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Environmental Geotechnical Engineering and Geological Disasters
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Environmental Geotechnical Engineering and Geological Disasters

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 797

Special Issue Editors

Dr. Xianghong Ding

E-Mail Website
Guest Editor
School of Civil Engineering, Central South University, Changsha 410075, China
Interests: environmental geotechnics; contaminant transport; soil and groundwater remediation
Dr. Huaxiang Yan

E-Mail Website
Guest Editor
Zijin School of Geology and Mining, Fuzhou University, Fuzhou 350108, China
Interests: subsurface pollution prevention and control; non-local numerical methods modeling; reactive flow in porous media; deep learning based on physical information
Dr. Biao Luo

E-Mail Website
Guest Editor Assistant
College of Civil Engineering, Hunan University, Changsha 410082, China
Interests: environmental geotechnical engineering; solid waste utilization; sustainability; soft soil foundation treatment
Dr. Wenle Hu

E-Mail Website
Guest Editor Assistant
1. School of Civil Engineering and Architecture, Henan University, Kaifeng 475001, China
2. School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710064, China
Interests: loess; electrokinetic remediation; microscale structural; macroscale mechanical properties; contaminated soil

Special Issue Information

Dear Colleagues,

Environmental geotechnical engineering, geodynamics, and engineering geology are critical to addressing the cross-cutting challenges between infrastructure development, Earth's dynamic systems, and ecological sustainability. This Special Issue emphasizes innovative solutions that integrate geotechnical engineering with environmental management, geological insights, and seismic resilience, focusing on soil behavior and hazard mitigation.

We welcome contributions advancing environmental geotechnics, including sustainable soil stabilization, bioremediation, and low-carbon materials, as well as studies exploring geodynamic phenomena such as subsidence, landslides, and earthquake-induced hazards (e.g., liquefaction, seismic site response).

Key topics include:

  • Contaminated site remediation: Strategies for managing heavy metals, hydrocarbons, and industrial pollutants in soil and groundwater.
  • Ground improvement and waste containment: Eco-conscious methods for landfill design, soil reinforcement, and hazardous material isolation.
  • Slope stability and landslides: Sustainable mitigation of slope failures exacerbated by earthquakes, rainfall, or anthropogenic activities.
  • Engineering geology challenges: Site characterization, acid mine drainage, geological hazard assessment, and resource extraction impacts.
  • Sustainable urban geotechnics: Managing subsidence, erosion, and urban heat island effects through green infrastructure.

This Special Issue aims to bridge geotechnical innovations, geological insights, and earthquake engineering to promote solutions that balance infrastructure safety, environmental protection, and dynamic earth processes. Contributions may include original research, case studies, and reviews involving theoretical, experimental, or field-based approaches.

Dr. Xianghong Ding
Dr. Huaxiang Yan
Guest Editors

Dr. Biao Luo
Dr. Wenle Hu
Guest Editors Assistants

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. 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

  • environmental geotechnical engineering
  • geological disasters
  • contaminated sites
  • slope stability
  • earthquake
  • sustainable geomaterials
  • eco-engineering

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

Published Papers (1 paper)

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Research

19 pages, 3332 KiB  
Article
Prediction on Permeability Coefficient of Continuously Graded Coarse-Grained Soils: A Data-Driven Machine Learning Method
by Jinhua Wang, Haibin Ding, Lingxiao Guan and Yulin Wang
Appl. Sci. 2025, 15(10), 5248; https://doi.org/10.3390/app15105248 - 8 May 2025
Viewed by 469
Abstract
Accurately predicting the permeability of coarse-grained soils is crucial for ensuring geotechnical safety and performance. In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics–discrete element method (CFD-DEM) coupled seepage simulations were [...] Read more.
Accurately predicting the permeability of coarse-grained soils is crucial for ensuring geotechnical safety and performance. In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics–discrete element method (CFD-DEM) coupled seepage simulations were conducted to generate a permeability coefficient (k) dataset comprising 256 entries under varying porosity and gradation conditions. Three machine learning models—a neural network model (BPNN), a regression model (GPR), and a tree-based model (RF)—were employed to predict k, with hyperparameters optimized via particle swarm optimization (PSO) and four-fold cross-validation applied to improve generalization. Gray relational analysis (GRA) revealed that all input parameters (α, β, dmax, n) significantly influence k (R > 0.6). The interquartile range (IQR) method confirmed data suitability for modeling. Among the models, BPNN achieved the best performance (R2 = 0.99, MAE = 1.5, RMSE = 2.9, U95 = 0.4), effectively capturing the complex nonlinear relationship between gradation and permeability. GPR (R2 = 0.92) was hindered by kernel selection and noise sensitivity, while RF (R2 = 0.97) was limited by its discrete regression nature. Compared to a traditional empirical model (R2 = 0.9031), BPNN improved prediction accuracy by 10.13%, demonstrating the advantage of data-driven methods for evaluating CGS permeability. Full article
(This article belongs to the Special Issue Environmental Geotechnical Engineering and Geological Disasters)
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