期刊：Geotechnics

主页：https://www.mdpi.com/journal/geotechnics

本期文章聚焦岩土工程智能感知与性能预测的前沿探索，展示机器学习、数字孪生及概率建模等方法如何赋能地质条件的精准判识与工程性能优化，涵盖AI驱动的边坡稳定性动态评估、TBM掘进效率智能优化、地层结构自动识别、钻孔数据智能解译及基础设施长期性能预测等应用场景，同时涉及地热开发、CO₂地质封存及矿山环境修复中的多场过程模拟与参数不确定性分析，旨在打通"数据-模型-性能评估"的技术链条，为岩土工程的勘察设计优化、施工风险预判及全寿命周期管理提供智能化决策工具。

Perspectives of 3D Probabilistic Subsoil Modeling for BIM

建筑信息模型环境下三维概率地基建模展望

https://www.mdpi.com/2673-7094/3/4/58

Wiegel, A.; Peña-Olarte, A.A.; Cudmani, R. Perspectives of 3D Probabilistic Subsoil Modeling for BIM. Geotechnics 2023, 3, 1069-1084. https://doi.org/10.3390/geotechnics3040058

2.

Back-Analysis of Structurally Controlled Failure in an Open-Pit Mine with Machine Learning Tools机器学习辅助的露天矿结构控制型破坏反分析

https://www.mdpi.com/2673-7094/3/4/66

McQuillan, A.; Mitelman, A.; Elmo, D. Back-Analysis of Structurally Controlled Failure in an Open-Pit Mine with Machine Learning Tools. Geotechnics 2023, 3, 1207-1218. https://doi.org/10.3390/geotechnics3040066

3.

Experimental and Numerical Analysis of Laterally Loaded Single- and Double-Paddled H-Piles in Clay

黏土中横向受荷单桨与双桨H型桩的试验及数值分析

https://www.mdpi.com/2673-7094/3/4/72

Abouziad, A.; El Naggar, M.H. Experimental and Numerical Analysis of Laterally Loaded Single- and Double-Paddled H-Piles in Clay. Geotechnics 2023, 3, 1324-1345. https://doi.org/10.3390/geotechnics3040072

4.

Advancements in Understanding Interface Friction: A Combined Experimental and Machine Learning Approach Using Multiple Linear and Random Forest Regressions

界面摩擦机理研究进展：多元线性回归与随机森林联合的试验-机器学习方法

https://www.mdpi.com/2673-7094/4/1/6

Daghistani, F.; Abuel-Naga, H. Advancements in Understanding Interface Friction: A Combined Experimental and Machine Learning Approach Using Multiple Linear and Random Forest Regressions. Geotechnics 2024, 4, 109-126. https://doi.org/10.3390/geotechnics4010006

5.

Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering

TBM性能提升：岩土工程中盾构摩擦分析与机器学习的融合应用

https://www.mdpi.com/2673-7094/4/1/10

Schlicke, M.; Wannenmacher, H.; Nübel, K. Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering. Geotechnics 2024, 4, 194-208. https://doi.org/10.3390/geotechnics4010010

6.

A State-of-the-Art Review on Computational Modeling of Dynamic Soil–Structure Interaction in Crash Test Simulations

碰撞试验模拟中动态土-结构相互作用计算建模研究进展

https://www.mdpi.com/2673-7094/4/1/7

Yosef, T.Y.; Faller, R.K.; Fang, C.; Kim, S. A State-of-the-Art Review on Computational Modeling of Dynamic Soil–Structure Interaction in Crash Test Simulations. Geotechnics 2024, 4, 127-157. https://doi.org/10.3390/geotechnics4010007

7.

Assessment of Bayesian Changepoint Detection Methods for Soil Layering Identification Using Cone Penetration Test Data

基于静力触探数据的土层识别贝叶斯变点检测方法评估

https://www.mdpi.com/2673-7094/4/2/21

Suryasentana, S.K.; Sheil, B.B.; Lawler, M. Assessment of Bayesian Changepoint Detection Methods for Soil Layering Identification Using Cone Penetration Test Data. Geotechnics 2024, 4, 382-398. https://doi.org/10.3390/geotechnics4020021

8.

An Investigation into the Utility of Large Language Models in Geotechnical Education and Problem Solving

大语言模型在岩土工程教育与问题求解中的实用性研究

https://www.mdpi.com/2673-7094/4/2/26

Chen, L.; Tophel, A.; Hettiyadura, U.; Kodikara, J. An Investigation into the Utility of Large Language Models in Geotechnical Education and Problem Solving. Geotechnics 2024, 4, 470-498. https://doi.org/10.3390/geotechnics4020026

9.

