Intelligent Technologies in Geotechnical Engineering and Geological Hazards

1. Introduction

Intelligent technology provides us with a new method to understand geotechnical engineering and geomechanics and has widespread application in geotechnical engineering and geological hazards [1,2,3,4,5,6,7]. With the rapid development of intelligent systems, intelligent technologies could offer insights and innovative solutions in geotechnical engineering [1,2,3,4], and are increasingly applied to geological hazards such as the chemical pollution of soils, landslides, debris flows, and earthquake-induced failures [4,5,6,7]. This Special Issue focuses on progress in intelligent technology in geotechnical engineering and geological and earthquake hazards. This Special Issue brings together 13 contributions. The topics covered include ground motion and earthquake hazards, geotechnical engineering and landslides, geochemical surveys, experiments and simulations in rock mechanics and rock physics, and the implications for earthquake mechanisms and geoengineering. The purpose of this editorial is to provide an overview of the Special Issue, identify thematic connections among the published studies, and outline future directions for research in applied geotechnical engineering and geological hazards.

2. An Overview of Thematic Contributions

The contributions to this Special Issue can be organized into five main thematic clusters. The first cluster concerns earthquake hazards. This includes seismic hazard prediction and pre-event estimation, as well as peak ground acceleration estimates for vertical and horizontal ground motion, and comprises four papers.

The second cluster focuses on geotechnical engineering. Contributions in this cluster apply deep machine learning to landslide displacement prediction, TBM penetration rate prediction during tunnel excavation, and the simulation of post-grouted piles in railway bridges, comprising 3 papers.

The third cluster addresses geochemical surveys. Contributions focus on the chemical pollution of soils and element migration of mineralization-alteration zones, comprising two papers.

The fourth cluster relates to rock mechanics and earthquake mechanisms. This section covers frictional experiments and field deformation on granitic faults, as well as the implications for seismic processes, comprising two papers.

The fifth cluster focuses on rock physics experiments in geoengineering. This cluster includes reports on thermal damage in granite based on machine learning and experimental data, as well as radiation exposure experiments of building materials, comprising two papers.

3. Summary of Contributions

Contribution 1 investigates the concentration levels of Cr and Mo in the soils of Beijing based on soil geochemical surveying. The results show that mean concentrations of Cr and Mo are at background levels. To compare the Cr and Mo concentrations in soils with those observed in other capital cities, the statistical parameters of Cr from 20 capital cities and Mo from 11 capital cities were collected. The data reveals medium Cr level concentrations in Beijing, and the Mo level is low.

Contribution 2 presents a pre-event estimation model of earthquake lethality in the Yunnan, Guizhou, and Sichuan provinces in China based on the ground motion acceleration, with 50-year exceedance probabilities. The distribution of casualties is dependent on the probability of a strong earthquake and the location of events.

Contribution 3 developed a physics-informed online learning framework for landslide displacement prediction based on data from the Sichuan Provincial Geological Hazard Monitoring and Warning Platform. The result indicates that the in-depth integration of geo-mechanical knowledge into an online learning framework can effectively improve the reliability, interpretability, and operational applicability of landslide displacement prediction models.

Contribution 4 introduces seismic hazard prediction based on the slip and locking rates of active faults in northern Ningxia. A constraint model is constructed to perform numerical simulations of active faults following the elastic dislocation theory, which can provide slip rates and locking positions of active faults in the areas, which are related to high seismic hazards.

Contribution 5 presents a Support Vector Regression (SVR) model based on machine learning, developed to predict the rate of penetration during tunnel excavation. Integrating hyperparameter optimization used in this study improves both predictive performance and model explainability. The proposed approach method offers a robust, generalizable, and scientifically sound framework for TBM operations and geotechnical modeling applications.

Contribution 6 develops horizontal PGA estimates for varying deep geological conditions. The city of Banja Luka is used as a case study to evaluate horizontal PGA values in an area with a history of moderate to strong earthquakes and with different deep geological conditions. The data indicate that variations in deep geological conditions may have a greater impact on PGA values than local soil effects. Given the scarcity of scaling equations that consider deep geology in addition to local soil conditions, this is a step toward developing more accurate PGA estimates for comparable regions.

Contribution 7 presents PGA estimates for vertical ground motion and varying deep geology site surroundings for the city of Banja Luka. the results show that the deep geological effects on vertical ground motion can exceed the local soil effects. The amount of recorded data obtained can be used to properly calibrate the existing scaling equations and obtain more reliable estimates of vertical PGA.

