Search Results (14)

It is an urgent issue for preventing slope failure caused by increasingly severe earthquakes and heavy rain. As a conventional construction method, reinforcing bar insertion work uses the tensile force of the core bar to integrate multiple core bars and pressure plates. Meanwhile, landslide deterrence piles are a construction method in which steel or concrete piles are constructed below the slope, and the rigidity of the piles is used to resist slope failure. In this study, these methods are combined to propose a reinforcing bar insertion work that uses pipes as a construction method. The pipes are not embedded in the immovable layer and are not connected to the reinforcing bar insertion work; therefore, the construction is expected to be simple. Two series of model experiments—a lift-up experiment and a water sprinkling experiment—were performed. Through the lift-up experiment, the effectiveness of the proposed method against static load was confirmed, and the evaluation formula of the load applied to the core bar was proposed. Through the water sprinkling experiment, the effectiveness against rainfall was confirmed, that is, the time until slope failure was extended by the proposed method. Full article

This study investigates the stability of high-rise wind turbine tower foundations in collapsible loess regions through finite element analysis. The mechanisms by which wind load, extreme rainfall load, and seismic load interact during the dynamic response of a pile foundation under single-action and intercoupling conditions are analyzed. A comprehensive multi-parameter analytical model is developed to evaluate pile foundation stability, incorporating key indicators including pile skin friction, average axial stress of pile groups, horizontal displacement at pile tops, and pile inclination. The results show that, among single-load conditions, seismic loading has the most pronounced impact on foundation stability. The peak horizontal displacement at the pile top induced by seismic loads reaches 10.07 mm, substantially exceeding the effects of wind and rainfall loads, posing a direct threat to wind turbine tower safety. Under coupled loading conditions, notable nonlinear interaction effects emerge. Wind–earthquake coupled loading amplifies horizontal displacement by 1.85 times compared to single seismic loading. Rainfall–earthquake coupled loading reduces the peak of positive skin friction by 20.17%. Notably, all seismic-involved loading combinations significantly compromise the pile foundation safety margin. The seismic load is the dominant influencing factor in various loading conditions, and its coupling with other loads induces nonlinear superposition effects. These findings provide critical insights for wind turbine foundation design in collapsible loess areas and strongly support the need for enhanced seismic considerations in engineering practice. Full article

Major earthquakes and rainfall may occur at the same time, necessitating further investigation into the dynamic characteristics and responses of reinforced soil retaining walls subjected to the combined forces of rainfall and seismic activity. Three sets of shaking table tests on model retaining walls were designed, a modular reinforced earth retaining wall was utilized as the subject of this study, and a custom-made device was made to simulate rainfall conditions of varying intensities. These tests monitored the rainwater infiltration pattern, macroscopic phenomena, panel displacement, tension behavior, dynamic characteristics, and acceleration response of the modular reinforced earth retaining wall during vibration under different rainfall intensities. The results indicated the following. (1) Rainwater infiltration can be categorized into three stages: rapid rise, rapid decline, and slow decline to stability. The duration for infiltration to reach stability increases with greater rainfall. (2) An increase in rainfall intensity enhances the seismic stability of the retaining wall panel, as higher rainfall intensity results in reduced sand leakage from the panel, thereby diminishing panel deformation during vibration. (3) Increased rainfall intensity decreases the shear strength of the soil, leading to a greater load on the reinforcement. (4) The natural vibration frequencies of the three groups of retaining walls decreased by 0.21%, 0.54%, and 2.326%, respectively, indicating some internal damage within the retaining walls, although the degree of damage was not severe. Additionally, the peak displacement of the panel increased by 0.91 mm, 0.63 mm, and 0.61 mm, respectively. (5) The amplification effect of rainfall on internal soil acceleration is diminished, with this weakening effect becoming more pronounced as rainfall intensity increases. These research findings can provide a valuable reference for multi-disaster risk assessments of modular reinforced soil retaining walls. Full article

