Computers and Geotechnics最新文献

英文中文

Assessing highway resilience subjected to rainfall-induced slope failure

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-14 DOI: 10.1016/j.compgeo.2025.107134

Jie Zhang , Xiangyu Ma , Meng Lu , Atma Sharma , Lulu Zhang

Resilience assessment of the highway under rainfall-induced slope failure can support landslide hazard mitigation effectively. However, this direction is rarely studied. This study proposes a novel probabilistic method to assess highway resilience subjected to rainfall-induced slope failure, where the residual functionality of highways is quantified based on the landslide runout and the recovery process of blocked highways is modelled by a step function. First, a physically based model is built using a two stage FEM-MPM approach to simulate landslide runout under rainfall, and three types of uncertainties involved in resilience assessment of highways are explicitly modelled. To improve computational efficiency, a surrogate model is then created to predict the residual functionality of blocked highways. Finally, the mean value and the coefficient of variation of the highway resilience are estimated via Monte Carlo simulation. A four-lane highway next to a sandy slope is employed to perform the proposed method. The results show that the highway resilience is most sensitive to the strength parameter of the slope. As the variability of the slope strength parameter increases, the mean resilience of the highway decreases and the resilience variability increases. Overall, this study provides a useful tool for assessing highway resilience subjected to rainfall-induced slope failure.

{"title":"Assessing highway resilience subjected to rainfall-induced slope failure","authors":"Jie Zhang , Xiangyu Ma , Meng Lu , Atma Sharma , Lulu Zhang","doi":"10.1016/j.compgeo.2025.107134","DOIUrl":"10.1016/j.compgeo.2025.107134","url":null,"abstract":"<div><div>Resilience assessment of the highway under rainfall-induced slope failure can support landslide hazard mitigation effectively. However, this direction is rarely studied. This study proposes a novel probabilistic method to assess highway resilience subjected to rainfall-induced slope failure, where the residual functionality of highways is quantified based on the landslide runout and the recovery process of blocked highways is modelled by a step function. First, a physically based model is built using a two stage FEM-MPM approach to simulate landslide runout under rainfall, and three types of uncertainties involved in resilience assessment of highways are explicitly modelled. To improve computational efficiency, a surrogate model is then created to predict the residual functionality of blocked highways. Finally, the mean value and the coefficient of variation of the highway resilience are estimated via Monte Carlo simulation. A four-lane highway next to a sandy slope is employed to perform the proposed method. The results show that the highway resilience is most sensitive to the strength parameter of the slope. As the variability of the slope strength parameter increases, the mean resilience of the highway decreases and the resilience variability increases. Overall, this study provides a useful tool for assessing highway resilience subjected to rainfall-induced slope failure.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107134"},"PeriodicalIF":5.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hydrothermal behaviors of geomaterials with multiple fracture channels: Effect of intersecting “X” and “Y” shape fractures

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-13 DOI: 10.1016/j.compgeo.2025.107121

Shi-Feng Lu , Xiao-Pei Guo , Teng-Yuan Zhao , Ling Xu

The water flow and heat transfer characteristics within fractured geomaterials have significant practical applications in various fields, including deep geothermal resource development and oil and gas extraction. However, the presence of numerous intersecting fracture networks in geothermal systems complicates the hydrothermal coupling process in fractured geomaterials. Therefore, in this study, a multiphase microcontinuum approach is introduced to systematically study the hydrothermal coupling behavior in a multichannel fractured rock mass. Initially, a numerical model for water flow and heat transfer in fractured rock masses was established, and the accuracy and reliability of the multiphase microcontinuum method were verified through experiments. Two representative intersecting fractures in the rock mass, namely, “X”-shaped and “Y”-shaped fractures, were subsequently considered to delve into the effects of key parameters, such as the fracture aperture, water injection velocity, intersection angle of fractures, and water injection strategy, on the heat transfer performance of the fractured rock mass. Additionally, rock with parallel fracture channels was established to compare and investigate the heat transfer effect between water and rock masses with different fracture channel shapes. The results indicate that the fracture aperture, water flow rate, and intersection angle of fractures have substantial control over the heat transfer effect in fractured rock masses, whereas adjustments to the water injection method have a limited overall impact on the final heat transfer effect. Compared with single fracture channels, multichannel fractures can effectively enhance the heat transfer effect, and the shape and distribution of fracture channels significantly influence the heat exchange efficiency of fractured rock masses.

