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Environmental Issues in Geotechnical Engineering
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Environmental Issues in Geotechnical Engineering

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 1181

Special Issue Editors

Prof. Dr. Haowen Guo

E-Mail Website
Guest Editor
School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen (HITSZ), Shenzhen 518000, China
Interests: unsaturated soil mechanics; eco and environmental geotechnics; low-carbon disposal and utilisation of solid waste; geotechnical disasters prevention and risk-driven warning; lifecycle resilience and intelligent perception of geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaofeng Li

E-Mail Website
Guest Editor
School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen Campus, Shenzhen 518055, China
Interests: soil–pile interaction; DEM simulation; particle breakage; micromechanics; suffusion
Special Issues, Collections and Topics in MDPI journals
Dr. Pui San So

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
Interests: unsaturated soil behaviour; soil remediation using biopolymers and biochar; soil-plant interactions; waste valorization

Special Issue Information

Dear Colleagues,

Amid escalating climate change and intensifying environmental degradation, environmental geotechnics has become pivotal in addressing geotechnical engineering’s role in ecological resilience. From waste disposal, soil contamination, and land degradation to geohazards worsened by climate variability, existing practices demand innovation—especially regarding the integration of artificial intelligence (AI).

This Special Issue seeks to advance research at the intersection of environmental geotechnics, focusing on sustainable solutions. We invite contributions spanning fundamental studies to practical applications, covering topics such as waste recycling in geotechnical systems, climate-resilient geotechnical design, pollution remediation innovations, carbon-negative geotechnics, eco-geotechnics, renewable energy–geotechnics integration, AI-driven site characterisation, predictive modelling for environmental risk, and optimisation of low-carbon practices and other topics related to environmental geotechnics.

By bridging environmental challenges with AI-driven methodologies, this Special Issue aims to accelerate the transition toward sustainable geotechnical engineering, offer transformative tools to enhance efficiency, reduce ecological footprints, and foster eco-friendly geotechnical innovation.

Submissions from global researchers are welcome to help shape a roadmap towards environmentally responsible geotechnical practices.

Prof. Dr. Haowen Guo
Dr. Zhaofeng Li
Dr. Pui San So
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-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

  • waste recycling in geotechnical systems
  • climate-resilient geotechnical design
  • pollution remediation innovations
  • carbon-negative geotechnics
  • eco-geotechnics
  • renewable energy-geotechnics integration
  • AI-driven geoenvironmental solution
  • predictive modelling for environmental risk optimisation of low-carbon practices

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

Published Papers (2 papers)

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Research

16 pages, 5067 KB  
Article
Modeling of Water Quality in Deep Tunnels Coupling Temperature–Depth Effects
by Xiaomei Zhang, Qingmin Zhang, Yuanjing Yang, Yuntao Guan and Rui Chen
Appl. Sci. 2026, 16(8), 3664; https://doi.org/10.3390/app16083664 - 9 Apr 2026
Viewed by 216
Abstract
As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for [...] Read more.
As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for the operation and management of such systems. In this study, field experiments were carried out in the Qianhai–Nanshan Deep Tunnel to investigate complex water quality behavior, leading to the development of chemical oxygen demand (COD) and ammonia nitrogen (NH3–N) models that incorporate temporal variation, temperature, and burial depth. Results indicate that temperature is the dominant factor influencing water quality in deep tunnel storage. Increased ground temperature promotes the degradation and mass transport of pollutants within the tunnel system. Owing to temperature–depth effects, the deeply buried Qianhai tunnel significantly reduces river discharge pollution after water storage, with COD and NH3–N removal rates reaching 74.9% and 26.8%, respectively. Temperature-controlled experiments showed that COD and NH3–N reduction rates varied between 60–94% and 10–30% across a temperature range of 20–34 °C. The proposed model was validated against experimental data, achieving Nash–Sutcliffe efficiency coefficients of 0.7–0.8. This study provides a methodological foundation for simulating complex aquatic environments and offers a decision-support tool for optimizing the operational strategies of deep tunnel systems. However, the model’s current generalization capability is constrained by the limited experimental conditions (20–34 °C, 12 days) and the lack of experimental replicates, which should be systematically addressed in future studies. Full article
(This article belongs to the Special Issue Environmental Issues in Geotechnical Engineering)
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19 pages, 3410 KB  
Article
Green AI for Energy-Efficient Ground Investigation: A Greedy Algorithm-Optimized AI Model for Subsurface Data Prediction
by Siyuan Zhang, Zhili Li, Xiang Qiu, Yaohua Sui, Zhi Lan and Pei Tai
Appl. Sci. 2025, 15(22), 12012; https://doi.org/10.3390/app152212012 - 12 Nov 2025
Viewed by 588
Abstract
Current ground investigation practice in geotechnical engineering is highly energy- and time-intensive, which is environmentally unfriendly. Several weeks are usually required to perform the investigation, and along this process, acquiring just one parameter of a soil sample may consume more than 100 Wh. [...] Read more.
Current ground investigation practice in geotechnical engineering is highly energy- and time-intensive, which is environmentally unfriendly. Several weeks are usually required to perform the investigation, and along this process, acquiring just one parameter of a soil sample may consume more than 100 Wh. Addressing this challenge, this study aimed at reducing energy consumption of ground investigation following Green AI concept, and an AI model for subsurface data prediction, which was optimized with greedy algorithm, was therefore developed. This model inputs soil parameters obtained by low-energy methods and outputs soil parameters (i.e., shear strength parameters) obtained by high-energy methods in traditional ground investigation practice. The model was established using ensemble learning techniques and a large dataset with fifteen types of parameters, which was obtained from 924 samples via a series of laboratory tests in a traditional ground investigation. Meanwhile, considering the variability on ensemble model architecture, greedy algorithm was adopted to optimize the architecture from a pool of nine base learners and six ensemble strategies. Thereby, the best-performing AI model was eventually identified, and it achieved a R2 value of 0.881 over the testing dataset, which accounts for 30% of the overall parameter dataset. With this model, the energy consumption needed to acquire the subsurface data was significantly reduced by 71%. The findings of this study pave the way for a lower-carbon but more intelligent ground investigation. Full article
(This article belongs to the Special Issue Environmental Issues in Geotechnical Engineering)
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