Contribution 8 developed a comprehensive dose estimation model that accounts for radon, thoron, and gamma radiation. This approach enables the comparison of combined dose values with accepted radiation thresholds for building materials. The results highlight the previously underestimated importance of thoron and the necessity of multi-parameter models for regulatory compliance. The study establishes a novel framework to evaluate holistic radiation risks, urging revised standards to be developed that prioritize ventilation strategies and material mineralogy to protect public health in residential and commercial built environments.

Contribution 9 presents the thermal damage (TD) of granite specimens subjected to high temperatures of up to 800 °C and proposes a predictive model for this thermal damage. Based on the results, the relationships between TD and its influential parameters were determined using regression models and several machine learning algorithms, which could serve as a valuable tool for assessing the thermal damage of rocks.

Contribution 10 proposes a model of rock deformation during coseismic, post-seismic relaxation, and interseismic periods based on pseudotachylytes and cataclasites, which record transient seismic slips within the brittle–ductile transition zone and ductile flow layers from the Anning River fault zone, providing a method to explain seismic processes according to the fault rocks record during brittle–ductile transition.

Contribution 11 investigates the effect of combined tip-and-side post-grouting on the bearing characteristics of post-grouted piles in railway bridges. The results show that the bearing capacity of the pile foundations is greatly increased by combined tip-and-side post-grouting. The combined post-grouting also optimizes the performance of the tip resistance, resulting in a more rapid response as the pile tip displacement increases, which modifies the pile shaft load transfer mechanism by increasing the tip resistance’s contribution to the pile foundation’s ultimate bearing capacity and moving the bearing center of gravity closer to the pile end.

Contribution 12 investigates the element migration of mineralization–alteration zones from the Beiya Porphyry–Skarn Deposit, Northwestern Yunnan. The mineralization–alteration zoning model indicates that the Beiya deposit has similar mineralization and alteration zone characteristics to a typical porphyry copper system, which provides new scientific information for understanding the metallogenic regularity and prospecting at Beiya, as well as similar types of deposits in the Sanjiang metallogenic belt and elsewhere worldwide.

Contribution 13 reviews frictional experiments on granitic faults, which can help to understand continental earthquakes and micromechanical mechanisms. The friction coefficient of granite gouges decreases with increasing temperature and pore fluid pressure, decreasing slip velocity, and increasing slip displacement. The velocity-weakening regime shifts to higher temperatures with increasing slip velocity or decreasing pore fluid pressure. Temperature, normal stress, pore fluid pressure, and slip velocity interact to modulate frictional stability. Microstructural observations reveal that grain size reduction, pressure solution creep, and fluid-assisted chemical processes are key mechanisms governing the transitions between velocity-weakening and velocity-strengthening regimes. The collective evidence underscores the importance of considering fault rheology in a temperature- and fluid-sensitive context, with implications for the interpretation of seismic cycle behavior in continental regions.

4. Conclusions and Future Directions

The papers collected in this Special Issue presents new progress in intelligent technology in geotechnical engineering and geological and earthquake hazards. The papers cover seismic hazard prediction and peak ground acceleration estimates for vertical and horizontal ground motion; apply deep machine learning to landslide displacement prediction, TBM penetration rate prediction during tunnel excavation, and the simulation of post-grouted piles in railway bridges; geochemical surveys on the chemical pollution of soils and element migration of mineralization–alteration zones; rock mechanics experiments and field deformation on granitic faults, and implications for earthquake mechanisms and seismic processes; rock physics experiments in geoengineering, with thermal damage in granite measured based on machine learning and experimental data; and radiation exposure experiments of building materials.

This Special Issue confirms that intelligent technology has widespread application in geotechnical engineering and geological hazards. However, the papers published in this Special Issue are focused on lower-level application in these fields; artificial intelligence should be used in more depth with the increase in big data. We plan to propose “Intelligent Technologies in Geotechnical Engineering and Geological Hazards, 2nd Edition”, which will focus on progress in intelligent technology in geotechnical engineering and geological and earthquake hazards. All topics related to geotechnical engineering and geological hazards, as well as earthquakes induced by human activities, are welcome. Contributions to this Special Issue could focus on field geological surveys, structural geology experiments and simulations, rock mechanics and rock physics, and rock deformation.