Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.9
Journals
Applied Sciences
Special Issues
Recent Advances in Applied Numerical Methods and Artificial Intelligence for...
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances in Applied Numerical Methods and Artificial Intelligence for Underground Engineering

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Earth Sciences".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1382

Editors

Dr. Zheng Yang

E-Mail Website
Guest Editor
Geotechnical Institute, TU Bergakademie Freiberg, 09599 Freiberg, Germany
Interests: numerical analysis; coupled multi-physics processes; underground engineering; rock mechanics
Dr. Yanliang Li

E-Mail Website
Guest Editor
College of Construction Engineering, Jilin University, Changchun 130012, China
Interests: crack characterization; thermal cracking; rock fragmentation; underground engineering
Dr. Chuanqi Li

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: high-performance concrete; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Underground engineering encompasses a wide range of applications, including urban infrastructure, mining, and petroleum engineering, all of which play critical roles in resource utilization, energy development, and sustainable infrastructure. These systems are often characterized by complex geological conditions, high in situ stresses, and strongly coupled thermo-hydro-mechanical processes, posing significant challenges for analysis, design, and operation.

To address these challenges, artificial intelligence (AI), together with advanced numerical simulation and computational methods, has emerged as a powerful approach for analyzing complex datasets and improving our understanding and prediction of rock behavior and underground system performance. In practical applications, these approaches have been widely used in tunnel excavation optimization, underground space design, reservoir characterization, real-time monitoring, and geohazard prediction, among others. They enable more efficient data-driven decision-making and support intelligent construction and operation in complex underground environments.

As a result, AI and advanced numerical computation are becoming important research directions for improving the safety, efficiency, and sustainability of underground engineering.

This Special Issue aims to showcase innovative numerical methods and AI-driven approaches for underground engineering applications.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Multi-physics and multi-scale modeling of underground systems;
  • Data-driven analysis of rock behavior and failure processes;
  • Intelligent monitoring, prediction, and early warning in underground projects;
  • Applications of AI in mining, tunneling, and subsurface energy systems;
  • Digital twins and smart underground infrastructure.

Dr. Zheng Yang
Dr. Yanliang Li
Dr. Chuanqi Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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

  • underground engineering
  • numerical simulation
  • artificial intelligence
  • intelligent monitoring
  • thermo-hydro-mechanical-chemical (THMC) coupling
  • multi-scale modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 4069 KB  
Article
Numerical Investigation of Hydrothermal Response and Moisture Migration in a Seasonally Frozen Highway Slope
by Wei Xian, Fuerhaiti Ainiwaer, Xiaomin Dai and Liang Song
Appl. Sci. 2026, 16(12), 6072; https://doi.org/10.3390/app16126072 - 16 Jun 2026
Cited by 1 | Viewed by 394
Abstract
In the seasonally frozen area, slopes are exposed to freeze–thaw cycles; thus, water and heat are moved, and the foundation for the transportation infrastructure in cold regions may be weakened. Based on the relatively strong water-recharge effect and considerable fluctuations in shallow soil [...] Read more.
In the seasonally frozen area, slopes are exposed to freeze–thaw cycles; thus, water and heat are moved, and the foundation for the transportation infrastructure in cold regions may be weakened. Based on the relatively strong water-recharge effect and considerable fluctuations in shallow soil moisture during the spring thaw along the Naba section of the G218 Highway in Xinjiang, China, a coupled hydro-thermal model for frozen soil that considers snowmelt infiltration and rainfall recharge was developed, and it was numerically implemented in COMSOL. A one-dimensional unidirectional freezing test of a soil column was used to validate the model, and the relative errors of the simulated temperature and moisture fields were 3.8% and 4.3%, respectively; both are within the accuracy requirements for engineering-scale analysis. Then, a model was used to determine how the temperature, volumetric ice content and volumetric water content of a representative slope in the Naba section changed during a freeze–thaw cycle. Based on the above results, the annual temperature range at the surface of the topsoil on the slope is 37.61 °C, and this thermal effect extends to a depth of 0–3 m. In the spring thaw, the volumetric water content of the surface layer increased from 8.45% in February to 19.34% in May, and further to 20.65% in July; therefore, it can be inferred that the shallow soil is still being replenished by snowmelt and rain. Freezing-thaw phase change, freezing-front migration and external water infiltration work together to control hydro-thermal transport in the slope; thus, a redistribution and local accumulation of liquid water occur below the residual frozen layer and under the shallow surface. The above results can serve as a reference for drainage design and as a means to prevent or control freeze–thaw damage to the slope of a highway in Xinjiang’s seasonally frozen area during the spring thaw. Full article
(This article belongs to the Special Issue Recent Advances in Applied Numerical Methods and Artificial Intelligence for Underground Engineering)
►▼ Show Figures

Figure 1

21 pages, 4529 KB  
Article
A High-Performance Model for Landslide Geological Hazard Detection, CDCS-YOLO
by Zijie Ye, Fuerhaiti Ainiwaer, Dongchen Han, Xinjun Song, Fulin Qu, Yuxi Wang, Xiaomin Dai and Shengqiang Ma
Appl. Sci. 2026, 16(10), 4804; https://doi.org/10.3390/app16104804 - 12 May 2026
Viewed by 527
Abstract
Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new framework for landslide detection by comparing various [...] Read more.
Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new framework for landslide detection by comparing various YOLO models. It employs deformable convolutional modules combined with GhostConv modules to enhance feature extraction for landslide targets. The framework uses a structured IoU loss function to optimize the alignment of actual and predicted frames in a directional sense. Additionally, it introduces the CoordAtt attention mechanism to accelerate model convergence and improve training efficiency. The experimental results demonstrate that the enhanced YOLO model (CDCS-YOLO), incorporating four key enhancement modules (Coordinate Attention, Deformable Convolutional Networks, the C3 Module/CSP Architecture and SIoU Loss), achieved a maximum mAP of 96.6%, an accuracy of 96.1%, and a frame rate of 142.6 FPS. Notably, it performed exceptionally well in soil landslide detection, achieving an average detection accuracy surpassing 90%. Based on the experimental results, we explored a morphological landslide classification method further as well as a multi-source differential monitoring strategy integrating UAV imagery, field surveys, ground-based LiDAR data, rainfall information and deformation indicators. The proposed method outperforms the baseline approach and is a promising solution for detecting landslides and geological hazards in Xinjiang. Full article
(This article belongs to the Special Issue Recent Advances in Applied Numerical Methods and Artificial Intelligence for Underground Engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop