Advances in Artificial Intelligence and Remote Sensing for Geohazard Modeling and Infrastructure Resilience

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 523

Special Issue Editors

Department of Civil Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Interests: geohazard; resilience of both infrastructure and the environment; forecasting geohazards through a combination of physics-based and data-driven modeling

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Guest Editor
1. State Key Laboratory of Intelligent Deep Metal Mining and Equipment, Shaoxing University, Shaoxing 312000, China
2. Zhejiang Key Laboratory of Rock Mechanics and Geohazards, School of Civil Engineering, Shaoxing University, Shaoxing 312000, China
Interests: landslides susceptibility assessment; geotechnical/geology engineering; seismic/rainfall-induced landslides; rock mechanics; slope stability analysis

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Guest Editor
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Interests: deep learning; hydrology; simulation and prediction; soil moisture; landslides

Special Issue Information

Dear Colleagues,

Intensifying climate change, accelerated urbanization, and aging infrastructure systems are amplifying the frequency and impacts of geohazards worldwide, posing increasing risks to the safety, functionality, and resilience of civil infrastructure systems.

This Special Issue focuses on geohazards and their consequences for civil and infrastructure systems, encompassing not only those driven by extreme climate events such as landslides, floods, debris flows, and permafrost degradation, but also those triggered by tectonic and anthropogenic activities, including earthquakes, reservoir impoundment, underground construction, and slope disturbance near transport corridors. These events can lead to cascading and compound effects that compromise infrastructure performance and endanger communities. There is an urgent need to better characterize geohazard and infrastructure interactions, improve large-scale monitoring, and transform multi-source observations into actionable strategies that support resilient design, operation, and risk management.

Recent advancements in remote sensing and artificial intelligence technologies are scaling up our capacity to understand geohazards and their impacts on infrastructure systems. We invite contributions that integrate artificial intelligence, machine learning, deep learning, remote sensing, and other sensing technologies with geohazard and infrastructure engineering.

The topics of interest include but are not limited to the following:

  • data-driven and physics-informed machine learning for geohazard modeling and mapping;
  • infrastructure condition and deformation monitoring, rapid damage or impact assessment, multi-hazard interaction, and early warning;
  • Digital twin and smart infrastructure applications, data-driven decision-support systems, and case studies that demonstrate how intelligent modeling and sensing can enhance the design, maintenance, and adaptation of infrastructure exposed to natural or anthropogenic geohazards.

We look forward to receiving your contributions!

Dr. Te Pei
Dr. Hongzhi Cui
Dr. Jiangtao Liu
Guest Editors

Manuscript Submission Information

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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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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

  • geohazard modeling and mapping
  • infrastructure resilience
  • geohazard–infrastructure interaction
  • natural hazards
  • structural and geotechnical monitoring
  • digital twin and smart infrastructure
  • remote sensing and earth observation
  • artificial intelligence
  • machine learning
  • deep learning
  • data-driven decision support

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Published Papers (1 paper)

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Research

24 pages, 6585 KB  
Article
Study on the Optimization Method of TBM Disk Cutter Spacing in Jointed Rock Mass
by Pengfei Song, Zhiwen Tan, Bingquan Liu, Chengzhi Yi, Jia Shi, Daibiao Yin, Yunchong Peng, Junning Xie and Junfeng Liu
Infrastructures 2026, 11(4), 137; https://doi.org/10.3390/infrastructures11040137 - 15 Apr 2026
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Abstract
This paper investigates the influence of three key parameters, which are the spacing of cutters, the dip angle of joints and the spacing of joints on the load evolution process of jointed rock masses from the perspective of rock-breaking mechanics. Furthermore, how variations [...] Read more.
This paper investigates the influence of three key parameters, which are the spacing of cutters, the dip angle of joints and the spacing of joints on the load evolution process of jointed rock masses from the perspective of rock-breaking mechanics. Furthermore, how variations in cutter spacing and joint characteristics affect cutting efficiency is studied from a macroscopic viewpoint, focusing on indicators such as specific energy (SE) for crack propagation and rock fragment formation. Based on the research results, a novel optimization approach for cutter spacing in jointed rock mass conditions is proposed. The optimal cutter spacings under varying joint conditions are calculated, and the effects of joint spacing and dip angle on cutter spacing optimization are systematically discussed. The results show that when the joint dip angle is 60°, the cutter spacing is 100 mm, and the joint spacing is 30 mm, the rock fragmentation efficiency reaches the highest. It is also found that the influence of the joint dip angle on the optimal cutter spacing is greater than that of the joint spacing. When the joint spacing is 70 mm, the corresponding optimal cutter spacing is 100.7 mm. When the joint dip angle increases from 0° to 60°, the optimal cutter spacing gradually increases to 112.8 mm. When the joint spacing is greater than 60 mm, the optimal hammer spacing of the hammer gradually decreases. Full article
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