{"title":"Hydrothermal behaviors of geomaterials with multiple fracture channels: Effect of intersecting “X” and “Y” shape fractures","authors":"Shi-Feng Lu , Xiao-Pei Guo , Teng-Yuan Zhao , Ling Xu","doi":"10.1016/j.compgeo.2025.107121","DOIUrl":"10.1016/j.compgeo.2025.107121","url":null,"abstract":"<div><div>The water flow and heat transfer characteristics within fractured geomaterials have significant practical applications in various fields, including deep geothermal resource development and oil and gas extraction. However, the presence of numerous intersecting fracture networks in geothermal systems complicates the hydrothermal coupling process in fractured geomaterials. Therefore, in this study, a multiphase microcontinuum approach is introduced to systematically study the hydrothermal coupling behavior in a multichannel fractured rock mass. Initially, a numerical model for water flow and heat transfer in fractured rock masses was established, and the accuracy and reliability of the multiphase microcontinuum method were verified through experiments. Two representative intersecting fractures in the rock mass, namely, “X”-shaped and “Y”-shaped fractures, were subsequently considered to delve into the effects of key parameters, such as the fracture aperture, water injection velocity, intersection angle of fractures, and water injection strategy, on the heat transfer performance of the fractured rock mass. Additionally, rock with parallel fracture channels was established to compare and investigate the heat transfer effect between water and rock masses with different fracture channel shapes. The results indicate that the fracture aperture, water flow rate, and intersection angle of fractures have substantial control over the heat transfer effect in fractured rock masses, whereas adjustments to the water injection method have a limited overall impact on the final heat transfer effect. Compared with single fracture channels, multichannel fractures can effectively enhance the heat transfer effect, and the shape and distribution of fracture channels significantly influence the heat exchange efficiency of fractured rock masses.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107121"},"PeriodicalIF":5.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A self-adaptive physics-informed neural networks method for large strain consolidation analysis

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-12 DOI: 10.1016/j.compgeo.2025.107131

Hang Zhou , Han Wu , Brian Sheil , Zhuhong Wang

Physics-Informed Neural Networks (PINNs) have shown considerable potential in solving both forward and inverse problems governed by partial differential equations (PDEs) for a wide range of practical applications. This study leverages PINNs for modeling nonlinear large-strain consolidation of soft soil, including creep behavior. The inherent material and geometric nonlinearities associated with soft soil consolidation pose challenges for PINNs, including precision and computational efficiency. To address these issues, we introduce self-adaptive physics-informed neural networks (SA-PINNs), featuring an adaptive loss function weighting and a slope scaling method for the activation functions. Additionally, a sensitivity analysis exploring the influence of monitoring data on the parameter inversion accuracy is presented. Two engineering case studies are used to benchmark the settlement prediction capabilities of the present SA-PINN method with traditional techniques, demonstrating the superior prediction accuracy and consistency of the SA-PINN approach. The findings highlight the significant potential of SA-PINN in practical geotechnical engineering problems.

{"title":"A self-adaptive physics-informed neural networks method for large strain consolidation analysis","authors":"Hang Zhou , Han Wu , Brian Sheil , Zhuhong Wang","doi":"10.1016/j.compgeo.2025.107131","DOIUrl":"10.1016/j.compgeo.2025.107131","url":null,"abstract":"<div><div>Physics-Informed Neural Networks (PINNs) have shown considerable potential in solving both forward and inverse problems governed by partial differential equations (PDEs) for a wide range of practical applications. This study leverages PINNs for modeling nonlinear large-strain consolidation of soft soil, including creep behavior. The inherent material and geometric nonlinearities associated with soft soil consolidation pose challenges for PINNs, including precision and computational efficiency. To address these issues, we introduce self-adaptive physics-informed neural networks (SA-PINNs), featuring an adaptive loss function weighting and a slope scaling method for the activation functions. Additionally, a sensitivity analysis exploring the influence of monitoring data on the parameter inversion accuracy is presented. Two engineering case studies are used to benchmark the settlement prediction capabilities of the present SA-PINN method with traditional techniques, demonstrating the superior prediction accuracy and consistency of the SA-PINN approach. The findings highlight the significant potential of SA-PINN in practical geotechnical engineering problems.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107131"},"PeriodicalIF":5.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategically Coupled Inertial Flow and Interface Evolution Model for Cavern Development by Dissolution Mining

