and Induced Failure Using a Multi‐Physics SPH Computational Framework","authors":"Guodong Ma, Ha H. Bui, Yanjian Lian, Tien V. Nguyen, Giang D. Nguyen","doi":"10.1002/nag.3847","DOIUrl":"https://doi.org/10.1002/nag.3847","url":null,"abstract":"Seepage‐induced backward erosion is a complex and significant issue in geotechnical engineering that threatens the stability of infrastructure. Numerical prediction of the full development of backward erosion, pipe formation and induced failure remains challenging. For the first time, this study addresses this issue by modifying a recently developed five‐phase smoothed particle hydrodynamics (SPH) erosion framework. Full development of backward erosion was subsequently analysed in a rigid flume test and a field‐scale backward erosion‐induced levee failure test. The seepage and erosion analysis provided results consistent with experimental data, including pore water pressure evolution, pipe length and water flux at the exit, demonstrating the good performance of the proposed numerical approach. Key factors influencing backward erosion, such as anisotropic flow and critical hydraulic gradient, are also investigated through a parametric study conducted with the rigid flume test. The results provide a better understanding of the mechanism of backward erosion, pipe formation and the induced post‐failure process.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of Suffusion Under Torsional Shear Conditions With CFD‐DEM","authors":"Shun‐Xiang Song, Zhen‐Yu Yin, Ya‐Jing Liu, Pei Wang, Yi‐Pik Cheng","doi":"10.1002/nag.3844","DOIUrl":"https://doi.org/10.1002/nag.3844","url":null,"abstract":"This study investigates, for the first time ever, the suffusion on gap‐graded granular soils under torsional shear conditions from a microscopic perspective. A numerical model of the hollow cylinder torsional shear test (HCTST) using the discrete element method (DEM) is first developed, where an algorithm for simulating the real inner and outer rubber membranes of the hollow cylinder apparatus (HCA) is introduced. After the validation, the computational fluid dynamics (CFD) approach is introduced for the coupling between the particle and fluid phases. Then, a series of the coupled CFD‐DEM suffusion simulations considering the rotation of the major principal stress axis (<jats:italic>α</jats:italic>) and intermediate principal stress ratio (<jats:italic>b</jats:italic>) are conducted. It is found that more fine particles are eroded in cases having smaller <jats:italic>α</jats:italic> and <jats:italic>b</jats:italic>, and the clogging phenomenon in the middle zones becomes more significant as both <jats:italic>α</jats:italic> and <jats:italic>b</jats:italic> increase. From the microscopic perspective, the specimens whose contact anisotropy principal direction is close to the fluid direction will lose more fines, and the anisotropy magnitude also plays an important role. In addition, the differences in structure and vertical connectivity of the pores in HCTST samples under various complex loading conditions cause fine particles to have different migration paths, further resulting in different fines mass loss.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Soil–Structure Interface Models Considering Cyclic Loading Effect","authors":"Hai‐Lin Wang, Zhen‐Yu Yin, Xiao‐Qiang Gu, Yin‐Fu Jin","doi":"10.1002/nag.3831","DOIUrl":"https://doi.org/10.1002/nag.3831","url":null,"abstract":"The simulation of the soil–structure interface (SSI) under cyclic loading is critically important in geotechnical engineering. Numerous studies have been conducted to explore the cyclic behaviors exhibited at the SSI. However, existing model evaluations primarily rely on direct comparisons between experiments and simulations, with limited analysis focused on specific behaviors like accumulated normal displacement and stress degradation under cyclic loading. This study proposes and adapts six SSI models, including three nonlinear incremental models and three elastoplastic models. These models incorporate nonlinear shear modulus, critical state theory, and particle breakage effects to enhance their capability to capture SSI behaviors. Utilizing optimization‐based calibration for a fair comparison, the model parameters are fine‐tuned based on the experimental data. Comprehensive assessments including global comparisons and specific behaviors like accumulated normal displacement and stress degradation are carried out to evaluate the models' performance. The results indicate that all models effectively replicate the typical behaviors of SSI systems. By incorporating the particle breakage effect, the models can represent both the reversible and irreversible normal displacements under cyclic loading with better performance. The irreversible normal displacement remains stable and is solely influenced by the soil properties rather than the stress level. Moreover, the models successfully capture the stress degradation under constant normal stiffness caused by the irreversible normal displacement.