Computers and Geotechnics最新文献

{"title":"A unified contact model incorporating surface abrasion for DEM simulation of granular soil behavior across small to large strains","authors":"Jiuyang Zhou ,&nbsp;Xiaoqiang Gu ,&nbsp;Hongwei Wu ,&nbsp;Jing Hu","doi":"10.1016/j.compgeo.2026.107965","DOIUrl":"10.1016/j.compgeo.2026.107965","url":null,"abstract":"<div><div>The discrete element method (DEM) is a valuable tool for simulating the mechanical behavior of granular soils at both large and small-to-medium strains. However, the use of different contact models for varying strain levels has led to inconsistencies in previous simulations. This study highlights that while factors such as particle shape and fragmentation are critical, the omission of surface degradation mechanisms in current models may significantly contribute to their unsatisfactory performance in conventional undrained simulations. To address this, a unified contact model is proposed to account for the influence of surface degradation (specifically abrasion) on the soil behaviors across small to large strains. The proposed model is formulated such that the normal force–displacement law transitions naturally from Hertzian contact mechanics at small strains to a linear-bounded constitutive relation at large strains, capturing the accumulation of surface degradation as a primary driver of the soil skeleton’s increased compressibility. Simulations confirm that the proposed model effectively captures the macroscopic mechanical behavior of granular soils at the considered strain levels, showing good agreement with experimental data. In addition, the comparative analyses of macroscopic and microscopic properties are revealing that small strain modulus <em>G</em>₀ and macro state parameter <em>ψ</em>_e0 can be well correlated with mechanical coordination number <em>MCN</em> and micro state parameter <em>ψ</em>_MCN0, respectively.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107965"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174383","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}

{"title":"Prediction of rock mass energy evolution during deep tunnel construction using static temporal fusion transformer and numerical surrogate model","authors":"Yaoru Liu ,&nbsp;Songyu Yue ,&nbsp;Rujiu Zhang ,&nbsp;Yuequn Huang ,&nbsp;Muwu Xie ,&nbsp;Qiang Yang","doi":"10.1016/j.compgeo.2026.107930","DOIUrl":"10.1016/j.compgeo.2026.107930","url":null,"abstract":"<div><div>During TBM tunneling, timely and effective prediction of energy evolution of surrounding rock is critical for forecasting potential hazards like rockburst, serving as a fundamental safeguard for deep underground construction. So far, most researchers often underestimate the importance of rapid prediction of the energy evolution of tunnel surrounding rock, resulting in the inability to predict specific information such as the location and time of rock bursts. In this study, a surrogate model for predicting the evolution of energy dissipation rate of tunnel surrounding rock based on the static TFT model is proposed to achieve fast time series prediction. Building upon the Temporal Fusion Transformer (TFT) framework, the static TFT model which considers the time invariant nature of tunnel surrounding rock data is proposed. 4373 numerical samples containing 9 surrounding rock energy influencing factors and 12 output features are established and trained on the model guided by the proposed mixed data and physical loss function. The model’s performance is evaluated through sample size impact, and ablation feature experiments, as well as comparing the predictive accuracy and fitting effectiveness with baseline models. It is found that the proposed model achieves superior performance across all metrics in predicting surrounding rock energy evolution without redundant features. Specifically, it attains an <em>MAE</em> of <span><math><mrow><mn>0.0447</mn><mi>J</mi><mo>·</mo><msup><mi>m</mi><mrow><mo>-</mo><mn>3</mn></mrow></msup><mo>·</mo><msup><mi>s</mi><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></math></span>, an <em>R</em>² of 0.9201, and an <em>MSE</em> of <span><math><mrow><mn>0.0148</mn><msup><mi>J</mi><mn>2</mn></msup><mo>·</mo><msup><mi>m</mi><mrow><mo>-</mo><mn>6</mn></mrow></msup><mo>·</mo><msup><mi>s</mi><mrow><mo>-</mo><mn>2</mn></mrow></msup></mrow></math></span> for energy dissipation rate prediction. These outcomes signify a substantive advancement in rapid energy evolution forecasting for tunnel surrounding rock and provide an early-warning basis for related geohazards.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107930"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174387","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}

{"title":"A novel large language model–inspired transformer framework for reconstructing subsurface soil properties at untested locations from sparse cone penetrometer test data","authors":"Qianbiao Liu ,&nbsp;Michael Bertolacci ,&nbsp;Fraser Bransby ,&nbsp;Tim French ,&nbsp;Phil Watson","doi":"10.1016/j.compgeo.2026.107986","DOIUrl":"10.1016/j.compgeo.2026.107986","url":null,"abstract":"<div><div>Accurate characterization of subsurface conditions from cone penetrometer test (CPT) data remains challenging due to inherent soil spatial variability and laterally sparse datasets. While transformers with attention mechanisms have demonstrated strong capability in capturing complex dependencies across various domains, their application for modeling spatial dependencies within CPT data has not been fully explored. Inspired by the training strategies and architecture of large language models, we propose a novel geotechnical self-supervised learning framework (GSSLF) to reconstruct the subsurface soil properties at untested locations from incomplete and sparsely distributed CPT data. GSSLF integrates a self-supervised masking strategy to augment limited training data and employs a tailored transformer-based architecture (GeoFormer) with a modified attention mechanism to adaptively capture spatial dependencies. A heteroscedastic model is also incorporated to provide reliable probabilistic predictions and quantify associated uncertainties. GSSLF is shown to deliver competitive performance with robust accuracy and reliable uncertainty estimation, when compared to other recently developed data-driven methods (GLasso, ASSD-BCS, GeoWarp, and BNN-BO) on three benchmark datasets from literature. It achieves this with high computational efficiency, enabling efficient high-resolution three-dimensional subsurface modeling. Moreover, incorporating relative positional encoding is found to be preferable for attention-based deep learning in limited CPT data scenarios. These findings highlight the potential of GSSLF as a robust and efficient tool for subsurface modeling, facilitating both deterministic and reliability-based geotechnical design and planning.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107986"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385395","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}

{"title":"Hypoplastic modeling of sand behavior accounting for memory surface and evolving fabric anisotropy under cyclic loading conditions","authors":"Yikang Wang ,&nbsp;Fayun Liang ,&nbsp;Hao Zhang","doi":"10.1016/j.compgeo.2026.108001","DOIUrl":"10.1016/j.compgeo.2026.108001","url":null,"abstract":"<div><div>Modeling the cyclic response of granular materials is a critical issue in geotechnical engineering design. Over the past decades, numerous constitutive models have been proposed to capture the cyclic behavior of granular soils. Among them, hypoplasticity has been widely adopted in soil constitutive modeling, owing to its capability to describe key features—including nonlinear stress–strain relations, dilatancy, and critical state failure—through a single tensorial equation. Nevertheless, existing hypoplastic models and their extensions still exhibit limitations, particularly in reproducing soil responses during unloading and reloading phases under both drained and undrained conditions. To address these challenges, this study proposes a hypoplastic model for granular soils that integrates the memory surface (MS) concept with the anisotropic critical state theory (ACST), thereby enhancing simulations capability under cyclic loading. Building upon the intergranular strain (IS) concept, the ACST-MS-IS hypoplastic model introduces a memory surface defined in terms of dissipation energy to account for stress and strain history. Furthermore, an evolving deviatoric fabric tensor and a scalar anisotropic variable are incorporated to characterize the influence of anisotropy on dilatancy and shear strength. The results demonstrate that the proposed ACST-MS-IS hypoplastic model can satisfactorily reproduce experimental observations across a wide range of conditions, including both drained and undrained loadings, different stress paths, initial states, and relative densities of sand. This model will serve as a valuable reference for the design and assessment of practical engineering problems in road, offshore, and bridge projects under both drained and undrained cyclic loading conditions.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 108001"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385398","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}

{"title":"Explainable machine learning and generative diffusion modeling for improved susceptibility mapping of rainfall-induced clustered landslides: A case study from Wuping County, southeastern China","authors":"Yu Huang ,&nbsp;Yinke Li ,&nbsp;Lisha Hei ,&nbsp;Jialing Zou ,&nbsp;Dingyu Chen","doi":"10.1016/j.compgeo.2026.107989","DOIUrl":"10.1016/j.compgeo.2026.107989","url":null,"abstract":"<div><div>Rainfall-induced clustered landslides have become increasingly frequent in southeastern China, characterized by strong multi-factor coupling and posing significant threats to local communities and infrastructure. Taking Wuping County, Fujian Province, as a representative case, this study established a post-event inventory of 6,005 shallow landslides triggered by the extreme rainfall on 15–16 June 2024. An equal number of non-landslide samples were generated, and eleven topographic, geological, hydrological, vegetation, and anthropogenic factors were compiled into a 12.5 m-resolution dataset (training/testing = 7:3). Based on station-observed rainfall data, a structural rainfall analysis revealed that the landslide clustering was jointly triggered by the dual mechanisms of “antecedent rainfall accumulation” and “short-duration high-intensity pulses.” A comprehensive factor quality assessment was performed, including multicollinearity analysis (VIF &lt; 5, TOL &gt; 0.1) and Pearson correlation screening, confirming the independence and reliability of the conditioning factors prior to modeling. Six models—SVC-GridSearch, SVC-Bayes, SVC-GWO, SVC-PSO, Random Forest, and XGBoost—were then developed and compared, with SHAP analysis used to enhance interpretability and cross-validate with IGR results. The XGBoost model achieved the best performance on the test set (AUC ≈ 0.915). To address class boundary ambiguity, a Denoising Diffusion Probabilistic Model (DDPM) was further introduced for controlled data augmentation in the 11-dimensional factor space, generating about 12% of targeted samples within the model’s “confusion zone” (predicted probability 0.45–0.55). After augmentation, the XGBoost AUC increased to ≈ 0.931, with a significant DeLong test result (p &lt; 0.01), improved sensitivity, and narrower confidence intervals. The proposed hybrid framework of explainable machine learning and generative probabilistic modeling effectively enhances susceptibility mapping accuracy under limited-sample conditions and provides technical support for risk assessment, emergency control, and mitigation planning in southeastern mountainous regions.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107989"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385401","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}

