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Advances in Hydro-Thermal–Mechanical Coupling Geotechnical Engineering
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Advances in Hydro-Thermal–Mechanical Coupling Geotechnical Engineering

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 308

Special Issue Editors

Dr. Linjian Ma

E-Mail Website
Guest Editor
State Key Laboratory of Disaster Prevention and Mitigation of Explosion and Impact, Army Engineering University of PLA, Nanjing 210007, China
Interests: water–rock interaction; solid dynamic mechanics; long-term stability; frozen soils and rocks; hydro-thermal–mechanical-coupled constitutive modeling
Dr. Yun Wu

E-Mail Website
Guest Editor
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
Interests: hydro-thermal-mechanical coupling; site selection and evaluation of nuclear waste; long term stability of underground gas storage facilities; geothermal resource development; intelligent monitoring of engineering rock mass seepage

Special Issue Information

Dear Colleagues,

Hydro-thermal–mechanical coupling is a significant issue in geotechnical engineering that has implications for a wide range of applications. These applications include, for example, nuclear waste disposal, deep geo-thermal development, tunnel engineering, hydropower stations, frozen soil in cold regions, etc. Understanding the complex interactions between these processes is essential for designing safe and reliable geotechnical structures, managing natural resources, and addressing environmental concerns. Although many scholars have conducted extensive experiments and numerical analyses, and established theoretical models for hydro-thermal–mechanical coupling, which have greatly promoted the development of multi-field coupling theory for rock and soil, there are still some problems, including the accuracy of the experimental equipment, boundary and convergence issues in numerical simulations, and the constitutive relationships of rock and soil materials. Continued research and development in numerical modeling, experimental techniques, and field monitoring are needed to improve our ability to predict and control the behavior of geomaterials under coupled thermal, hydraulic, and mechanical loading. Potential topics include, but are not limited to, the following:

  • Hydro-thermal–mechanical-coupled constitutive modeling;
  • New methods and technology for rock mechanics experiments;
  • Research on rock dynamics in cold regions;
  • Long-term stability of underground engineering;
  • Mechanism of water rock interaction;
  • Grouting reinforcement of fractured rock mass.

Dr. Linjian Ma
Dr. Yun Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydro-thermal–mechanical coupling
  • rock mechanics
  • cold regions
  • underground engineering
  • deterioration and deg-radation
  • grouting
  • constitutive model
  • geological stability
  • fatigue and creep
  • disaster prevention

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Published Papers (1 paper)

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Research

23 pages, 2997 KB  
Article
Improving a Prediction Model for Tunnel Water Inflow Estimation Using LSTM and Bayesian Optimization
by Zhen Huang, Zishuai Yang, Yun Wu, Lijian Ma, Tao Sun, Zhenpeng Wang, Kui Zhao, Xiaojun Zhang, Haigang Li and Yu Zheng
Water 2025, 17(21), 3045; https://doi.org/10.3390/w17213045 - 23 Oct 2025
Abstract
Water inrush and mud burst disasters pose severe challenges to the safe and efficient construction of underground engineering. Water inflow prediction is closely related to drainage design, disaster prevention and control, and the safety of the surrounding ecological environment. Thus, assessing the water [...] Read more.
Water inrush and mud burst disasters pose severe challenges to the safe and efficient construction of underground engineering. Water inflow prediction is closely related to drainage design, disaster prevention and control, and the safety of the surrounding ecological environment. Thus, assessing the water inflow accurately is of importance. This study proposes a Bayesian Optimization-Long Short-Term Memory (BOA-LSTM) recurrent neural network for predicting tunnel water inflow. The model is based on four input parameters, namely tunnel depth (H), groundwater level (h), rock quality designation (RQD), and water-richness (W), with water inflow (WI) as the single-output variable. The model first processes and analyzes the data, quantitatively characterizing the correlations between input parameters. The tunnel water inflow is predicted using the long short-term memory (LSTM) recurrent neural network, and the Bayesian optimization algorithm (BOA) is employed to select the hyperparameters of the LSTM, primarily including the number of hidden layer units, initial learning rate, and L2 regularization coefficient. The modeling process incorporates a five-fold cross-validation strategy for dataset partitioning, which effectively mitigates overfitting risks and enhances the model’s generalization capability. After a comprehensive comparison among a series of machine learning models, including a long short-term memory recurrent neural network (LSTM), random forest (RF), back propagation neural network (BP), extreme learning machine (ELM), radial basis function neural network (RBFNN), least squares support vector machine (LIBSVM), and convolutional neural network (CNN), BOA-LSTM performed excellently. The proposed BOA-LSTM model substantially surpasses the standard LSTM and other comparative models in tunnel water inflow prediction, demonstrating superior performance in both accuracy and generalization. Hence, it provides a reference basis for tunnel engineering water inflow prediction. Full article
(This article belongs to the Special Issue Advances in Hydro-Thermal–Mechanical Coupling Geotechnical Engineering)
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