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-12 DOI: 10.1016/j.compgeo.2025.107122

Li Li , Robert Garcie , Maurice B. Dusseault

Shape control is important for large-scale underground caverns developed by dissolution mining; however, it is greatly complicated by turbulent brine flow and natural and forced convection. An improved model for simulating dissolution mining of large caverns over long injection periods is presented. The brine flow is modeled using the Reynolds-Averaged Navier-Stokes equations coupled with a mass conservation equation governing the evolution of the cavern walls. It is demonstrated that cavern wall irregularities previously assumed to be exclusively due to mineral heterogeneity are also readily attributable to the turbulent flow. Two competing dissolution mechanisms are identified, one enhancing dissolution unevenness and one that smooths out irregular dissolution features on the cavern walls. Two cavern construction methods were investigated: reverse and direct dissolution methods, which tend towards a “morning glory” and a “wide bottom decanter” shaped cavern, respectively. Results suggest that, because of the buoyancy effect, large roof spans are unavoidable without using an oil/air blanket; however, blanket usage leads to more jagged boundaries and can decrease the cavern construction rate. This study opens a path to the development of robust models of large-scale cavern development for energy storage and has implications for similar processes such as leach mining or ice melting.

{"title":"Strategically Coupled Inertial Flow and Interface Evolution Model for Cavern Development by Dissolution Mining","authors":"Li Li , Robert Garcie , Maurice B. Dusseault","doi":"10.1016/j.compgeo.2025.107122","DOIUrl":"10.1016/j.compgeo.2025.107122","url":null,"abstract":"<div><div>Shape control is important for large-scale underground caverns developed by dissolution mining; however, it is greatly complicated by turbulent brine flow and natural and forced convection. An improved model for simulating dissolution mining of large caverns over long injection periods is presented. The brine flow is modeled using the Reynolds-Averaged Navier-Stokes equations coupled with a mass conservation equation governing the evolution of the cavern walls. It is demonstrated that cavern wall irregularities previously assumed to be exclusively due to mineral heterogeneity are also readily attributable to the turbulent flow. Two competing dissolution mechanisms are identified, one enhancing dissolution unevenness and one that smooths out irregular dissolution features on the cavern walls. Two cavern construction methods were investigated: reverse and direct dissolution methods, which tend towards a “morning glory” and a “wide bottom decanter” shaped cavern, respectively. Results suggest that, because of the buoyancy effect, large roof spans are unavoidable without using an oil/air blanket; however, blanket usage leads to more jagged boundaries and can decrease the cavern construction rate. This study opens a path to the development of robust models of large-scale cavern development for energy storage and has implications for similar processes such as leach mining or ice melting.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107122"},"PeriodicalIF":5.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven probabilistic seismic demand prediction and sustainability optimization of stone columns for liquefaction mitigation in regional mildly sloping ground

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-10 DOI: 10.1016/j.compgeo.2025.107125

Zhijian Qiu , Junrui Zhu , Ahmed Ebeido , Athul Prabhakaran , Yewei Zheng

With the growing need for efficient mitigation strategies in liquefaction-prone regions, ensuring both seismic resilience and sustainability of infrastructure has become increasingly significant. This paper presents a data-driven probabilistic seismic demand model (PSDM) prediction and sustainability optimization framework to mitigate liquefaction-induced lateral deformation in regional mildly sloping ground improved with stone columns. The framework integrates finite element (FE) simulations with machine learning (ML) models, generating 1,200 ground FE models based on the key site attributes, such as ground inclination, soil properties, and stone column configurations. The performance of the selected ML models is evaluated through hyperparameter tuning by k-fold cross-validation, with the artificial neural network (ANN) outperforming other models in accurately predicting the PSDM. Subsequently, this framework is applied to a set of representative mildly sloping ground sites, enabling rapid PSDM prediction for each site with varying site attributes. Moreover, by incorporating cost and sustainability metrics, multi-objective optimization is performed using the developed ANN predictive model to maximize seismic performance while minimizing total carbon emissions and costs associated with ground improvement. Overall, the framework allows for rapid and accurate PSDM prediction and regional optimization, facilitating the identification of the optimal stone column configurations for efficient and sustainable liquefaction mitigation.

