Application of remote monitoring technology in landslides in the Luoshan mining area

With the scale extending of mining, the landslide disaster in the earth’s surface will become more and more serious, and these landslide disasters are being threatened to the sustainable safe mining of the underground mine and the open-pit mine. Based on the theory that sliding force is greater than the shear resistance (resisting force) at the potential slip surface is the necessary and sufficient condition to occur the landslide as the sliding criterion, the principle and method for sliding force remote monitoring is presented, and the functional relationship between the human mechanical quantity and the natural sliding force is derived, hereby, the natural sliding force can be calculated according to the human mechanical quantity. Based on above principle and method, a new system of landslide remote monitoring is designed and 53 systems are installed on the landslide body in the Luoshan mining area, which make up the landslide remote monitoring network. According to the results of field test around 8 months, monitoring curves between sliding force and time are obtained, which can describe and forecast the develop trend of landslide. According to above analysis, the results show that this system has some following advantages: (1) real-time monitoring; (2) remote intelligent transmission; (3) landslides early warning.

The formation of the Wulipo landslide and the resulting debris flow in Dujiangyan City, China

The Wulipo landslide, triggered by heavy rainfall on July 10, 2013, transformed into debris flow,resulted in the destruction of 12 houses, 44 deaths, and 117 missing. Our systematic investigation has led to the following results and to a new understanding about the formation and evolution process of this hazard. The fundamental factors of the formation of the landslide are a high-steep free surface at the front of the slide mass and the sandstone-mudstone mixed stratum structure of the slope. The inducing factor of the landslide is hydrostatic and hydrodynamic pressure change caused by heavy continuous rainfall. The geological mechanical model of the landslide can be summarized as "instability-translational slide-tension fracture-collapse" and the formation mechanism as "translational landslide induced by heavy rainfall". The total volume of the landslide is 124.6×104 m3, and 16.3% of the sliding mass was dropped down from the cliff and transformed into debris flow during the sliding process, which enlarged 46.7% of the original sliding deposit area. The final accumulation area is found to be 9.2×104 m2. The hazard is a typical example of a disaster chain involving landslide and its induced debris flow. The concealment and disaster chain effect is the main reason for the heavy damage. In future risk assessment, it is suggested to enhance the research onpotential landslide identification for weakly intercalated slopes. By considering the influence of the behaviors of landslide-induced debris flow, the disaster area could be determined more reasonably.

Landslide risk perception and communication for disaster risk management in mountain areas of developing countries：a Mexican foretaste

The increasing impact of disasters at local,national,regional and global scales in recent decades has provided enough evidence to urgently direct attention towards the necessity of disaster risk reduction and management,and this requires knowledge.Knowledge without communication is barren,and to communicate the risk of disaster it is necessary to understand the perception of the people at risk.In particular,this paper deals with the necessity to delineate strategies of risk communication in pursuance of risk knowledge as a core of disaster risk reduction and management,especially in mountain areas of developing countries.To portray this issue,an analysis of landslide risk perception in terms of experience,landslide risk awareness,exposure,preparedness,and risk communication and trust was undertaken in the municipality of Teziutlán,Puebla,Mexico,an area that has been affected for several decades by episodes of mass movement.Analysis of the responses to a risk perception questionnaire has offered valuable insights in terms of the information and knowledge most required by the people living in the area of interest,in order to devise a realistic and functional strategy to communicate the risk of a landslide disaster.This includes better understanding of controlling factorsand drivers of this risk,and the establishment of potential trusted sources of risk communication.Beyond considering practical matters of risk assessment and management,risk perception and communication can increase the resilience of vulnerable people,and can enhance capacity building for present and future generations.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Support principles of NPR bolt/cable and control techniques of large-deformation disasters

Deep mining has been paid much more attention because of the depletion of shallow mining resources.Traditional bolts could be invalid to accommodate large displacement and deformation in geomaterials.Consequently, alternative support and reinforcement bolts need to be studied and their constitutive models also need to be developed to help understanding for the complex stress-strain responses of rock masses under loadings. The effect of Negative Poisson's Ratio(NPR) that is attributed to the swelling phenomenon along the lateral direction may appear in metal materials under tensional loadings. Thence NPR materials often have an advantage over NPR ones in mechanical behavior such as impact resistance, antishearing, and energy absorbed. From the characteristics of NPR materials, a series of bolt and cable supports with the effect of NPR and constant-resistance have been recently developed. We here firstly introduce the structural features of NPR support. Then the constitutive model of NPR support is presented and its corresponding equation of energy equilibrium. Its basic principle interacted on rock masses is further discussed. Finally, NPR cables are employed to support the slope of an open-pit mine. The applications show that NPR cables can ease failure within the slope and play an important role in predicting and providing early warning of slope failure, together with a monitoring system of slope stability.

Damage and geological assessment of the 18 September 2011 M_w 6.9 earthquake in Sikkim,India using very high resolution satellite data

Post-disaster very high resolution（VHR） satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time.In this paper,we studied landslides triggered by the M_w 6.9 earthquake in Sikkim,India which occurred on 18 September 2011 using VHR data from Cartosat-1,GeoEye-1,QuickBird-2 and WorldView-2 satellites.Since the earthquake-affected area is located in mostly inaccessible Himalayan terrain,VHR data from these satellites provided a unique opportunity for quick and synoptic assessment of the damage.Using visual change analysis technique through comparison of pre- and post-earthquake images,we assessed the damage caused by the event.A total of 123 images acquired from eight satellites,covering an area of4105 km2 were analysed and 1196 new landslides triggered by the earthquake were mapped.Road blockages and severely affected villages were also identified.Geological assessment of the terrain highlighted linear disposition of landslides along existing fault scarps,suggesting a reactivation of fault.The landslide inventory map prepared from VHR images also showed a good correlation with the earthquake shake map.Results showed that several parts of north Sikkim,particularly Mangan and Chungthang,which are close to the epicentre,were severely affected by the earthquake,and that the event-based landslide inventory map can be used in future earthquake-triggered landslide susceptibility assessment studies.

The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks

Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relationships are not deeply analyzed,and the scene contents are fixed,which is not conducive to meeting multilevel visualization task requirements for diverse users.To resolve the above issues,a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper.The characteristics of relationships among users,scenes and data were first discussed in detail;then,a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects,and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph.Therefore,a personalized Landslide Disaster scene data recommendation mechanism was proposed.Finally,a prototype system was developed,and an experimental analysis was conducted.The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users.The recommendation accuracy stabilizes above 80%–a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.