Advances in Geohazard Mitigation and Adaptation

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geomechanics".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 938

Special Issue Editors


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Guest Editor
School of Engineering in the College of Sciences and Engineering, University of Tasmania, Hobart, Australia
Interests: geohazard mitigation; computational geomechanics; tunnelling and rock mechanics
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Guest Editor
School of Advanced Engineers, University of Science and Technology Beijing, Beijing, China
Interests: rock dynamics; stress wave propagation; rockburst; deep mining
Department of Civil Engineering, Yunnan University, Yunnan, China
Interests: advanced numerical simulation techniques; FDEM; rock fracture and fragmentation processes; rock dynamics and fracture mechanics; stability of complex underground engineering; deep rock excavation and control; slope stability analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The impressing challenges of climate change and the global shift toward clean, low‑carbon energy are driving unprecedented transformations in how we design, construct, and maintain critical infrastructure. As extreme weather intensifies and renewable‑energy technologies expand, geotechnical systems are being pushed into new environments and deeper geological settings, creating fresh challenges for hazard resilience and sustainable development.

These geotechnical systems may include urban tunnelling for resident mobility, large-scale excavations for critical minerals and energy storage, deep tunnelling for transport and hydro energy, deep wellbore for geothermal exploitation, subsea foundation for offshore wind development, and coastal adaptation infrastructure, all of which interact with complex geological and climate conditions. These interactions may manifest as rainfall‑triggered landslides, slope failures, tunnelling‑induced ground movement or collapse, deep‑excavation‑related microseismicity and rockbursts, geothermal wellbore breakout, hydraulic‑fracturing‑induced earthquakes, sea‑level‑rise‑driven coastal erosion, offshore foundation instability, and even cryospheric hazards such as Antarctic ice‑shelf fracture and instability. Understanding these evolving geohazards and developing robust mitigation and adaptation strategies are essential for ensuring the safety, resilience, and long‑term performance of critical infrastructure in a rapidly changing world.

This Special Issue aims to bring together cutting‑edge research, innovative technologies, and interdisciplinary approaches that enhance our ability to assess, monitor, model, and manage geohazards in the context of climate change and clean‑energy development. We welcome contributions including, but not limited to, the following topics:

  1. Climate‑Driven Geohazards
  • Rainfall‑induced landslides and debris flows.
  • Slope instability under extreme weather and hydrological shifts.
  • Permafrost degradation and associated ground hazards.
  • Coastal erosion, shoreline retreat, and deltaic instability under sea‑level rise.
  1. Geohazards in Clean‑Energy and Decarbonisation Projects
  • Geotechnical risks in deep geothermal systems (wellbore breakout, induced seismicity, etc).
  • Rockburst and microseismicity associated with deep excavation.
  • Geomechanical challenges in underground hydrogen, CO₂, or compressed‑air storage.
  • Hydraulic‑fracturing‑induced seismicity and reservoir deformation.
  • Offshore wind turbine foundation stability and seabed–structure interaction.
  1. Urban Infrastructure‑Related Geohazards
  • Urban tunnelling‑induced settlement, ground movement, and collapse.
  • Excavation‑related instability in megaprojects.
  • Ground–structure interaction under dynamic or cascade hazard conditions.
  • Early‑warning systems for underground construction hazards.
  1. Cryosphere and Polar Geohazards
  • Ice‑shelf fracture mechanics and large‑scale instability.
  • Glacier‑related hazards and their downstream impacts.
  • Geotechnical challenges in polar infrastructure development.
  1. Modelling, Simulation, and Data‑Driven Approaches
  • Numerical modelling of dynamic rock fracture and extreme events.
  • Multiphysics simulation of coupled hydro‑thermal–mechanical processes.
  • AI/ML‑enabled hazard prediction, clustering, and early‑warning algorithms.
  • Digital twins for geohazard‑prone infrastructure.
  • Probabilistic hazard assessment and uncertainty quantification.

Dr. Hongyuan Liu
Dr. Xianhui Feng
Dr. Haoyu Han
Guest Editors

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Keywords

  • geohazards
  • climate change
  • urban tunnelling-induced settlement and collapse
  • landslides and slope instability
  • deep tunnelling-induced microseismicities and rockbursts
  • coastal erosion
  • geothermal systems
  • offshore foundation stability
  • hazard mitigation and adaptation
  • iceshelf fracture and instability

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Published Papers (1 paper)

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Research

24 pages, 4631 KB  
Article
LLM-Powered Multi-Agent Framework for Automated PPV Prediction in Tunnel Blasting
by Jian Xu, Haiping Fan and Danial Jahed Armaghani
Geosciences 2026, 16(5), 176; https://doi.org/10.3390/geosciences16050176 - 28 Apr 2026
Viewed by 599
Abstract
Accurate prediction of blasting-induced peak particle velocity (PPV) is critical for assessing structural damage risk and ensuring safe tunnel construction. This study proposes an AI agent-based Evaluator-Optimizer workflow that automates the model-development pipeline from prepared dataset input through model training, performance evaluation, hyperparameter [...] Read more.
Accurate prediction of blasting-induced peak particle velocity (PPV) is critical for assessing structural damage risk and ensuring safe tunnel construction. This study proposes an AI agent-based Evaluator-Optimizer workflow that automates the model-development pipeline from prepared dataset input through model training, performance evaluation, hyperparameter optimization, and ensemble construction, with limited manual intervention after dataset definition. The framework employs a multi-agent architecture comprising three collaborative agents—an Orchestrator, an Evaluator, and an Optimizer—supported by a large language model (LLM) reasoning layer. The Evaluator agent analyzes model performance across multiple metrics and generates diagnostic insights; the Optimizer agent translates these insights into structured optimization plans; and the Orchestrator coordinates the evaluate-optimize loop and stopping logic. The workflow was applied to a dataset of 102 tunnel blasting events. Nine candidate regression models spanning tree-based, kernel-based, neural network, and regularized linear families were trained and evaluated. The results show that the workflow enables three substantive observations: (i) across five tree-based models the powder factor is the dominant predictor (28.7–50.5% relative importance); (ii) under 50 Monte-Carlo repeated 80/20 splits, KNN and the Voting ensemble are statistically indistinguishable and form the most stable performance cluster, while Gradient Boosting lies within the same cluster with larger variance; and (iii) under nested 5 × 5 cross-validation, the un-leaked R2 for the top models is about 0.84–0.86, which quantifies the small-sample over-optimism that any future PPV study on single 80/20 splits should expect. The study therefore contributes both a portable agent architecture for tabular geotechnical regression and a concrete cautionary result about single-split benchmarking. Full article
(This article belongs to the Special Issue Advances in Geohazard Mitigation and Adaptation)
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