{"title":"A dual mortar method for analyzing the effects of wave-induced instantaneous liquefaction on an immersed tunnel with a liquefaction-associated non-Darcy flow model","authors":"Shichong Han ,&nbsp;Mozhen Zhou ,&nbsp;Tielin Chen ,&nbsp;Dingli Zhang ,&nbsp;Wengang Qi","doi":"10.1016/j.compgeo.2026.107988","DOIUrl":"10.1016/j.compgeo.2026.107988","url":null,"abstract":"<div><div>Engineering practices have illustrated that ocean waves can cause non-ignorable deformations in immersed tunnels and the surrounding seabed. Under extreme wave conditions, instantaneous liquefaction of the seabed can occur, leading to a decrease in the bearing capacity of the seabed and potential damage to subsea structures. The liquefaction-associated non-Darcy flow model, previously proposed to eliminate the nonphysical tensile behavior of the seabed, is incorporated into a wave–seabed–tunnel model, which introduces special interfacial conditions between the seabed and the immersed tunnel. These interfacial conditions are numerically treated by developing a dual mortar method, which also permits using nonconforming meshes between the seabed and the tunnel. This model is applied to investigate the wave-induced response of an immersed tunnel fully buried in a non-cohesive seabed. The representative values of wave parameters are obtained by analyzing in-situ monitoring data from coastal stations, and then used as the input of the wave–seabed–tunnel model. The numerical results show that extreme waves can pose significant influences on the immersed tunnel.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107988"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385840","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}

{"title":"Studying the mechanical behaviour of an anisotropic clay rock around gallery intersections and the effect of the support stiffness","authors":"Panteleimon Rapanakis ,&nbsp;Benoît Pardoen ,&nbsp;Denis Branque ,&nbsp;Jan S. Cornet ,&nbsp;Gilles Armand","doi":"10.1016/j.compgeo.2026.107945","DOIUrl":"10.1016/j.compgeo.2026.107945","url":null,"abstract":"<div><div>Gallery intersections are frequently encountered in underground construction of deep geological repositories for nuclear waste. Their three-dimensional nature, combined with the excavation sequence and primary support installation, makes them a complex challenge to address. The redistribution of stress and strain that occurs, plays a key role in the response of the surrounding rock. Furthermore, a high in-situ stress state and an anisotropic nature of the material could have a substantial influence on the response of the rock. In this context, the present study investigates the behaviour of perpendicularly intersecting supported galleries excavated at great depth in an anisotropic clay rock through 3D finite element analyses. A conventional step-by-step progressive excavation is modelled as well as the phasing of the intersection excavation. The surrounding rock is a sedimentary indurated claystone, the Callovo-Oxfordian (COx) clay rock, which is assumed to follow an anisotropic Drucker-Prager elastoplastic constitutive law with shear strength hardening. Different stiffnesses for the elastic support are used to account for a flexible, an intermediate, and a more rigid support. The obtained results focus on the impact of each support stiffness on the stress distribution, plastic strain, and the generated plastic zone in the surrounding rock mass.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107945"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174290","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}