In 2013, a Ms 6.6 earthquake occurred at the boundary of Min County and Zhang County, triggering numerous landslides. Notably, two landslides with significantly different sliding characteristics emerged less than 100 m apart in Yongguang Village, Min County. The eastern landslide was characterized by instability induced by seismic inertial forces, whereas the western landslide exhibited flow slides triggered by liquefaction in loess. To further analyze the causes of these landslides, this study employed a 1 m depth ground temperature survey to probe the shallow groundwater in the area, aiming to understand the distribution of shallow groundwater. Based on the results from the 1 m depth ground temperature survey, a random forest model was applied to regressively predict the initial groundwater levels. The TRIGRS model was utilized to evaluate the influence of pre-earthquake rainfall conditions on landslide stability, and the pore water pressure outputs from TRIGRS were integrated with the Scoops3D model to analyze landslide stability under seismic effects. The results indicate that the combination of the 1 m depth ground temperature survey with high-density electrical methods and random forest approaches effectively captures the initial groundwater levels across the region. Notably, the heavy rainfall occurring one day prior to the earthquake did not significantly reduce the stability of the landslide in Yongguang Village. Instead, the abundant groundwater in the source area of the western landslide, combined with several months of pre-earthquake rainfall, resulted in elevated groundwater levels that created favorable conditions for its occurrence. While the primary triggering factor for both landslides in Yongguang Village was the earthquake, the distinct topographic and groundwater conditions led to significantly different sliding characteristics under seismic influence at the same slope. Full article

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. Full article

Railway embankment slopes are exposed to natural hazards such as excess rainfall, floods, earthquakes, and lake water/groundwater level variations. These are generally considered during the design, construction, and maintenance periods of the embankment. In this study, combined laboratory test methods and a computational approach were applied to assess the effect of groundwater level changes on the railway embankment. The Plackett–Burman (PBD), Box–Behnken design response surface methodology (BBD-RSM), and an artificial neural network (ANN) were used to predict the behavior of the embankment soil hydromechanical properties to determine the integrity of the embankment as water level fluctuates under varied seasonal conditions. The results show that the seepage line is concave during the rising water level (RWL) period, and the railway slope’s static stability factor surges and then stabilizes. Further analysis found that the slope’s stability is largely affected by some of the hydromechanical properties of the soil embankment material, such as the internal friction angle (ϕ), soil density (ρ_s), and cohesion (c). The second-order interaction factors c x s, x s, and s² also affect the stability factor. It was observed that the four most sensitive parameters under both falling water level (FWL) and RWL conditions are ϕ, ρ_s, c, and rate of fall/rise in water level (H). The statistical evaluation of the RSM model produced R² values of 0.99(99) and 0.99, with MREs of 0.01 and 0.24 under both RWL and FWL conditions, respectively, while for ANN, they produced R² values of 0.99(99) and 0.99(98), with MRE values of 0.02 and 0.21, respectively. This study demonstrates that RSM and ANN performed well under these conditions and enhanced accuracy, efficiency, iterations, trial times, and cost-effectiveness compared to full laboratory experimental procedures. Full article

A landslide is a common natural disaster that causes environmental damage, casualties and economic losses, which seriously affects the sustainable development of society. In geomechanics, it is one of the largest deformation problems. Herein, the GPU-accelerated explicit smoothed particle finite element method (eSPFEM) for large deformation analysis in geomechanics was developed on the CUDA platform based on high-performance computing using a self-designed eSPFEM program code. The eSPFEM combines the strain smoothing nodal integration techniques found in the particle finite element method (PFEM) framework, which allows for the use of low-order triangular elements without volume locking and avoids frequent information transfer and mapping errors between Gaussian points and particles in PFEM. A numerical simulation of slope instability using the eSPFEM and based on a strength reduction technique was conducted using various examples, including a cohesive homogeneous slope, a non-cohesive homogeneous slope, a non-homogeneous slope and a slope with a thin soft band. The calculation results show that the eSPFEM can be applied to slope stability analysis under different working conditions, simulating the entire process of slope instability initiation, sliding and reaccumulation, and obtaining reliable FOS values. A numerical simulation was conducted to analyse a landslide that occurred in the Zhangjiazhuang tunnel on the Lanzhou–Xinjiang high-speed railway line on 18 January 2016. A natural unsaturated soil slope, a soil slope with a high moisture content and a soil slope with a high moisture content subjected to an earthquake were analysed. The findings of this study are in good agreement with the actual slope failure conditions. The primary triggers identified for the landslide were heavy rainfall and earthquakes. The verification results indicate that the eSPFEM can effectively simulate an actual landslide case, showcasing high accuracy and applicability in simulating the large deformation behaviour of landslides. Full article

The management of nutrition, food, and health for disaster relief personnel is one of the crucial aspects for carrying out effective rescue activities during large-scale natural disasters, such as a big earthquake, flooding, and landslide following heavy rainfall or man-made disasters, such as widespread fire in industrial areas. Rescue workers, such as fire fighters and rescue teams who work on the disaster relief operations, have to work long, hard, and irregular hours that require energy (both intake and expenditure), with especially altered eating patterns. Reliable estimates of the energy expenditure (TEE) for such disaster relief operations have not been fully established. Here, we propose to clarify the energy expenditure for each type of large-scale disaster activity conducted by fire fighters. Thirty fire fighters (survey participants in this research) who participated in the simulation training of large-scale disaster activities wore tri-axial accelerometers and heart rate monitors during training; and, post-training, 28 fire fighters submitted complete activity record tables. An estimation formula combining tri-axial accelerometer and heart rate monitor data was used. Additionally, energy expenditure per hour (excluding resting energy expenditure: REE) (per average body weight of participants) was calculated for 10 types of large-scale disaster response activities. We propose utilization of these data as a reference value for examining the TEE of firefighting and rescue operations in future large-scale disasters. Full article

The Himalayan state of Sikkim is prone to some of the world’s largest landslides, which have caused catastrophic damage to lives, properties, and infrastructures in the region. The settlements along the steep valley sides are particularly subject to frequent rainfall-triggered landslide events during the monsoon season. The region has also experienced smaller rock slope failures (RSF) after the 2011 Sikkim earthquake. The surface displacement field is a critical observable for determining landslide depth and constraining failure mechanisms to develop effective mitigation techniques that minimise landslide damage. In the present study, the persistent scatterers InSAR (PSI) method is employed to process the series of Sentinel 1-A/B synthetic aperture radar (SAR) images acquired between 2015 and 2021 along ascending and descending orbits for the selected areas in Gangtok, Sikkim, to detect potentially active, landslide-prone areas. InSAR-derived ground surface displacements and their spatio-temporal evolutions are combined with field investigations to better understand the state of activity and landslide risk assessment. Field investigations confirm the ongoing ground surface displacements revealed by the InSAR results. Some urban areas have been completely abandoned due to the structural damage to residential housing, schools, and office buildings caused by displacement. This paper relates the geotechnical investigations carried out on the ground to the data obtained through interferometric synthetic aperture radar (InSAR), focusing on the triggering mechanisms. A strong correlation between seasonal rainfall and landslide acceleration, as well as predisposing geological-structural setting, suggest a causative mechanism of the landslides. Full article

On 17 October 2015, a large-scale subaerial landslide occurred in Taan Fiord, Alaska, which released about 50 Mm${}^3$ of rock. This entered the water body and triggered a tsunami with a runup of up to 193 m. This paper aims to simulate the possible formation of a weak layer in this mountainous slope until collapse, and to analyze the possible triggering factors of this landslide event from a geotechnical engineering perspective so that a deeper understanding of this large landslide event can be gained. We analyzed different remote-sensing datasets to characterize the evolution of the coastal landslide process. Based on the acquired remote-sensing data, Digital Elevation Models were derived, on which we employed a 2D limit equilibrium method in this study to calculate the safety factor and compare the location of the associated sliding surface with the most probable actual location at which this landslide occurred. The calculation results reflect the development process of this slope collapse. In this case study, past earthquakes, rainfall before this landslide event, and glacial melting at the toe may have influenced the stability of this slope. The glacial retreat is likely to be the most significant direct triggering factor for this slope failure. This research work illustrates the applicability of multi-temporal remote sensing data of slope morphology to constrain preliminary slope stability analyses, aiming to investigate large-scale landslide processes. This interdisciplinary approach confirms the effectiveness of the combination of aerial data acquisition and traditional slope stability analyses. This case study also demonstrates the significance of a climate change for landslide hazard assessment, and that the interaction of natural hazards in terms of multi-hazards cannot be ignored. Full article

