Erol Lale, Jan Eliáš, Ke Yu, Matthew Troemner, Monika Středulová, Julien Khoury, Tianju Xue, Ioannis Koutromanos, Alessandro Fascetti, Bahar Ayhan, Baixi Chen, Giovanni Di Luzio, Yuhui Lyu, Madura Pathirage, Gilles Pijaudier‐Cabot, Lei Shen, Alessandro Tasora, Lifu Yang, Jiawei Zhong, Gianluca Cusatis
This article presents a comparison of various implementations of the Lattice Discrete Particle Model (LDPM) for the numerical simulation of concrete and other heterogeneous quasibrittle materials. The comparison involves the use of transient implicit and explicit solvers and steady‐state (static) solvers as well as implementations for central processing unit (CPU) and graphics processing unit (GPU). The various implementations are compared on the basis of a set of benchmarks tests describing behaviors of increasing computational complexity. They include elastic vibrations, confined strain‐hardening compressive response, tensile fracture, and unconfined strain‐softening compressive response. Metrics of interest extracted from the simulations include macroscopic stress versus strain responses, computational times, number of iterations, and energy balance error. Pairwise comparison of final crack patterns is provided through the correlation coefficient and normalized root mean square error of the crack opening vectors. Moreover, for the most numerically challenging case of unconfined compression with sliding boundary conditions, the stability of the strain‐softening response is tested by perturbing the solutions as well as changing the convergence criteria and time step size. Attached to this paper is the complete input data of the benchmark tests; this will allow researchers to run the examples and compare them with their own implementations. In addition, most of the reported implementations are publicly available in open source packages.
{"title":"Lattice Discrete Particle Model (LDPM): Comparison of Various Time Integration Solvers and Implementations","authors":"Erol Lale, Jan Eliáš, Ke Yu, Matthew Troemner, Monika Středulová, Julien Khoury, Tianju Xue, Ioannis Koutromanos, Alessandro Fascetti, Bahar Ayhan, Baixi Chen, Giovanni Di Luzio, Yuhui Lyu, Madura Pathirage, Gilles Pijaudier‐Cabot, Lei Shen, Alessandro Tasora, Lifu Yang, Jiawei Zhong, Gianluca Cusatis","doi":"10.1002/nag.70286","DOIUrl":"https://doi.org/10.1002/nag.70286","url":null,"abstract":"This article presents a comparison of various implementations of the Lattice Discrete Particle Model (LDPM) for the numerical simulation of concrete and other heterogeneous quasibrittle materials. The comparison involves the use of transient implicit and explicit solvers and steady‐state (static) solvers as well as implementations for central processing unit (CPU) and graphics processing unit (GPU). The various implementations are compared on the basis of a set of benchmarks tests describing behaviors of increasing computational complexity. They include elastic vibrations, confined strain‐hardening compressive response, tensile fracture, and unconfined strain‐softening compressive response. Metrics of interest extracted from the simulations include macroscopic stress versus strain responses, computational times, number of iterations, and energy balance error. Pairwise comparison of final crack patterns is provided through the correlation coefficient and normalized root mean square error of the crack opening vectors. Moreover, for the most numerically challenging case of unconfined compression with sliding boundary conditions, the stability of the strain‐softening response is tested by perturbing the solutions as well as changing the convergence criteria and time step size. Attached to this paper is the complete input data of the benchmark tests; this will allow researchers to run the examples and compare them with their own implementations. In addition, most of the reported implementations are publicly available in open source packages.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"4 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374060","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}
Tran Vu‐Hoang, Tan Nguyen, Hung‐Thinh Pham‐Tran, Duy Ly‐Khuong, Tuan A. Pham
This study develops a reliability‐based framework for predicting and optimizing tunnel stability in rock masses under surcharge loading while explicitly accounting for both aleatory and epistemic uncertainties. A unified dataset for twin circular and square tunnels is generated using Adaptive Finite Element Limit Analysis under the generalized Hoek–Brown criterion. The results demonstrate that probabilistic predictions obtained using Natural Gradient Boosting provide accurate stability estimates together with well‐calibrated uncertainty bounds, consistently outperforming multiple baseline machine‐learning models. Validation against more than 300 independent Optum G2 simulations confirms strong agreement with numerical benchmarks. A dedicated uncertainty decomposition analysis further shows that neglecting either input uncertainty or model uncertainty can lead to misleading and potentially unsafe reliability estimates, underscoring the necessity of joint uncertainty propagation. Overall, the proposed framework enables robust, uncertainty‐aware tunnel design under reliability constraints and provides a practical decision‐support tool for rock engineering applications.