{"title":"Data-driven probabilistic seismic demand prediction and sustainability optimization of stone columns for liquefaction mitigation in regional mildly sloping ground","authors":"Zhijian Qiu , Junrui Zhu , Ahmed Ebeido , Athul Prabhakaran , Yewei Zheng","doi":"10.1016/j.compgeo.2025.107125","DOIUrl":"10.1016/j.compgeo.2025.107125","url":null,"abstract":"<div><div>With the growing need for efficient mitigation strategies in liquefaction-prone regions, ensuring both seismic resilience and sustainability of infrastructure has become increasingly significant. This paper presents a data-driven probabilistic seismic demand model (PSDM) prediction and sustainability optimization framework to mitigate liquefaction-induced lateral deformation in regional mildly sloping ground improved with stone columns. The framework integrates finite element (FE) simulations with machine learning (ML) models, generating 1,200 ground FE models based on the key site attributes, such as ground inclination, soil properties, and stone column configurations. The performance of the selected ML models is evaluated through hyperparameter tuning by <em>k</em>-fold cross-validation, with the artificial neural network (ANN) outperforming other models in accurately predicting the PSDM. Subsequently, this framework is applied to a set of representative mildly sloping ground sites, enabling rapid PSDM prediction for each site with varying site attributes. Moreover, by incorporating cost and sustainability metrics, multi-objective optimization is performed using the developed ANN predictive model to maximize seismic performance while minimizing total carbon emissions and costs associated with ground improvement. Overall, the framework allows for rapid and accurate PSDM prediction and regional optimization, facilitating the identification of the optimal stone column configurations for efficient and sustainable liquefaction mitigation.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107125"},"PeriodicalIF":5.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of compression index of red mud by machine learning interpretability methods

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-10 DOI: 10.1016/j.compgeo.2025.107130

Fan Yang , Jieya Zhang , Mingxing Xie , Wenwen Cui , Xiaoqiang Dong

The annual increase in red mud emissions necessitates the expansion of bauxite residue disposal areas (BRDAs), while the escalating land value has led to proposals for development on closed BRDAs. Therefore, understanding the compressive properties of red mud is critical for the safe management and construction of BRDAs. The process of deriving compression index (C_c) through consolidation tests to assess compression characteristics is both time-intensive and vulnerable to the quality of the sampling methods employed. Consequently, it is essential to develop predictive models for compression indices that utilize more easily measurable physical parameters. This study proposes the use of machine learning(ML) models to predict the C_c of red mud. Several machine learning models were studied, including Linear Regression (LR), Ridge Regression (RR), Support Vector Machines (SVR), Random Forest(RF), Extremely Randomized Trees (Extra Trees), K-Nearest Neighbors (KNN), Category Boosting (CatBoost), and LightGBM (Light Gradient Boosting Machine). The grid search algorithm was used to obtain the optimal parameters for each ML model, and k-fold cross-validation was employed to enhance the model’s generalization performance. Ultimately, the KNN model achieved the best performance. The SHAP method was used to describe the specific influence patterns, and to provide a quantitative contribution of each feature to the C_c of red mud. The research indicated that the I_L and w_n exerted the most substantial influence on the C_c, yielding a positive effect.

