Since 1955, Japan has experienced a period of rapid economic growth, during which various social infrastructure facilities were constructed. Recently, however, the aging of these infrastructures has become a significant issue. Inspection and maintenance are essential for the upkeep of these infrastructure facilities. Currently, visual inspections are the mainstream method, making it challenging to quantitatively identify damage. In this study, vibration sensors were installed at the base and top sections of retaining walls, and vibrations from adjacent trains were utilized to evaluate the collapse risk of the retaining walls based on their vibration characteristics. The vibration characteristics were quantitatively assessed by calculating the primary natural frequency from the transfer function and observing temporal changes in the natural frequency to determine the degree of deterioration of the retaining walls. The analysis results showed no significant changes in the vibration characteristics of the retaining walls, indicating no collapse risk. By understanding the inherent vibration characteristics of the retaining walls, it is possible to perform quantitative evaluations, which may be applicable in determining the presence of damage and the necessity of countermeasures in the event of a disaster.

In recent years, slope failure caused by heavy rainfall from linear precipitation bands has occurred frequently, causing extensive damage. Predicting slope failure is an important and necessary issue. A method used to predict the time of failure has been proposed, which focuses on the tertiary stage of the creep theory, shown as V = A/(tr − t), where V is the velocity of displacement, A is a constant, and (tr − t) is the time until failure. To verify this method, indoor model experiments and field monitoring were used to observe the behavior of surface displacement. Seven cases of laboratory experiments were conducted by changing the conditions in the model, such as materials, the thickness of the surface layer, and relative density. Then, two cases of field monitoring slope failure were examined using this method. The results show that, in the tertiary stage of creep theory, the relationship between tilt angle velocity and the time until failure can be expressed as an inversely proportional relationship. When the tilt angle velocity has reached the tertiary creep stage, it initially ranges from 0.01°/h to 0.1°/h; when near failure, it was found to be over 0.1°/h, so, combining this with previous research results, this is a reasonable value as a guideline for an early warning threshold.

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for Integrated Sensing and Communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning based multi-node ISAC 4D environment reconstruction method with Uplink-Downlink (UL-DL) cooperation, which employs virtual aperture technology, Constant False Alarm Rate (CFAR) detection, and Mutiple Signal Classification (MUSIC) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving multi-node cooperation and Uplink-Downlink (UL-DL) cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models Attention Gate Gridding Residual Neural Network (AGGRNN) and Multi-View Sensing Fusion Network (MVSFNet) to enhance the density of sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multi-level fusion strategy incorporating both data-level and feature-level fusion to fully leverage the advantages of multi-node cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms other comparative method, enabling high-precision environmental reconstruction using ISAC system.

Rain-induced natural terrain landslides are the most frequent geo-hazard in many regions of the world. As an essential tool in addressing rising landslide challenges due to climate change, landslide susceptibility assessment has been widely investigated in Hong Kong for over twenty years. However, a public dataset for Hong Kong landslide susceptibility assessment is currently absent in the geoscience research community, which brings difficulties in establishing consistent evaluation criteria for testing any new method or theory. Thus, to facilitate the development of new statistical and/or artificial intelligence-based methods for landslides susceptibility assessment, here we compile the first version of The Hong Kong University of Science and Technology – Landslide Susceptibility Dataset (HKUST-LSD) based on multiple sources of open data. Aiming at comprehensively describing the rain-induced natural terrain landslide conditioning factors in Hong Kong, HKUST-LSD v1.0 comprises data of (a) a landslide inventory; (b) a high-resolution digital terrain model (DTM) and its topographical derivatives; (c) superficial geology, distance to faults and rivers/sea; (d) historical maximum rolling rainfall and (e) ground vegetation condition. HKUST-LSD v1.0 provides a ready-to-use dataset that includes processed landslide and non-landslide samples, together with reference codes that utilized representative machine learning techniques to assess the landslide susceptibility in Hong Kong and achieved satisfactory performance. The dataset will be updated on a regular basis to fulfil the latest research needs that might arise in the research community and support global sustainable development.Download the dataset at: https://github.com/cehjwang/HKUST-LSD

Using data sourced from 15 periglacial debris flow gullies in the Parlung Zangbo Basin of southeast Tibet, the importance of 26 potential indicators to the development of debris flows was analyzed quantitatively. Three machine learning approaches combined with the borderline resampling technique were introduced for predicting debris flow occurrences, and several scenarios were tested and compared. The results indicated that temperature and precipitation, as well as vegetation coverage, were closely related to the development of periglacial debris flow in the study area. Based on seven selected indicators, the Random Forest-based model, with its weighted recall rate and Area Under the ROC Curve (AUC) greater than 0.76 and 0.77, respectively, performed the best in predicting debris flow events. Scenario tests indicated that the resampling was necessary to the improvement of model performance in the context of data scarcity. The new understandings obtained may enrich existing knowledge of the effects of main factors on periglacial debris flow development, and the modeling method could be promoted as a prediction scheme of regional precipitation-related debris flow for further research.