{"title":"Tunnel Design in Rock Masses Under Uncertainty With Reliability Constraints and Natural Gradient Boosting‐Based Surrogates","authors":"Tran Vu‐Hoang, Tan Nguyen, Hung‐Thinh Pham‐Tran, Duy Ly‐Khuong, Tuan A. Pham","doi":"10.1002/nag.70285","DOIUrl":"https://doi.org/10.1002/nag.70285","url":null,"abstract":"This study develops a reliability‐based framework for predicting and optimizing tunnel stability in rock masses under surcharge loading while explicitly accounting for both aleatory and epistemic uncertainties. A unified dataset for twin circular and square tunnels is generated using Adaptive Finite Element Limit Analysis under the generalized Hoek–Brown criterion. The results demonstrate that probabilistic predictions obtained using Natural Gradient Boosting provide accurate stability estimates together with well‐calibrated uncertainty bounds, consistently outperforming multiple baseline machine‐learning models. Validation against more than 300 independent Optum G2 simulations confirms strong agreement with numerical benchmarks. A dedicated uncertainty decomposition analysis further shows that neglecting either input uncertainty or model uncertainty can lead to misleading and potentially unsafe reliability estimates, underscoring the necessity of joint uncertainty propagation. Overall, the proposed framework enables robust, uncertainty‐aware tunnel design under reliability constraints and provides a practical decision‐support tool for rock engineering applications.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"81 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374061","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}
Lina‐María Guayacán‐Carrillo, Jean‐Michel Pereira, Jean Sulem
Integrating interdisciplinary strategies with artificial intelligence (AI), particularly machine learning (ML), is an effective way of addressing urgent engineering challenges. Therefore, a thorough evaluation of existing methodologies is essential, taking into account their respective strengths, limitations and opportunities. This paper presents the main findings from exploratory research conducted through a variety of case studies. Based on the insights gained from these case studies, the paper critically examines three key areas of tunnelling. First, the challenges related to acquiring, generating and storing data, particularly for ML applications, are addressed. Emphasis is placed on ensuring that data are stored securely and are accessible for straightforward analysis. Second, the paper examines the application of ML to small datasets, providing insight into tunnelling requirements. It reviews ensemble methods and demonstrates their applicability using examples of small datasets. Third, the paper discusses the importance of interpretable tools in tunnel projects. Transparent and interpretable models help engineers understand model outputs, so it is important to consider this type of model wherever possible. The use of symbolic regression for estimating the long‐term closure of tunnels is presented. Finally, the paper summarises the key findings and considers the future prospects of this interdisciplinary approach. The aim is to encourage further development in this area.
{"title":"Lessons Learned From Using Simple Supervised Learning Tools on Small‐Ensemble Data—Applicability to Tunnel Design and Monitoring","authors":"Lina‐María Guayacán‐Carrillo, Jean‐Michel Pereira, Jean Sulem","doi":"10.1002/nag.70287","DOIUrl":"https://doi.org/10.1002/nag.70287","url":null,"abstract":"Integrating interdisciplinary strategies with artificial intelligence (AI), particularly machine learning (ML), is an effective way of addressing urgent engineering challenges. Therefore, a thorough evaluation of existing methodologies is essential, taking into account their respective strengths, limitations and opportunities. This paper presents the main findings from exploratory research conducted through a variety of case studies. Based on the insights gained from these case studies, the paper critically examines three key areas of tunnelling. First, the challenges related to acquiring, generating and storing data, particularly for ML applications, are addressed. Emphasis is placed on ensuring that data are stored securely and are accessible for straightforward analysis. Second, the paper examines the application of ML to small datasets, providing insight into tunnelling requirements. It reviews ensemble methods and demonstrates their applicability using examples of small datasets. Third, the paper discusses the importance of interpretable tools in tunnel projects. Transparent and interpretable models help engineers understand model outputs, so it is important to consider this type of model wherever possible. The use of symbolic regression for estimating the long‐term closure of tunnels is presented. Finally, the paper summarises the key findings and considers the future prospects of this interdisciplinary approach. The aim is to encourage further development in this area.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"188 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374062","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}
Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the necessary accuracy, MLPs often need to be sufficiently deep or wide, owing to the curse of dimensionality inherent in these problems. To overcome this limitation, we present an elastoplasticity informed Chebyshev‐based Kolmogorov–Arnold network (EPi‐cKAN) in this study. This architecture leverages the benefits of KANs and augmented Chebyshev polynomials, as well as integrates physical principles within both the network structure and the loss function. The primary objective of EPi‐cKAN is to provide an accurate and generalizable function approximation for non‐linear stress‐strain relationships, using fewer parameters compared to standard MLPs. To evaluate the efficiency, accuracy, and generalization capabilities of EPi‐cKAN in modeling complex elastoplastic behavior, we initially compare its performance with other cKAN‐based models, which include purely data‐driven parallel and serial architectures. Furthermore, to differentiate EPi‐cKAN's distinct performance, we also compare it against purely data‐driven and physics‐informed MLP‐based methods. Lastly, we test EPi‐cKAN's ability to predict blind strain‐controlled loading paths that extend beyond the training data distribution to gauge its generalization and predictive capabilities. EPi‐cKAN achieves superior accuracy in predicting stress components and generalizes well under blind strain‐controlled loading paths. It maintains robustness to noise, achieving only 1.52% error in deviatoric stress predictions with 5% noisy data, outperforming MLP models.
{"title":"Elastoplasticity Informed Kolmogorov–Arnold Networks Using Chebyshev Polynomials","authors":"Farinaz Mostajeran, Salah A. Faroughi","doi":"10.1002/nag.70283","DOIUrl":"https://doi.org/10.1002/nag.70283","url":null,"abstract":"Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the necessary accuracy, MLPs often need to be sufficiently deep or wide, owing to the curse of dimensionality inherent in these problems. To overcome this limitation, we present an elastoplasticity informed Chebyshev‐based Kolmogorov–Arnold network (EPi‐cKAN) in this study. This architecture leverages the benefits of KANs and augmented Chebyshev polynomials, as well as integrates physical principles within both the network structure and the loss function. The primary objective of EPi‐cKAN is to provide an accurate and generalizable function approximation for non‐linear stress‐strain relationships, using fewer parameters compared to standard MLPs. To evaluate the efficiency, accuracy, and generalization capabilities of EPi‐cKAN in modeling complex elastoplastic behavior, we initially compare its performance with other cKAN‐based models, which include purely data‐driven parallel and serial architectures. Furthermore, to differentiate EPi‐cKAN's distinct performance, we also compare it against purely data‐driven and physics‐informed MLP‐based methods. Lastly, we test EPi‐cKAN's ability to predict blind strain‐controlled loading paths that extend beyond the training data distribution to gauge its generalization and predictive capabilities. EPi‐cKAN achieves superior accuracy in predicting stress components and generalizes well under blind strain‐controlled loading paths. It maintains robustness to noise, achieving only 1.52% error in deviatoric stress predictions with 5% noisy data, outperforming MLP models.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"188 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374067","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}
The stability of shield tunneling through inclined strata composed of a soft upper layer and a hard lower layer represents a critical challenge in current underground engineering practice. This study proposes a theoretical framework for predicting surface settlement induced by shield tunneling under such geological conditions. First, a generalized model for the radial convergence of the surrounding soil is developed, and analytical expressions for the convergence center are derived for eight representative shield tunneling configurations commonly encountered in stratified ground. Next, the applicability of the classical Peck formula is evaluated using one‐dimensional linear regression analysis, tailored to the characteristics of upper‐soft and lower‐hard inclined strata. Based on this analysis, an analytical expression for surface settlement is established to account for the specific mechanical behavior of the composite strata. The proposed methodology is validated through a case study of the second Jiaozhou Bay Subsea Tunnel project in Qingdao, Shandong Province, utilizing both numerical simulations and in‐situ monitoring data. Results reveal that the maximum settlement and the offset of the settlement trough play distinct roles in shaping the overall deformation profile, with their relative significance varying across different strata configurations. These findings underscore the importance of considering both parameters in engineering practice. The proposed analytical model provides a reliable and practical tool for surface deformation prediction, offering empirical support for both real‐time assessment and preemptive risk management in shield tunneling projects.
