Review on the artificial intelligence-based methods in landslide detection and susceptibility assessment: Current progress and future directions
December 2024
Landslides pose significant risks to human life, property, and the environment in mountainous regions. Effective detection and susceptibility assessment are essential for mitigating these hazards. Recent...
A physics-informed framework for earthquake-induced landslide hazard assessment considering faulting style and ground motion directionality
June 2026
Accurate assessment of earthquake-induced landslide (EIL) hazard remains challenging because landslide occurrence is jointly controlled by faulting style, pulse-like ground motions, ground motion directionality,...
Landslide risk assessment of Guangdong expressway network using random forest and complex network topological analysis
June 2026
Expressways constitute essential lifeline infrastructure supporting regional interconnection and optimizing social spatial patterns. However, geohazards including landslides severely threaten expressway...
Data-driven digital twin-based smart tunnel maintenance system
December 2025
Tunnel facility management (FM) is crucial for ensuring safety, efficiency, and resilience of tunnel infrastructure. Current FM practices, such as reactive and preventive maintenance, have limitations...
UAV based falling-object risk assessment of damaged building façades using visible and thermal infrared images
June 2026
Extreme wind, heavy rainfall, solar radiation, and thermal cycling can accelerate the degradation of interfacial bonding in existing building façade systems. Conventional manual hammer sounding, close-range...
Research on road crack classification based on ResUNet semantic segmentation and projection features
June 2026
Pavement cracks are one of the most common types of road distresses, making their identification and classification essential for intelligent road inspection and maintenance decision-making. Nevertheless,...
The prediction and characterization of concrete properties by using the machine learning algorithms: A state-of-the-art review
December 2025
Concrete strength mainly depends on the hydration between water and cement and how the resulting calcium silicate hydrate (C-S-H) crystals binds the other concrete components together. Traditional empirical...
Machine learning techniques for soil moisture prediction in arid and semi-arid regions: A case study of Morocco
December 2025
Soil moisture (SM) is a critical variable in hydrological, agricultural, and climatic systems, yet its accurate estimation remains challenging, particularly in arid and semi-arid environments where...
The rise of deep learning: AI and engineering applications under the spotlight of the 2024 Nobel prize
March 2025
The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications. The 2024 Nobel Prizes, particularly in Physics and Chemistry, highlighted...
Generalizable digital rock image segmentation under limited data with the segment anything model
March 2026
Accurate segmentation of digital rock images is essential for characterizing pore–matrix systems and predicting petrophysical properties. However, the diversity of rock textures across different lithologies...
Landslide risk prediction in near-fault areas based on 3D ground motion simulation
June 2026
Seismic landslide risk assessment in near-fault areas requires highly reliable ground motion input data. This study takes the 2016 Kumamoto, Japan, Mw 7.1 earthquake as a case study and proposes an...
Comparative evaluation of threshold-based and CNN-based segmentation methods for multi-modal digital images of geotechnical materials
March 2026
This study systematically evaluates the performance of 15 conventional global single-threshold segmentation algorithms and three representative convolutional neural network (CNN) models across multi-modal...
Hybrid DRASTIC–machine learning framework for groundwater vulnerability assessment in parts of Akwa Ibom State, Nigeria
September 2026
Groundwater is the primary source of potable water in Akwa Ibom State, Nigeria, yet it faces increasing threats from rapid urbanization and agricultural activities. This study evaluates groundwater...
Physics-informed neural network optimized by particle swarm algorithm for accurate prediction of blast-induced peak particle velocity
September 2025
Accurately forecasting peak particle velocity (PPV) during blasting operations plays a crucial role in mitigating vibration-related hazards and preventing economic losses. This research introduces an...
Efficient generation of digital rock CT images using LoRA-enhanced stable diffusion models
June 2025
Digital rock analysis (DRA) is fundamental for geo-energy research, enabling the characterisation of microstructures for applications like hydrocarbon recovery, carbon storage, and groundwater modelling....
Physics-informed reconstruction of transient seepage fields in slopes under rainfall infiltration from sparse observations
June 2026
Rainfall infiltration induces transient seepage responses in slopes, but continuous reconstruction of internal hydraulic fields remains difficult under nonlinear unsaturated flow, complex boundaries,...
GeoPredict-LLM: Intelligent tunnel advanced geological prediction by reprogramming large language models
December 2024
With the improvement of multisource information sensing and data acquisition capabilities inside tunnels, the availability of multimodal data in tunnel engineering has significantly increased. However,...
Quantitative morphological analysis of rock particles on laser scanner data using deep learning
March 2026
While size distribution has traditionally been the dominant metric in rock fragmentation, studies have shown that both size and shape characteristics are influential in determining energy consumption,...
Two-stage transfer fine-tuning for few-shot cross-regional landslide susceptibility mapping in complex mountainous areas
September 2026
Landslide susceptibility assessment plays an important role in regional disaster prevention and mitigation. However, existing machine learning and deep learning methods generally rely on sufficient...
Spatial prediction of potential slope failure areas associated with a heavy rainfall event by integrating pre-event SBAS-InSAR time-series deformation and machine learning: A case study along national route 210 in Kyushu, Japan
June 2026
Extreme rainfall events can trigger slope failures and threaten transportation infrastructure in mountainous areas. Therefore, monitoring slope deformation and predicting potential slope failure areas...
Physical attribute atlas-driven machine learning model for co-seismic landslide prediction
June 2026
Earthquake-induced landslide susceptibility prediction (ELSP) provides a critical scientific basis for post-earthquake emergency response and reconstruction planning. A fundamental challenge lies in...
A bio-inspired artificial intelligence framework leveraging remote sensing for groundwater storage modeling in climate-stressed regions
March 2026
This study presents an AI-driven framework for predicting groundwater storage (GWS) in the arid to semi-arid regions of Agdz and Zagora in southern Morocco, where sustainable water resource management...
Large language model based expert assistant for workflow consistent NPR-DDA analysis
June 2026
NPR-DDA is a discontinuum method for analysing large deformation and failure in reinforced rock masses. Its practical application remains strongly experience-dependent due to the complexity of block...
A review of intelligent technologies for underground construction and infrastructure maintenance
March 2025
Scientific and technological advancements are rapidly transforming underground engineering, shifting from labor-intensive, time-consuming methods to automated, real-time systems. This timely and comprehensive...
Machine learning and remote sensing for modeling groundwater storage variability in semi-arid regions
September 2025
This study investigates the prediction of groundwater Storage in the Rabat-Sale-Kenitra region under climate change conditions using advanced machine learning models. A comprehensive dataset encompassing...