During the past 10 years, Timor-Leste has concentrated all its efforts on infrastructure development. However, it has not achieved enough due to unexpected ground deformation in mountainous areas that is seriously affecting road constructions, etc. In order to design roads and other infrastructure under such difficult conditions, it is important to know the present and future ground conditions. Continuous monitoring is a significant methods of detecting ground deformation and providing essential information to realize an effective design. The problem arises of “How can ground deformation be monitored in extensive areas, which are generally located in mountain areas that are difficult to access?” Differential Interferometry Synthetic Aperture Radar (DInSAR) has recently been applied to monitor displacement in extensive areas. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry is useful for detecting the deformation in detail. Both methods are advantageous in that they do not require any sensors. Therefore, the combination of DInSAR and UAV photogrammetry is one of the solutions for monitoring the ground deformation in Timor-Leste. In this paper, DInSAR and UAV photogrammetry are applied to unstable ground in the Bobonaro region of Timor-Leste to find the recent ground deformation, since 2007, due to earthquakes and hard rainfall events. It is found that DInSAR is useful for screening usual and unusual ground behavior and that UAV photogrammetry is flexible to use and can detect displacements with cm accuracy after the DInSAR screening. Full article

Slope failures occur when parts of a slope collapse abruptly under the influence of gravity, often triggered by a rainfall event or earthquake. The resulting slope failures often cause problems in mountainous or hilly regions, and the detection of slope failure is therefore an important topic for research. Most of the methods currently used for mapping and modelling slope failures rely on classification algorithms or feature extraction, but the spatial complexity of slope failures, the uncertainties inherent in expert knowledge, and problems in transferability, all combine to inhibit slope failure detection. In an attempt to overcome some of these problems we have analyzed the potential of deep learning convolutional neural networks (CNNs) for slope failure detection, in an area along a road section in the northern Himalayas, India. We used optical data from unmanned aerial vehicles (UAVs) over two separate study areas. Different CNN designs were used to produce eight different slope failure distribution maps, which were then compared with manually extracted slope failure polygons using different accuracy assessment metrics such as the precision, F-score, and mean intersection-over-union (mIOU). A slope failure inventory data set was produced for each of the study areas using a frequency-area distribution (FAD). The CNN approach that was found to perform best (precision accuracy assessment of almost 90% precision, F-score 85%, mIOU 74%) was one that used a window size of 64 × 64 pixels for the sample patches, and included slope data as an additional input layer. The additional information from the slope data helped to discriminate between slope failure areas and roads, which had similar spectral characteristics in the optical imagery. We concluded that the effectiveness of CNNs for slope failure detection was strongly dependent on their design (i.e., the window size selected for the sample patch, the data used, and the training strategies), but that CNNs are currently only designed by trial and error. While CNNs can be powerful tools, such trial and error strategies make it difficult to explain why a particular pooling or layer numbering works better than any other. Full article

The seismogenic fault is crucial for spatial prediction of co-seismic landslides, e.g., in logistic regression (LR) analysis considering influence factors. On one hand, earthquake-induced landslides are usually densely distributed along the seismogenic fault; on the other hand, different sections of the seismogenic fault may have distinct landslide-triggering capabilities due to their different mechanical properties. However how the feature of a fault influence mapping of landslide occurrence probability remains unclear. Relying on the landslide data of the 2013 Lushan, China Mw 6.6 earthquake, this study attempted to further address this issue. We quantified the seismogenic fault effects on landslides into three modes: the distance effect, the different part effects, and the combined effects of the two. Four possible cases were taken into consideration: zoning the study area vertical and parallel to the fault (case 1), zoning the study area only vertical to the fault (case 2), zoning the study area only parallel to the fault (case 3), and without such study-area zonations (case 4). Using the LR model, predictive landslide probability maps were prepared on these four cases. The model also fully considered other influencing factors of earthquake landslides, including elevation, slope, aspect, topographic wetness index (TWI), peak ground acceleration (PGA), lithology, rainfall, distance from the epicenter, distance from the road, and distance from the river. Then, cross-comparisons and validations were conducted on these maps. For training datasets, results show that the success rates of earthquake-triggered landslides for the former three scenarios were 85.1%, 84.2%, and 84.7%, respectively, while that of the model for case 4 was only 84%. For testing datasets, the prediction rates of the four LRs were 84.45%, 83.46%, 84.22%, and 83.61%, respectively, as indicated by comparing the test dataset and the landslide probability map. This means that the effects of the seismogenic fault, which are represented by study-area zonations vertical and parallel to the fault proper, are significant to the predictive mapping of earthquake-induced landslides. Full article

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides. Full article