{"title":"Analytical Prediction of Ground Settlement Induced by Shield Tunneling in Upper‐Soft and Lower‐Hard Inclined Strata","authors":"Pengfei Li, Jiannan Xie, Shuang Chen, Fei Jia","doi":"10.1002/nag.70289","DOIUrl":"https://doi.org/10.1002/nag.70289","url":null,"abstract":"The stability of shield tunneling through inclined strata composed of a soft upper layer and a hard lower layer represents a critical challenge in current underground engineering practice. This study proposes a theoretical framework for predicting surface settlement induced by shield tunneling under such geological conditions. First, a generalized model for the radial convergence of the surrounding soil is developed, and analytical expressions for the convergence center are derived for eight representative shield tunneling configurations commonly encountered in stratified ground. Next, the applicability of the classical Peck formula is evaluated using one‐dimensional linear regression analysis, tailored to the characteristics of upper‐soft and lower‐hard inclined strata. Based on this analysis, an analytical expression for surface settlement is established to account for the specific mechanical behavior of the composite strata. The proposed methodology is validated through a case study of the second Jiaozhou Bay Subsea Tunnel project in Qingdao, Shandong Province, utilizing both numerical simulations and in‐situ monitoring data. Results reveal that the maximum settlement and the offset of the settlement trough play distinct roles in shaping the overall deformation profile, with their relative significance varying across different strata configurations. These findings underscore the importance of considering both parameters in engineering practice. The proposed analytical model provides a reliable and practical tool for surface deformation prediction, offering empirical support for both real‐time assessment and preemptive risk management in shield tunneling projects.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"16 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374063","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}
Pressure changes in reservoirs lead to strain in the overlying confining unit, which can be measured near the ground surface using high‐precision strainmeters. We propose a methodology that adapts the classic Agarwal type curves used for analyzing recovery pressure data to interpret strain data. Poroelastic analyses indicate that plotting components of the strain tensor as a function of Agarwal time creates semi‐log straight lines. The average horizontal and vertical strains intersect the zero‐strain axis at times that are similar to the times determined using a similar analysis of the pressure. The intersection time gives a direct estimate of the hydraulic diffusivity. The relationship between the transformational strain and reservoir permeability, specific storage and porosity‐to‐fluid compressibility ratio was established using an Evolutionary Polynomial Regression (EPR) model. The model was trained and validated for different scenarios with the outlined reservoir parameters as inputs and simulated transformational strain as outputs. The result is an accurate model with good generalization power that will be used with strain data to estimate the bulk modulus of the solid and fluid and Poisson's ratio by assuming permeability is available from transient pressure well testing or other independent sources. The prediction and measurement uncertainties were also included in the solution process, leading to a distribution of the estimated parameters. The method was validated using (1) datasets from an idealized example created with a poroelastic simulator, and (2) field data measured at the North Avant Field during a recovery test conducted in a 530‐m reservoir.