{"title":"Evaluation of compression index of red mud by machine learning interpretability methods","authors":"Fan Yang , Jieya Zhang , Mingxing Xie , Wenwen Cui , Xiaoqiang Dong","doi":"10.1016/j.compgeo.2025.107130","DOIUrl":"10.1016/j.compgeo.2025.107130","url":null,"abstract":"<div><div>The annual increase in red mud emissions necessitates the expansion of bauxite residue disposal areas (BRDAs), while the escalating land value has led to proposals for development on closed BRDAs. Therefore, understanding the compressive properties of red mud is critical for the safe management and construction of BRDAs. The process of deriving compression index (<em>C<sub>c</sub></em>) through consolidation tests to assess compression characteristics is both time-intensive and vulnerable to the quality of the sampling methods employed. Consequently, it is essential to develop predictive models for compression indices that utilize more easily measurable physical parameters. This study proposes the use of machine learning(ML) models to predict the <em>C<sub>c</sub></em> of red mud. Several machine learning models were studied, including Linear Regression (LR), Ridge Regression (RR), Support Vector Machines (SVR), Random Forest(RF), Extremely Randomized Trees (Extra Trees), K-Nearest Neighbors (KNN), Category Boosting (CatBoost), and LightGBM (Light Gradient Boosting Machine). The grid search algorithm was used to obtain the optimal parameters for each ML model, and k-fold cross-validation was employed to enhance the model’s generalization performance. Ultimately, the KNN model achieved the best performance. The SHAP method was used to describe the specific influence patterns, and to provide a quantitative contribution of each feature to the <em>C<sub>c</sub></em> of red mud. The research indicated that the <em>I<sub>L</sub></em> and <em>w<sub>n</sub></em> exerted the most substantial influence on the <em>C<sub>c</sub></em>, yielding a positive effect.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107130"},"PeriodicalIF":5.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel soil reaction model for continuous impact pile driving

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-08 DOI: 10.1016/j.compgeo.2025.107123

Shihong Zhang , Lizhong Wang , Mengtao Xu , Shengjie Rui , Zhen Guo

Impact pile driving is widely employed in various environments. The soil surrounding driven piles undergoes large shear displacements and highly cyclic loads, leading to significant strength degradation. This paper introduces a novel soil reaction model with easily calibrated parameters to estimate the pile penetration performance under continuous impact driving, incorporating both cyclic degradation and base gap. Soil cumulative plastic displacement is utilized to quantity the degradation, enabling more accurate simulation of cyclic pile response. The model is integrated into the pile driving system and applied in multiple-blow analysis. Non-linear cumulative displacement-blow count curves are analyzed and the development of residual stress varies between the pile upper and lower sections. It is found that lower blow counts are required when cyclic degradation is considered, although the increased rebound effect may counterbalance this benefit. Comparative analyses for degradation constants further demonstrate that early-stage degradation has a more pronounced impact. Finally, the proposed model is also adopted to predict blow count in field practice, offering valuable insights for driveability analysis.

{"title":"A novel soil reaction model for continuous impact pile driving","authors":"Shihong Zhang , Lizhong Wang , Mengtao Xu , Shengjie Rui , Zhen Guo","doi":"10.1016/j.compgeo.2025.107123","DOIUrl":"10.1016/j.compgeo.2025.107123","url":null,"abstract":"<div><div>Impact pile driving is widely employed in various environments. The soil surrounding driven piles undergoes large shear displacements and highly cyclic loads, leading to significant strength degradation. This paper introduces a novel soil reaction model with easily calibrated parameters to estimate the pile penetration performance under continuous impact driving, incorporating both cyclic degradation and base gap. Soil cumulative plastic displacement is utilized to quantity the degradation, enabling more accurate simulation of cyclic pile response. The model is integrated into the pile driving system and applied in multiple-blow analysis. Non-linear cumulative displacement-blow count curves are analyzed and the development of residual stress varies between the pile upper and lower sections. It is found that lower blow counts are required when cyclic degradation is considered, although the increased rebound effect may counterbalance this benefit. Comparative analyses for degradation constants further demonstrate that early-stage degradation has a more pronounced impact. Finally, the proposed model is also adopted to predict blow count in field practice, offering valuable insights for driveability analysis.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107123"},"PeriodicalIF":5.3,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hypoplastic model for gas hydrate-bearing sediments considering pore morphology

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-07 DOI: 10.1016/j.compgeo.2025.107115

Sahil Wani , Ramesh Kannan Kandasami , Wei Wu

A novel hypoplastic constitutive model is proposed which accurately captures the geomechanical behavior of gas hydrate-bearing sediments. This model explicitly accounts for the effects of hydrate saturation, temperature, and pore pressure across different pore morphologies. A cementation coefficient (

α_{h}

) is introduced which represents the influence of pore morphology on the sediment’s mechanical response. The predictive capabilities of the proposed model are thoroughly validated against an extensive dataset from the literature, covering various initial stress, hydrate saturation, and temperature conditions. The model’s generality is demonstrated by its ability to predict the mechanical behavior of sediments containing both methane (

{CH}_{4}

) and carbon dioxide (

{CO}_{2}

) hydrates. Furthermore, the model responses are validated using data obtained from testing natural hydrate cores from the Nankai Trough.

