Climate change is reshaping the risk landscape for natural gas pipelines, with landslides emerging as a major driver of technological accidents triggered by natural hazards (Natech events). Conventional Natech risk models rarely incorporate climate-sensitive parameters such as groundwater levels and soil moisture, limiting their capacity to capture evolving threats. This study develops a probabilistic model that explicitly links climate-driven landslide susceptibility to pipeline vulnerability, providing a quantitative basis for assessing pipeline failure probability under different emission projection scenarios. Using Monte Carlo simulations across five regions in China, the results show that under high-emission pathways (SSP5-8.5), pipeline failure probability in summer increases dramatically. For example, from 0.320 to 0.943 in Xinjiang, 0.112 to 0.220 in Sichuan, and 0.087 to 0.188 in Hainan. In cold regions, winter failure probability more than doubles, rising from 0.206 to 0.501 in Heilongjiang and from 0.235 to 0.488 in Beijing. These shifts reveal an overall increase in risk, intensification of seasonal contrasts, and, in some areas, a reconfiguration of high-risk periods. Sensitivity analysis highlights groundwater levels and soil moisture as the dominant drivers, with regional differences shaped by precipitation regimes, permafrost thaw, and typhoon impacts. Building on these insights, this study proposes an AI-based condition-monitoring framework that integrates real-time climate and geotechnical data to support adaptive early warning and safety management.

On December 18, 2023, a magnitude MS6.2 earthquake struck Jishishan County, Gansu Province, triggering over 40 seismic subsidence sites within a seismic intensity VI zone, 32 km from the epicenter.The earthquake caused tens of millions in economic losses to mountain photovoltaic power stations. Extensive geological surveys and comparisons with similar landslides (such as soil loosening, widespread cracks, and stepped displacements) triggered by the 1920 Haiyuan MS8.5 earthquake and the 1995 Yongdeng MS5.8 earthquake, this study preliminarily identifies one subsidence sites as a seismic-collapsed loess landslide. To investigate its disaster-causing mechanism: the dynamic triaxial test was conducted to assess the seismic subsidence potential of the loess at the site, and the maximum subsidence amount under different seismic loads were calculated by combining actual data from nearby bedrock stations with site amplification data from the active source; simulation of the destabilization evolution of seismic-collapsed loess landslides by large-scale shaking table tests; and a three-dimensional slope model was developed using finite element method to study the complex seismic conditions responsible for site damage. The research findings provide a theoretical foundation for further investigations into the disaster mechanisms of seismic-collapsed loess landslides.

In light of a series of recent fatal landslides in Alaska, we set out to determine 1) the history of Alaskan landslides and 2) if the number of associated fatalities has increased with time. To answer our research questions, we searched a combination of 24 digital newspapers and online media sources, including historic digitized Alaskan newspapers, seeking landslides that affected people and/or infrastructure. This resulted in an inventory of 281 landslides occurring in Alaska since 1883. Our database includes the date on which the landslide occurred, its location and probable trigger, any reported injuries and/or fatalities, other reported damage, and the media source. Our inventory indicates that the number of reported landslides started to increase in the 1980's, and has increased dramatically in recent decades. We correlate the increase in landslides to a 1.2 degrees C to 3.4 degrees C increase in average annual air temperature and a 3% to 27% increase in precipitation over the last 50 years across Alaska. This change in climate is degrading permafrost, increasing the number of annual freeze/thaw events, and contributing to larger and more intense precipitation events - such as atmospheric rivers, all of which increase landslide susceptibility in various parts of the state. Alaska's last four fatal landslides occurred in Southeast Alaska, which has experienced the greatest increase in the number of landslides per capita. Our landslide database can serve as the initial inventory for analyses of landslides related to specific extreme weather events, as well as a resource to determine costs incurred from landslide-related damage.

The frequent occurrence of earthquakes worldwide has rendered highway slope protection projects highly vulnerable to damage from seismic events and their secondary disasters. This severely hampers the smooth implementation of post-disaster rescue and recovery efforts. To address this challenge, this study proposes a comprehensive method for assessing seismic losses in slope protection projects, incorporating factors such as topography and elevation to enhance its universality. The method categorizes seismic losses into two main components: damage to protection structures and costs associated with landslide and rockfall clearance and transportation. This study estimates the cost range for common protection structures and clearance methods under general conditions based on widely recognized quota data in China. It establishes criteria for classifying the damage states of protection structures and provides loss ratio values based on real-world seismic examples and expert experience, constructing a model for assessing damage losses. Additionally, by summarizing the geometric characteristics of soil and rock accumulations on road surfaces, a method for estimating landslide volumes is proposed, considering the dynamic impact of slope gradients on clearance and transportation volumes, and a corresponding cost assessment model for clearance and transportation is developed. The feasibility and reliability of the proposed method are verified through two case studies. The results demonstrate that the method is easy to implement and provides a scientific basis for improving relevant standards and practices. It also offers an efficient and scientific tool for loss assessment to industry practitioners.

Permafrost degradation, driven by the thawing of ground ice, results in the progressive thinning and eventual loss of the permafrost layer. This process alters hydrological and ecological systems by increasing surface and subsurface water flow, changing vegetation density, and destabilising the ground. The thermal and hydraulic conductivity of permafrost are strongly temperature-dependent, both increasing as the soil warms, thereby accelerating thaw. In addition, thawing permafrost releases large quantities of greenhouse gases, establishing a feedback loop in which global warming both drives and is intensified by permafrost loss. This paper reviews the mechanisms and consequences of permafrost degradation, including reductions in strength and enhanced deformability, which induce landslides and threaten the structural integrity of foundations and critical infrastructure. Permafrost has been investigated and modelled extensively, and various approaches have been devised to address the consequences of thawing permafrost on communities and the built environment. Some techniques focus on keeping the ground frozen via insulation, while others propose local replacement of permafrost with more stable materials. However, given the scale and pace of current changes, systematic remediation appears unfeasible. This calls for increased efforts towards adaptation, informed by interdisciplinary research.

Predicting cumulative surface slope displacements induced by rainfall infiltration is crucial for accurately assessing the risks to potentially affected infrastructure. In this paper the numerical modelling of the case history of Miscano slope is presented. Plaxis 2D code has been used adopting two constitutive laws: the linear elastoplastic model (Mohr-Coulomb, MC) and the Hardening Soil with small strain stiffness (HSsmall). The aim is to test the suitability of these constitutive laws in predicting the hydro-mechanical behaviour of clayey soil slope. Based on long-term field measurements, the parameters of MC and HSsmall have been determined by back analysing the first-year field measurements in terms of cumulative surficial horizontal displacements and pore water pressure. Subsequently, the numerical models have been validated against the analogous field measurements collected from the second year. The numerical models predict with a good agreement the field measurements for both years. In terms of cumulative surficial horizontal displacements, the HSsmall underestimates the field measurements by 21.2% at the end of the first year, while that based on MC exhibits a 32.8% overestimation. Moreover, the initialization procedure clearly affects the cumulative surficial horizontal displacements results obtained with both the HSsmall and MC models for the second year. In fact, the best results have been achieved when the second-year net rainfall have been applied starting from the initial phase used to generate the lithostatic stress state.

Submarine landslides are a geological hazard that may cause significant damage, and are among the most serious problems in offshore geotechnics. Understanding the mechanism of submarine landslide/offshore structure interaction is essential for risk assessment, but it is challenging due to its complexities. In this study, ten centrifuge tests were conducted to determine how offshore wind turbines founded on four piles respond to consecutive submarine landslides. The tests highlighted two mechanisms of soil deformation and foundation settlement associated with the landslide cycle: (1) deformations of the clay were associated with induced excess pore water pressure, and increased with the number of landslides; and (2) by contrast, foundation settlements largely depended on the dynamic impact of the first cycle and remained unchanged for the remaining events. The settlements were 0.5 m for the 10 m pile foundation and about 0.1 m for the 20 m pile foundation, both in clay and in sand. It was also found that increasing pile length reduces the excess pore water pressure, soil deformation and foundation settlement.

Climate change increases the frequency of extreme weather events, intensifying shallow flow-type landslides, soil erosion in mountainous regions, and slope failures in coastal areas. Vegetation and biopolymers are explored for ecological slope protection; however, these approaches often face limitations such as extended growth cycles and inconsistent reinforcement. This study investigates the potential of filamentous fungi and wheat bran for stabilizing loose sand. Triaxial shear tests, disintegration tests, and leachate analyses are conducted to evaluate the mechanical performance, durability, and environmental safety of fungus-treated sand. Results show that the mycelium enhances soil strength, reduces deformation, and lowers excess pore water pressure, with a more pronounced effect under undrained than drained conditions. Mycelium adheres to particle surfaces, forming a durable bond that increases cohesion and shifts the slope of the critical state line, significantly enhancing the mechanical stability of fungus-treated sand. The resulting strength parameters are comparable to those of soils reinforced with plant roots. Fungus-treated sand remains stable after 14 days of water immersion following triaxial shear tests, with no environmental risk from leachate. These findings demonstrated that fungal mycelium provides an effective and eco-friendly solution for stabilizing loose sand, mitigating shallow landslides, and reinforcing coastlines.

Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.

From July 26 to July 28, 2024, a rare heavy rainfall associated with Typhoon Gaemi triggered widespread clustered landslides in Zixing City, Hunan Province, China. The severe disaster caused 50 fatalities and 15 missing persons across 26 villages, damaging 11,869 houses and affecting a total of 128,000 individuals. Timely and accurate event analysis is essential for deepening our understanding of landslide clustering mechanisms and guiding future disaster prevention efforts. To achieve this, remote sensing analysis using satellite and unmanned aerial vehicle (UAV) aerial images was conducted to assess the distribution pattern of landslide clusters and explore their relationship with environmental factors. Field investigations were subsequently carried out to identify the failure mechanisms of representative landslides. The results identified three main landslide clustering areas in the eastern mountainous forest region of Zixing City. The landslides are predominantly shallow soil slides, with their distribution closely linked to rainfall thresholds and lithology. The clustering areas typically received cumulative precipitation exceeding 400 mm during the extreme rainfall event. Lithology significantly influences the composition and thickness of slope soils, which in turn controls sliding patterns and affects landslide distribution density and individual landslide size. Granite residual soils contributed to the highest landslide density, with many large individual landslides. Topography and vegetation also play important roles in landslide formation and movement. This study provides preliminary insights into the clustered landslide event, aiding researchers in quickly understanding its key features.