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Simplified Practical Method for One‐Dimensional Large‐Strain Consolidation","authors":"Ding‐Bao Song, Peng‐Lin Li, Zhen‐Yu Yin, Jian‐Hua Yin","doi":"10.1002/nag.3843","DOIUrl":"https://doi.org/10.1002/nag.3843","url":null,"abstract":"A new simplified practical method for one‐dimensional nonlinear large‐strain consolidation of saturated homogenous soils is proposed. The derivation processes of the proposed method are introduced first, with a modification of Terzaghi's theory from a novel perspective to solve large‐strain consolidation problems. Verification checks of the proposed method with other solutions are then conducted. The proposed method is different from Lekha's solution because Lekha's analytical solution is based on the small strain theory. For linear consolidation, the proposed method shows excellent agreement with the Consolidation Settlement 2 (CS2) model. For nonlinear large‐strain consolidation, the new method is in good agreement with the CS2 model when <jats:italic>C<jats:sub>c</jats:sub></jats:italic>/<jats:italic>C<jats:sub>k</jats:sub></jats:italic> ≤ 1. After that, optimization of the proposed nonlinear solution is carried out for <jats:italic>C<jats:sub>c</jats:sub></jats:italic>/<jats:italic>C<jats:sub>k</jats:sub></jats:italic> &gt; 1 with a more precise average constant coefficient of consolidation used in the simplified practical method, and good agreement is obtained between the solutions from the proposed method and the CS2 model. Overall, the proposed simplified method provides practical, reliable, and efficient solutions for analyzing linear and nonlinear large‐strain consolidation.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical Solution to Estimate the Effect of Underlying Tunnel Excavations on the Existing Tunnel","authors":"Guohui Feng, Zhi Ding, Changjie Xu, Luju Liang, Shangqi Ge, Xiaozhen Fan, Kaifang Yang, Gang Wei","doi":"10.1002/nag.3836","DOIUrl":"https://doi.org/10.1002/nag.3836","url":null,"abstract":"The tunneling underlying inevitably leads to the displacement of adjacent soil, greatly influencing the deformation of the tunnel above. Most theoretical studies primarily concentrate on analyzing the mechanical equilibrium of individual tunnel sections, ignoring the energy generated by the system during tunnel deformation. Based on this, in view of the energy relationship, the overlying tunnel's deformation is simulated by using the Rayleigh–Ritz method. Further, its potential energy equation can be established based on the Vlasov foundation. The corresponding energy variational solution is solved according to the minimum potential energy principle, leading to an analytical solution for the shield tunneling‐induced overlying tunnel's response. The efficacy of the suggested method is verified by contrasting it with centrifuge experiments and field case studies derived from prior studies. Relative to the Winkler foundation model, which deviated from the suggested approach, the results derived from the suggested method show a closer correlation with the collected measurement data. Further parameter studies show that the vertical clearance and skew angle between two tunnels, the volume loss rate, and elastic modulus are significant factors affecting the tunnel behaviors due to tunneling underneath. The suggested theoretical model can be applied to forecast potential risks that an existing tunnel may encounter during the excavation of a new tunnel underlying in similar engineering projects.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Hybrid Algorithms for Segmentation and Reconstruction of Granular Grains From X‐Ray Micro Computed‐Tomography Images","authors":"Ruidong Li, Pin Zhang, Zhen‐Yu Yin, Brian Sheil","doi":"10.1002/nag.3832","DOIUrl":"https://doi.org/10.1002/nag.3832","url":null,"abstract":"Accurate three‐dimensional (3D) reconstruction of granular grains from x‐ray micro‐computed tomography (µCT) images is a long‐standing challenge, particularly for dense soil samples. This study develops a machine learning (ML) enhanced approach to automatically reconstruct granular grains from µCT images. The novel academic contributions of this paper include (a) a hierarchical strategy based on parameter‐independent polygonal approximation, area, and concavity analysis, for the first time, to identify and eliminate both intergranular and intragranular voids; (b) incorporation of a recursive segmentation scheme and ML‐based grain classifier to avoid over‐segmentation; (c) novel modifications on the determination of splitting paths to enhance segmentation accuracy; and (d) an effective approach of assigning initial level set functions for reconstructing granular grains automatically. The hybrid ML algorithm is applied to µCT images of dense Mojave Mars Simulant. The results indicate that the proposed method can accurately segment grain clumps with unclear boundaries. The new automatic reconstruction algorithm eliminates ineffective operations and achieves a three‐fold increase in computational speed than previous methods documented in the literature. Ninety‐one percent of grains with distinct boundaries can be reconstructed and the reconstruction ratio reaches 81% even for grains without distinct boundaries. The overall reconstruction ratio of grains increases by 20% compared with previous methods, achieving a step‐change improvement for one‐to‐one mapping of real soil samples.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}