{"title":"Crack initiation and propagation mechanism of borehole surrounding rock subjected to cyclic thermal loading: insights from theoretical solution and DEM simulation","authors":"Qiuxin Gu ,&nbsp;Qiang Zhang ,&nbsp;Kai Zhang ,&nbsp;Hui Liu ,&nbsp;Yihan Du ,&nbsp;Bo Huang ,&nbsp;Wei Han","doi":"10.1016/j.compgeo.2026.107943","DOIUrl":"10.1016/j.compgeo.2026.107943","url":null,"abstract":"<div><div>The hydraulic fracturing and low-temperature thermal stimulation are commonly adopted for the construction of geothermal reservoirs in hot dry rock (HDR). However, it is hardly involved in the existing research on the variation law of the temperature and stress fields in the surrounding rock and the crack initiation propagation mechanism when the cooling fluid flows through the borehole. In this study, the unsteady temperature and stress field of the borehole surrounding rock during the cooling process was derived and solved firstly using the heat transfer and elasticity mechanics theories. Then, the crack initiation and propagation criteria for the borehole surrounding rock are proposed according to the fracture mechanics theory. Finally, the initiation and propagation laws of thermal cracks in the borehole surrounding rock under cyclic thermal shock are investigated through the discrete element method. The results reveal that when the cooling fluid is injected into the borehole, the temperature of the rock around the borehole drops the fastest. As the distance from the borehole increases, the temperature gradually rises and gets closer to the initial rock temperature. The temperature variation of the surrounding rock is closely related to the duration of thermal shock. During the initial stage of liquid nitrogen injection, the temperature drop is the most obvious. With the increase in thermal shock time, the tangential stress transitions from compressive stress to tensile stress. The tensile stress is the largest at the edge of the borehole, which is the location most prone to cracking. The mesoscopic cracking characteristics of the borehole surrounding rock are influenced by multiple factors, including buried depth, initial temperature, cooling method, thermal cycles, and the mesoscopic composition features of HDR. These research findings provide significant theoretical reference for EGS reservoir construction and high-efficiency stable operation</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107943"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174472","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}

{"title":"Research on catastrophic process of large-scale slope using 3D-DDA with machine learning-based parameter optimization","authors":"Jingyu Kang ,&nbsp;Xiaodong Fu ,&nbsp;Hao Sheng ,&nbsp;Jian Chen ,&nbsp;Xu Cheng","doi":"10.1016/j.compgeo.2026.107969","DOIUrl":"10.1016/j.compgeo.2026.107969","url":null,"abstract":"<div><div>The investigations on the entire process of slope instability holds significant implications for disaster prevention. Discontinuous deformation analysis (DDA) serves as a robust numerical tool capable of capturing dynamic interactions among rock blocks, making it particularly suitable for landslide simulation. However, due to the low computational efficiency and complex parameter determination, its application to large-scale landslides remains challenging. In this study, we employ parallel computing and machine learning to enhance three dimensional (3D) DDA for large-scale landslide deduction. Firstly, the OpenMP parallel strategy is adopted in the full stage of 3D explicit DDA, and the accuracy and efficiency are validated by a designed case. Then, a surrogate model based on the Light Gradient Boosting Machine (LightGBM) and Bayesian optimization is proposed to determine the subjective parameters in simulation. The training data involving common interaction modes in landslide are obtained by 3D-DDA and the performance of the surrogate model are evaluated by various indicators. Finally, a large-scale landslide is comprehensively analyzed based on the enhanced 3D DDA. The digital elevation of the landslide is obtained by unmanned aerial vehicle, and the numerical model composed of over ten thousands blocks is established. The time step and contact stiffness is provided by the surrogate model. The catastrophic process of the landslide is reproduced and the kinematic characteristics are comprehensively investigated. The present study enhances the capability of 3D DDA in large-scale simulation and can also be referenced by related discontinuum-based methods.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107969"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174464","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}

{"title":"Utilizing physics-informed neural network and geotechnical distance field for solving three-dimensional nonlinear consolidation","authors":"Tran-Gia-Khiem Nguyen,&nbsp;Jongmuk Won","doi":"10.1016/j.compgeo.2026.107926","DOIUrl":"10.1016/j.compgeo.2026.107926","url":null,"abstract":"<div><div>Solving three-dimensional (3D) nonlinear consolidation is complex and computationally expensive. This study proposes a framework for solving 3D nonlinear consolidation by utilizing an improved physics-informed neural network with hard constraints coupling with machine learning based geotechnical distance functions for three-dimensional spatial interpolation. The performance of the developed framework was assessed by comparing pore water pressure data between the developed framework and those obtained from COMSOL Multiphysics. In addition, the impact of vertical hydraulic conductibility heterogeneity, compression index, and void ratio on long-term settlement was also evaluated and discussed. It was found that the proposed framework showed a reliable estimation of the 3D distribution of pore water pressure across the 3D domain, achieving results that are comparable to data obtained from COMSOL. In addition, the heterogeneity of hydraulic conductivity can be successfully considered using the developed framework, which enables assessing the long-term settlement of a clay deposit with high uncertainty of hydraulic conductivity. Overall, the developed framework shown in this study can be applied to complex consolidation problems with low computational costs and high accuracy.</div></div>","PeriodicalId":55217,"journal":{"name":"Computers and Geotechnics","volume":"193 ","pages":"Article 107926"},"PeriodicalIF":6.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026134","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}