Machine Learning Analysis of Borehole Data for Geotechnical Insights

钻孔数据机器学习分析及其岩土工程应用

https://www.mdpi.com/2673-7094/4/4/60

Mitelman, A. Machine Learning Analysis of Borehole Data for Geotechnical Insights. Geotechnics 2024, 4, 1175-1188. https://doi.org/10.3390/geotechnics4040060

10.

AI-Powered Digital Twin Technology for Highway System Slope Stability Risk Monitoring

公路系统边坡稳定性风险监测的AI驱动数字孪生技术

https://www.mdpi.com/2673-7094/5/1/19

Xu, J.; Zhang, Y. AI-Powered Digital Twin Technology for Highway System Slope Stability Risk Monitoring. Geotechnics 2025, 5, 19. https://doi.org/10.3390/geotechnics5010019

11.

Mathematical Modeling of the Rail Track Superstructure–Subgrade System

铁路轨道上部结构-路基系统的数学建模

https://www.mdpi.com/2673-7094/5/1/20

Kurhan, D.; Fischer, S.; Khmelevskyi, V. Mathematical Modeling of the Rail Track Superstructure–Subgrade System. Geotechnics 2025, 5, 20. https://doi.org/10.3390/geotechnics5010020

12.

Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data

基于现场仪器数据的土壤电阻率机器学习建模方法

https://www.mdpi.com/2673-7094/5/1/5

Alam, M.J.B.; Gunda, A.; Ahmed, A. Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data. Geotechnics 2025, 5, 5. https://doi.org/10.3390/geotechnics5010005

13.

Improving Data Quality with Advanced Pre-Processing of MWD Data

随钻测量数据高级预处理技术与数据质量提升

https://www.mdpi.com/2673-7094/5/2/28

Sapronova, A.; Marcher, T. Improving Data Quality with Advanced Pre-Processing of MWD Data. Geotechnics 2025, 5, 28. https://doi.org/10.3390/geotechnics5020028

14.

A Review of Soil Constitutive Models for Simulating Dynamic Soil–Structure Interaction Processes Under Impact Loading

冲击荷载下土-结构相互作用过程模拟的土体本构模型综述

https://www.mdpi.com/2673-7094/5/2/40

Yosef, T.Y.; Fang, C.; Faller, R.K.; Kim, S.; Alomari, Q.A.; Atash Bahar, M.; Kumar, G.S. A Review of Soil Constitutive Models for Simulating Dynamic Soil–Structure Interaction Processes Under Impact Loading. Geotechnics 2025, 5, 40. https://doi.org/10.3390/geotechnics5020040

15.

Machine Learning–Enhanced Modeling of Stress–Strain Behavior of Frozen Sandy Soil

冻砂应力-应变行为机器学习增强建模

https://www.mdpi.com/2673-7094/4/4/62

Rezazadeh Eidgahee, D.; Shiri, H. Machine Learning–Enhanced Modeling of Stress–Strain Behavior of Frozen Sandy Soil. Geotechnics 2024, 4, 1228-1245. https://doi.org/10.3390/geotechnics4040062

Geotechnics 期刊介绍

Geotechnics（ISSN 2673-7094）是一个国际性、跨学科、同行评审开放获取期刊。创刊于2021年，作者群体覆盖全球多个国家和地区，编委会由来自13个国家的43位资深学者组成，负责把控所发表文章的质量以及期刊的整体发展方向。目前，Geotechnics期刊已被Scopus、ESCI（Web of Science）、DOAJ等数据库收录，并持续提升其学术影响力。

期刊旨在发表与地学信息领域各方面相关的最新研究成果与综述论文，主题包括且不限于：

地基与土–结构相互作用；土体性质、改良与修复；土力学与岩石力学；地震工程；岩土材料的力学、物理、水力和热学性质；滑坡与边坡稳定；地质灾害；地下结构；岩土工程中的数值模拟与数据分析；环境岩土工程；水文地质；废弃物与废弃物管理

主编：

Prof. Dr. George Mylonakis

University of Bristol, UK and Khalifa University, UAE

2024 Impact Factor：1.9

2024 CiteScore：3.6

Time to First Decision：20.6 Days

Acceptance to Publication：4.8 Days