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Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners. [ABSTRACT FROM AUTHOR]

In a strong earthquake, not only a large number of coseismic landslides are triggered, but relaxation and cracks in the rocks and soils are induced, which make these rocks and soils vulnerable to instability during subsequent rainfall; thus, strong earthquakes always have long-term effects on landslides. The geo-hazards along the Zhaotong-Qiaojia road in the 2014 Ludian earthquake (Ms 6.5) of Yunnan Province, China, were investigated, and the Ganjiazhai giant landslide was chosen as a case study. First, using the limit equilibrium analysis and Newmark method, the critical seismic intensity of the landslide before the earthquake was evaluated. Secondly, the dynamic failure process of the landslide under the measured ground motion was simulated with the discontinuous deformation analysis method. Lastly, based on stress–seepage coupling analysis and precipitation data from Ludian meteorological station, the stability of the landslide during subsequent rainfall after the earthquake was predicted. The results show that the critical seismic intensity was within degrees VIII–IX, which is consistent with the results of the earthquake damage investigation. The dynamic failure process can be divided into four stages, and four scarps formed; the potential sliding zones during the subsequent rainfall were at the first scarp and the fourth scarp, and their critical rainfall amounts were 35–40 mm and 55–60 mm, respectively. In this paper, failure process simulation and stability prediction of the landslide before, during, and after the strong earthquake are presented, which provide analysis methods for the dynamic stability of landslides in the meizoseismal area. [ABSTRACT FROM AUTHOR]

A landslide inventory map, which shows the location of landslide phenomena and contains information about movement type, activity, etc., is a basic element for landslide susceptibility and risk assessment. For this reason, the evaluation of the quality, in terms of accuracy and completeness, of landslide inventory maps is an important issue. In this paper, two landslide inventory maps are compared, in order to determine the corresponding quality, through a direct comparison, aimed to evaluate the degree of cartographic matching between the maps, and the determination of the statistical properties of landslide areas and the comparison between the frequency-area statistics of landslides contained in the two inventories. The two landslide inventory maps at 1:25,000 scale, used for these analyses, have been produced for Daunia region (Apulia, Southern Italy), by the Apulia River Basin Authority; the first, 'archive inventory', by unifying the existing archive inventories; the second, 'surveyed inventory', through aerial-photo interpretation and field investigations. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate and compare the performances of 5 machine learning (ML) techniques for predicting the spatial probability of landslide at Atsuma, Japan, and Mt. Umyeon, Korea. 5 ML models used are Naïve Bayes (NB), ensemble learning (random forest (RF) and adaboost (AB)), and deep learning (multilayer perceptron (MLP) and convolutional neural network (CNN)) models. Real landslide events at the study areas are randomly separated to the training set for landslide mapping and the validation set for assessing performance. To assess the performance of the used models, the resulting models are validated using receiver operating characteristic (ROC) curve. The success rate curves show that the areas under the curve (AUC) for the NB, RF, AB, MLP, and CNN are 85.1, 88.8, 88.6, 87.5, and 95.0%, respectively, at Atsuma and 68.7, 85.6, 90.5, 81.6, and 92.0%, respectively, at Mt. Umyeon. Similarly, the validation results show that the areas under the curve for the NB, RF, AB, MLP, and CNN are 84.3, 87.1, 87.1, 86.7, and 89.7%, respectively, at Atsuma and 64.9, 85.5, 83.9, 84.7, and 90.5%, respectively, at Mt. Umyeon. In addition, statistical tests such as Chi-square test and difference of proportions test show that all classified landslide susceptibility maps have statistical significance and the significant difference in classified landslide susceptibility maps from different ML models. The comparison results among 5 ML models show that the CNN model had the best performance and NB model had the worst performance in both study areas. [ABSTRACT FROM AUTHOR]

An effort to implement a siphon drainage system requires a comprehensive knowledge of the factor affecting the performance of the drainage system to reduce the groundwater table in the landslide area. A long-term field experiment was conducted to evaluate the effectiveness of the siphon drainage system with vertical wells to maintain the stability of landslide masses in a volcanic hillslope in West Java. The groundwater levels in all wells and water discharged from each well were monitored and measured periodically. Results of a 1-year measurement of the groundwater level indicate that the siphon drainage system was generally effective in decreasing the groundwater level in the landslide masses to a maximum depth of 7 m with a maximum total drainage flow of 24 m3/day. The implemented siphon drainage system was also able to prevent the groundwater level from rising during a rainfall period. A series of slope stability analyses under a steady-unsteady state seepage condition indicates the siphon drainage increased the long-term stability of the landslide area significantly. Based on this study, factors affecting the effectiveness of long-term siphon drainage performance are thoroughly evaluated. [ABSTRACT FROM AUTHOR]

This study aimed to illustrate the seismic response and instability process of a double-row anti-slide pile-reinforced bridge foundation and gravel landslide. Selecting the gravel slope of Jiuzhai Valley along the under-construction Chengdu-Lanzhou high-speed railway as the site prototype, large-scale shaking table tests were first conducted at a similitude ratio of 1:70, with sine waves and El Centro waves as the seismic wave inputs. The amplitudes of the input seismic waves were increased, while the acceleration, dynamic earth pressure, and strain distribution were monitored in the gravel landslide. The dynamic response patterns of anti-slide pile-reinforced bridge foundations in gravel landslides were illustrated. The front-row and back-row anti-slide piles should be a reasonable distance from the bridge foundation. The response acceleration manifested an elevation amplification effect with an increasing elevation in the slope behind the anti-slide piles. Back-row anti-slide piles reinforcing the bridge foundation can reduce the effect of landslide thrust on the bridge foundation and maintain a uniform distribution of earth pressure behind the bridge foundation, mitigating seismic effects. The dynamic earth pressure peaked at the top of the bridge foundation and then decreased along the depth. The back-row anti-slide piles displayed greater resonance coupling and unloading effects before and after reaching the load-carrying limit, respectively. In seismic strengthening design involving bridge foundations and gravel landslides, when the earthquake-induced resonance coupling effect on inclined, loosely packed land masses is fully considered, pre-reinforcement measures (e.g., high-pressure grouting and anchor spraying) should be carried out on the gravel slopes. [ABSTRACT FROM AUTHOR]