{"title":"Strain Type‐Curve Analysis During Recovery Using Evolutionary Polynomial Regression for Evaluating Confined Reservoir Properties","authors":"Soheil Roudini, Lawrence C. Murdoch, Scott DeWolf","doi":"10.1002/nag.70284","DOIUrl":"https://doi.org/10.1002/nag.70284","url":null,"abstract":"Pressure changes in reservoirs lead to strain in the overlying confining unit, which can be measured near the ground surface using high‐precision strainmeters. We propose a methodology that adapts the classic Agarwal type curves used for analyzing recovery pressure data to interpret strain data. Poroelastic analyses indicate that plotting components of the strain tensor as a function of Agarwal time creates semi‐log straight lines. The average horizontal and vertical strains intersect the zero‐strain axis at times that are similar to the times determined using a similar analysis of the pressure. The intersection time gives a direct estimate of the hydraulic diffusivity. The relationship between the transformational strain and reservoir permeability, specific storage and porosity‐to‐fluid compressibility ratio was established using an Evolutionary Polynomial Regression (EPR) model. The model was trained and validated for different scenarios with the outlined reservoir parameters as inputs and simulated transformational strain as outputs. The result is an accurate model with good generalization power that will be used with strain data to estimate the bulk modulus of the solid and fluid and Poisson's ratio by assuming permeability is available from transient pressure well testing or other independent sources. The prediction and measurement uncertainties were also included in the solution process, leading to a distribution of the estimated parameters. The method was validated using (1) datasets from an idealized example created with a poroelastic simulator, and (2) field data measured at the North Avant Field during a recovery test conducted in a 530‐m reservoir.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"5 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374066","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}
Due to the presence of the tunnel lining, it is difficult to develop a system of linear equations for the unknowns of a shallow lined circular tunnel (SLCT) analytically based on the conformal mapping method. Besides, the traditional complex variable based series expansion (CVSE) method is limited to the problem associated with circular boundaries. To overcome the above limitations and extend the applicability of the CVSE method, a new analytical method, that is, the generalized series expansion (GSE) method for the SLCT is developed based on complex variable method. For this purpose, two generalized series for two complex potentials of the soil are introduced. Each generalized series is composed of two parts, that is, the singular and regular parts. The singular part of each generalized series is already known and singular in the lower half‐space occupied by the soil, while the regular part is unknown and analytic in the lower half‐space and it can be obtained by using Cauchy's integral theorem as well as the traction free condition along the soil surface. For simplicity, the lining of the tunnel is treated as a thin cylindrical shell. With the expressions for the above generalized series and governing equation for the tunnel lining, a system of linear equations for all the unknowns of the SLCT is derived analytically, with which the response of the SLCT and soil to arbitrary external loads is obtained.
{"title":"Analytical Solution for a Shallow Lined Circular Tunnel Based on the Generalized Series Expansion (GSE) Method","authors":"Jian‐Fei Lu, Kang‐Qi Sun","doi":"10.1002/nag.70282","DOIUrl":"https://doi.org/10.1002/nag.70282","url":null,"abstract":"Due to the presence of the tunnel lining, it is difficult to develop a system of linear equations for the unknowns of a shallow lined circular tunnel (SLCT) analytically based on the conformal mapping method. Besides, the traditional complex variable based series expansion (CVSE) method is limited to the problem associated with circular boundaries. To overcome the above limitations and extend the applicability of the CVSE method, a new analytical method, that is, the generalized series expansion (GSE) method for the SLCT is developed based on complex variable method. For this purpose, two generalized series for two complex potentials of the soil are introduced. Each generalized series is composed of two parts, that is, the singular and regular parts. The singular part of each generalized series is already known and singular in the lower half‐space occupied by the soil, while the regular part is unknown and analytic in the lower half‐space and it can be obtained by using Cauchy's integral theorem as well as the traction free condition along the soil surface. For simplicity, the lining of the tunnel is treated as a thin cylindrical shell. With the expressions for the above generalized series and governing equation for the tunnel lining, a system of linear equations for all the unknowns of the SLCT is derived analytically, with which the response of the SLCT and soil to arbitrary external loads is obtained.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"34 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374064","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}
Landfill liners serve as crucial barriers against contaminant migration. However, temperature effects can induce thermal diffusion and may cause clay liners to crack, significantly reducing containment performance. This study presents an analytical model for evaluating coupled heat and mass transport in a composite liner system. The system contains an intact geomembrane over a fractured compacted clay layer, and the model works under both steady‐state and transient conditions. The model incorporates diffusion, degradation, and thermal diffusion processes within both the soil matrix and the fractures. The validity and robustness of the proposed approach were verified through comparisons with existing analytical models. Results demonstrate that high Soret coefficients accelerate contaminant transport and cause abnormal contaminant accumulation far from the source, raising pollution risks in low concentration areas. The width of the fracture plays a dominant role in the breakthrough time and steady state concentration of contaminants, while the effect of changes in fracture spacing is not significant. Temperature difference has the most significant effect on the transport of Dichlorodiphenyltrichloroethane (DDT) and is the most relatively significant factor. The proposed analytical model shows that thermal diffusion shortens the service time of barrier systems. Fractures caused by temperature gradients also reduce their service life. These effects are particularly strong in the early stage. To ensure the long‐term operation of the barrier systems, it is vital to reduce the temperature difference between landfills and the external environment. It is also crucial to improve the degradation rates of contaminants and to prevent the formation of fractures.