{"title":"Hypoplastic model for gas hydrate-bearing sediments considering pore morphology","authors":"Sahil Wani , Ramesh Kannan Kandasami , Wei Wu","doi":"10.1016/j.compgeo.2025.107115","DOIUrl":"10.1016/j.compgeo.2025.107115","url":null,"abstract":"<div><div>A novel hypoplastic constitutive model is proposed which accurately captures the geomechanical behavior of gas hydrate-bearing sediments. This model explicitly accounts for the effects of hydrate saturation, temperature, and pore pressure across different pore morphologies. A cementation coefficient (<span><math><msub><mrow><mi>α</mi></mrow><mrow><mi>h</mi></mrow></msub></math></span>) is introduced which represents the influence of pore morphology on the sediment’s mechanical response. The predictive capabilities of the proposed model are thoroughly validated against an extensive dataset from the literature, covering various initial stress, hydrate saturation, and temperature conditions. The model’s generality is demonstrated by its ability to predict the mechanical behavior of sediments containing both methane (<span><math><msub><mrow><mi>CH</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span>) and carbon dioxide (<span><math><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) hydrates. Furthermore, the model responses are validated using data obtained from testing natural hydrate cores from the Nankai Trough.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107115"},"PeriodicalIF":5.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic analysis of ground surface settlement induced by super large diameter shield tunneling based on 3D random finite element method

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-07 DOI: 10.1016/j.compgeo.2025.107111

Jing-Kang Shi , Jin-Zhang Zhang , Shuai Zhao , Zhen-Chang Guan , Hong-Wei Huang

The accurate prediction of ground surface settlement remains a crucial undertaking in shield tunnelling, particularly for tunnels with super large diameters. Instead of employing conventional deterministic analysis, this study employed 3D random finite element method (RFEM) to probabilistically investigate the longitudinal ground surface settlement induced by super large diameter shield tunneling. Through large quantities of numerical simulations, a new modified Logistic function with parameters of maximum ground settlement S_max, settlement upon face arrival S₀, and settlement speed during face passage v was proposed to represent the longitudinal ground surface settlement curves. The probabilistic distribution of longitudinal ground settlement was transformed into a multivariate Gaussian distribution of S_max, S₀, and v. A randomness transfer coefficient η was innovatively proposed to evaluate the effects of randomness transferred from modulus random field to the ground settlements. It was found that the mean values of S_max, S₀, and v were rarely affected by random field statistics. The randomness transferring coefficient η was determined by the vertical and horizontal scales of fluctuation (δ_v and δ_h). The correlation coefficients between S_max, S₀, and v were only sensitive to δ_h.

{"title":"Probabilistic analysis of ground surface settlement induced by super large diameter shield tunneling based on 3D random finite element method","authors":"Jing-Kang Shi , Jin-Zhang Zhang , Shuai Zhao , Zhen-Chang Guan , Hong-Wei Huang","doi":"10.1016/j.compgeo.2025.107111","DOIUrl":"10.1016/j.compgeo.2025.107111","url":null,"abstract":"<div><div>The accurate prediction of ground surface settlement remains a crucial undertaking in shield tunnelling, particularly for tunnels with super large diameters. Instead of employing conventional deterministic analysis, this study employed 3D random finite element method (RFEM) to probabilistically investigate the longitudinal ground surface settlement induced by super large diameter shield tunneling. Through large quantities of numerical simulations, a new modified Logistic function with parameters of maximum ground settlement <em>S</em><sub>max</sub>, settlement upon face arrival <em>S</em><sub>0</sub>, and settlement speed during face passage <em>v</em> was proposed to represent the longitudinal ground surface settlement curves. The probabilistic distribution of longitudinal ground settlement was transformed into a multivariate Gaussian distribution of <em>S</em><sub>max</sub>, <em>S</em><sub>0</sub>, and <em>v</em>. A randomness transfer coefficient <em>η</em> was innovatively proposed to evaluate the effects of randomness transferred from modulus random field to the ground settlements. It was found that the mean values of <em>S</em><sub>max</sub>, <em>S</em><sub>0</sub>, and <em>v</em> were rarely affected by random field statistics. The randomness transferring coefficient <em>η</em> was determined by the vertical and horizontal scales of fluctuation (<em>δ</em><sub>v</sub> and <em>δ</em><sub>h</sub>). The correlation coefficients between <em>S</em><sub>max</sub>, <em>S</em><sub>0</sub>, and <em>v</em> were only sensitive to <em>δ</em><sub>h</sub>.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107111"},"PeriodicalIF":5.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic bearing capacity analysis of square and rectangular footings on cohesive soil slopes considering three-dimensional rotational anisotropy