Assessment of the landslide susceptibility can provide useful information in the management and mitigation of the negative consequences of this phenomenon. The purpose of this study was to map the landslide susceptibility and to highlight the effects of tectonic and lithology. The landslide-causing factors including distance from faults, lithology, slope gradient, slope aspect, distance from drainages, distance from roads, land use, and precipitation were used for the landslide susceptibility mapping by using analytical hierarchical process (AHP) method. Based on the landslide susceptibility zonation map, the study area was grouped into five susceptibility classes including very low, low, moderate, high, and very high. High values of landslide density in very high and high susceptible zone, and the high percentage of area under the success rate curve (72.65%) show that the AHP method has accurately predicted the landslide susceptibility zonation map of the study area. Data reveal that most landslides are close to the Zagros Main Recent Fault (MRF) and the Main Zagros Reverse Fault (MZRF) that merge with each other along the trend of Azad River. These faults have locally produced zones of extension and contraction that have caused the intense faulting and fracturing of rocks and hence the intensification of the landslides. The Oligo-Miocene marl (with high potential of plasticity, expanding, and cracking), the Oligo-Miocene limestone (with steep dip slopes and strike-slip faults) that overlies the Oligo-Miocene marls as a lubricant, and serpentinites (with high capacity of flexibility, plasticity, and sheeting) are three important lithological units that are more prone to landsliding. [ABSTRACT FROM AUTHOR]

On 16 April 2016, a Mw 7.0 earthquake occurred in Kumamoto city, Japan. The main shock induced two large landslides, namely the Aso Bridge landslide and the Aso Volcanological Laboratory landslide. Their topographical and geological conditions and motion features were investigated by using an unmanned aerial vehicle (UAV) and portable dynamic cone penetration tests (PPTs). The Aso Bridge landslide lies between elevations of 385 m and 725 m, with a total estimated volume of about 1,980,000 m3. The main body is composed of cohesive soil with lapilli and block. The Aso Volcanological Laboratory landslide lies on a slope between 483 m to 582 m, and the total volume is about 81,000 m3, with an average thickness of 4.5 m. The main body is composed of Kusasenrigahama volcanic pumice tephra beds. The material compositions and deposits of both landslides have low cohesion and easily induced shear failure for the two landslides. The sliding distance of the Aso Bridge landslide was long, the sliding direction almost unchanged from the scarp to the toe, and the sliding speed was rapid. The sliding distance of the Aso Volcanological Laboratory landslide, however, was short, the sliding direction changed from the N-direction at the scarp to the NW-direction at the toe, and the sliding speed was slow. [ABSTRACT FROM AUTHOR]

Landslides are a common type of mass movement that occur in large numbers and have significant effects on topography. In Turkey, landslides rank second after earthquakes among natural disasters that cause widespread loss of life and property. This study investigates the monitoring of the Taşkent Landslide (Konya, Turkey), which has considerably affected the local topography. The landslide in Taşkent had an adverse effect on transportation to nearby towns and villages. Initial studies were conducted using the global navigation satellite system (GNSS) measurement scale; these studies are currently on-going. Since 2011, studies using terrestrial laser scanning (TLS), which is one of the latest ground-positioning technologies, have also been launched. This article presents the results of the TLS and GNSS approaches used to monitor, characterise and model an active landslide. It includes the comparison and analysis of a point cloud, three-dimensional model, profile and change in volume mass acquired in different time periods in one landslide area. The point cloud data, obtained from two different time periods using the TLS technique, and supported by the results of GNSS analysis, were analysed via different methods. The TLS revealed that up to 4 m of material was displaced at the site of the landslide, and it revealed detailed information about how the landslide occurred. The volume of the mass that moved as a result of the landslide was determined, and movements that occurred in the geological structure were monitored. These studies showed that the TLS point cloud method can be effectively used in the monitoring of landslides that occur in areas that are large, difficult to reach, rugged or mountainous. [ABSTRACT FROM AUTHOR]

In the last decades, building damage caused by landslides in urban areas has increased due to the rapid growth of urbanization even on landslide-prone slopes. This work presents the analysis results of landslide damage evolution by means of a macrodamage index in relation to the characteristics of landslides. The study area is located in southern Italy, and is affected by slow-moving landslides, which, over time, have caused relevant effects on the buildings. A series of geological and geomorphological studies and field surveys, supported by monitoring via conventional geotechnical techniques, allowed us to classify four categories of landslides based on the following criteria: landslide movement, type of material and estimated depth. Detailed field surveys aimed at evaluating distribution and evolution of the damage to buildings located within and near the unstable area were carried out in 2005 and 2011. The evaluation of the macrodamage index in two different years enabled us to directly correlate the features of the landslide categories and their effects on the urban fabric. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W and W) was used as weight for each factor's type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %. [ABSTRACT FROM AUTHOR]