{"title":"Thermally Induced Coupled Effects on Contaminant Transport in Composite Landfill Liners: An Analytical Modeling Approach","authors":"Hao Ding, Ziheng Wang, Junbo Zhou, Haijian Xie, Chunhua Zhang","doi":"10.1002/nag.70281","DOIUrl":"https://doi.org/10.1002/nag.70281","url":null,"abstract":"Landfill liners serve as crucial barriers against contaminant migration. However, temperature effects can induce thermal diffusion and may cause clay liners to crack, significantly reducing containment performance. This study presents an analytical model for evaluating coupled heat and mass transport in a composite liner system. The system contains an intact geomembrane over a fractured compacted clay layer, and the model works under both steady‐state and transient conditions. The model incorporates diffusion, degradation, and thermal diffusion processes within both the soil matrix and the fractures. The validity and robustness of the proposed approach were verified through comparisons with existing analytical models. Results demonstrate that high Soret coefficients accelerate contaminant transport and cause abnormal contaminant accumulation far from the source, raising pollution risks in low concentration areas. The width of the fracture plays a dominant role in the breakthrough time and steady state concentration of contaminants, while the effect of changes in fracture spacing is not significant. Temperature difference has the most significant effect on the transport of Dichlorodiphenyltrichloroethane (DDT) and is the most relatively significant factor. The proposed analytical model shows that thermal diffusion shortens the service time of barrier systems. Fractures caused by temperature gradients also reduce their service life. These effects are particularly strong in the early stage. To ensure the long‐term operation of the barrier systems, it is vital to reduce the temperature difference between landfills and the external environment. It is also crucial to improve the degradation rates of contaminants and to prevent the formation of fractures.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"15 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147374065","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}
Xiuling Wang, Yongli Xie, Jinxing Lai, Junling Qiu, Weiling Teng
This study proposes a simple numerical approach that incorporates rock strain‐softening (SS) and dilatancy into the triple‐shear‐element unified strength criterion (TS‐USC). A parametric analysis is conducted to elucidate rock mass responses. The results demonstrate that the intermediate principal stress (IPS) enhances rock mass stability and limits plastic zone growth. The TS‐USC should be used with caution for tunnel stability evaluation because it may underestimate rock displacements. Dilatancy behaviors notably affect rock displacements but have minimal influence on plastic zone radius, radial stresses, and tangential stresses. Therefore, the dilatancy model needs to be chosen reasonably to achieve an acceptable accuracy level. Rock displacements at the excavation profile and the plastic zone radius increase approximately linearly under different SS behaviors. SS behaviors mainly affect tangential stresses; however, for a fixed SS behavior (i.e., for a given δ ), radial stresses at softening‐residual interface are minimally affected by supporting force. These factors deserve attentions during stability analysis and support system design.
{"title":"A Simple Approach for Circular Tunnels Excavated in Strain‐Softening and Dilatancy Rock Masses","authors":"Xiuling Wang, Yongli Xie, Jinxing Lai, Junling Qiu, Weiling Teng","doi":"10.1002/nag.70278","DOIUrl":"https://doi.org/10.1002/nag.70278","url":null,"abstract":"This study proposes a simple numerical approach that incorporates rock strain‐softening (SS) and dilatancy into the triple‐shear‐element unified strength criterion (TS‐USC). A parametric analysis is conducted to elucidate rock mass responses. The results demonstrate that the intermediate principal stress (IPS) enhances rock mass stability and limits plastic zone growth. The TS‐USC should be used with caution for tunnel stability evaluation because it may underestimate rock displacements. Dilatancy behaviors notably affect rock displacements but have minimal influence on plastic zone radius, radial stresses, and tangential stresses. Therefore, the dilatancy model needs to be chosen reasonably to achieve an acceptable accuracy level. Rock displacements at the excavation profile and the plastic zone radius increase approximately linearly under different SS behaviors. SS behaviors mainly affect tangential stresses; however, for a fixed SS behavior (i.e., for a given <jats:italic>δ</jats:italic> ), radial stresses at softening‐residual interface are minimally affected by supporting force. These factors deserve attentions during stability analysis and support system design.","PeriodicalId":13786,"journal":{"name":"International Journal for Numerical and Analytical Methods in Geomechanics","volume":"49 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146260715","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}
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