IF 5.3 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Geotechnics

Pub Date : 2025-02-07 DOI: 10.1016/j.compgeo.2025.107117

Xiaoming Liu , Qinji Jia , R.A. Galindo , Fengshan Mao

The influence of soil variability on the probabilistic bearing capacity of strip footings near slopes has been extensively studied, particularly under short-term undrained conditions. However, these investigations, predominantly based on the plane-strain assumption, fall short in accurately estimating the bearing capacity of square and rectangular footings and in capturing the spatial variability of soils. This study focuses on short-term undrained conditions and employs the random finite element method (RFEM) and Monte Carlo simulation (MCS) techniques to explore the effect of rotational anisotropy on the bearing capacity response and failure probability of a square and rectangular footing-cohesive slope system under a three-dimensional (3D) framework. The findings reveal that the rotation angles of soil strata significantly impact both the mean and coefficient of variation of the bearing capacity, with distinct variation patterns emerging for different footing orientations and aspect ratios. Typical failure patterns are identified, illustrating the correlation between the bearing capacity response, the footing orientations and aspect ratios, and the extension direction of plasticity. The probabilistic results are presented as probability density functions (PDF) and cumulative distribution functions (CDF) for various rotation angles around the x-axis and y-axis and for different L/B ratios of the footings. Additionally, detailed design tables, including failure probability results and corresponding safety factors for specific target failure probabilities, are provided to guide engineering applications.

{"title":"Probabilistic bearing capacity analysis of square and rectangular footings on cohesive soil slopes considering three-dimensional rotational anisotropy","authors":"Xiaoming Liu , Qinji Jia , R.A. Galindo , Fengshan Mao","doi":"10.1016/j.compgeo.2025.107117","DOIUrl":"10.1016/j.compgeo.2025.107117","url":null,"abstract":"<div><div>The influence of soil variability on the probabilistic bearing capacity of strip footings near slopes has been extensively studied, particularly under short-term undrained conditions. However, these investigations, predominantly based on the plane-strain assumption, fall short in accurately estimating the bearing capacity of square and rectangular footings and in capturing the spatial variability of soils. This study focuses on short-term undrained conditions and employs the random finite element method (RFEM) and Monte Carlo simulation (MCS) techniques to explore the effect of rotational anisotropy on the bearing capacity response and failure probability of a square and rectangular footing-cohesive slope system under a three-dimensional (3D) framework. The findings reveal that the rotation angles of soil strata significantly impact both the mean and coefficient of variation of the bearing capacity, with distinct variation patterns emerging for different footing orientations and aspect ratios. Typical failure patterns are identified, illustrating the correlation between the bearing capacity response, the footing orientations and aspect ratios, and the extension direction of plasticity. The probabilistic results are presented as probability density functions (PDF) and cumulative distribution functions (CDF) for various rotation angles around the <em>x</em>-axis and <em>y</em>-axis and for different <em>L</em>/B ratios of the footings. Additionally, detailed design tables, including failure probability results and corresponding safety factors for specific target failure probabilities, are provided to guide engineering applications.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"181 ","pages":"Article 107117"},"PeriodicalIF":5.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143324760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Computers and Geotechnics

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