The authors have developed a real-time monitoring system. The monitoring sensors are in small size, power-saving, low-cost. The sensor unit is embedded with a 1D micro seism accelerometer and 3D MEMS (Micro Electro Mechanical Systems) accelerometer (seismic motion/vibration) and has been verifying its field performance since 2019. In the microtremor measurement, compared the result of acceleration Fourier amplitude spectrum with speed Fourier amplitude spectrum of conventional microtremor speed sensor at the same site, almost the same results of the predominant period were obtained. The measured results of the Mj 6.9 earthquake in Miyagi prefecture (March 20, 2021) will also be introduced as a case study. The developed microseism sensor units were applied to the field test of slope over two years, the result is that it can withstand long-term measurement with an accuracy comparable to a conventional seismometer.

ブラジルでは,気候変動の影響,都市拡張による郊外斜面地の開発,防災施設の整備の遅れ,ソフト面の防災対策の遅れなどが複合して豪雨による土砂災害が多発している.本稿は,ブラジルの土砂災害の実態と,その防災対策について概観するとともに,ブラジルで実施されている斜面モニタリング手法の傾向を整理し,傾斜センサーによる斜面崩壊検知センサーを含めて斜面災害への適用性について分析・考察したものである.その結果,斜面崩壊検知センサーは,表層崩壊や鉱滓ダム崩壊のモニタリングに概ね適用性があり,住民の的確な避難行動を促す効果が期待できることが分かった.

Slope monitoring and early warning systems are a promising approach toward mitigating landslide-induced disasters. Many large-scale sediment disasters result in the destruction of infrastructure and loss of human life. The mitigation of vulnerability to slope and landslide hazards will benefit significantly from early warning alerts. The authors have been developing monitoring technology that uses a micro-electro-mechanical systems tilt sensor array that detects the precursory movement of vulnerable slopes and informs the issuance of emergency caution and warning alerts. In this regard, the determination of alarm thresholds is very important. Although previous studies have investigated the recording of threshold values by an extensometer which installation of an extensometer at appropriate sites is also difficult. The authors prefer tilt sensors and have proposed a novel threshold for the tilt angle, which was validated in this study. This threshold has an interesting similarity to previously reported viscous models. Additionally, multi-point monitoring has recently emerged and allows for many sensors to be deployed at vulnerable slopes without disregarding the slope's precursory local behavior. With this new technology, the detailed spatial and temporal variation of the behavior of vulnerable slopes can be determined as the displacement proceeds toward failure.

Considering monitoring noise and complexity of landslide movement, quantify the uncertainty of landslide displacement prediction is crucial and challenging. Traditional data-driven models such as long short-term memory networks (LSTM), support vector regression, and extrema learning machines, etc, give the predicted displacement without considering the uncertainty of the predictions. Moreover, the loss function of the data-driven model is mainly taken as mean square error, which may lead to a worse performance when the training data follows non-normal distribution and thus reduces the robustness of the model. This study tends to propose a novel hybrid model based on LSTM and mixture density network to quantify each data point's probability density distribution. By introducing the mixture density network and the maximum likelihood loss function, this model can get rid of the limitation that the data needs to obey the normal distribution. The mixed probability density parameters of each data point were predicted accurately due to the dynamic learning process in time series by LSTM. Moreover, we introduce the ensemble prediction to consider the uncertainty of model parameters. The performance of the model was validated based on a typical landslide in the Three Gorges Reservoir Area (TGRA), the Baishuihe landslide. Application results demonstrate that the proposed model provides accurate mean predictions and reasonable confidence displacement intervals.

An Mw6.6 earthquake occurred at 3a.m. on September 6, 2018, in the eastern part of Iburi, Hokkaido. This earthquake killed 41 people, caused a series of service interruptions in Sapporo city. Soil liquefaction and approximately 6000 landslides were triggered. A field investigation was conducted on liquefaction and typical landslides during September 18 to 24, 2018. The liquefaction disaster exhibited soil flow, subgrade collapse, uneven settlement, earthquake subsidence, and deformation of buildings. After investigation of typical landslides, it was found that owing to the continuous heavy rainfall and typhoon, the surface soil had a high-water content. A layered loam of approximately 2 m below ground surface developed into a weak surface. Based on the analysis of sliding morphology, the landslides were classified into translational earth slides and earth flow. To clarify the mechanism of regional landslides, the stability and permanent displacement of slopes considering effect of continuous heavy rainfall and seismic motion was analyzed. Limit equilibrium analysis was applied based on the pseudo-static method. Then Newmark displacement calculation was conducted based on the seismic acceleration record. The distribution range of analysis results showed agreement with actual landslides disasters. The results verified the contribution of continuous rainfall and strong motion to the failure of regional slopes.

In recent decades, early warning systems to predict the occurrence of landslides using tilt sensors have been developed and employed in slope monitoring due to their low cost and simple installation. Although many studies have been carried out to validate the efficiency of these early warning systems, few studies have been carried out to investigate the tilting direction of tilt sensors at the slope surface, which have revealed controversial results in field monitoring. In this paper, the tilting direction and the pre-failure tilting behavior of slopes were studied by performing a series of model tests as well as two field tests. These tests were conducted under various testing conditions. Tilt sensors with different rod lengths were employed to investigate the mechanism of surface tilting. The test results show that the surface tilting measured by the tilt sensors with no rods and those with short rods located above the slip surface are consistent, while the tilting monitored by the tilt sensors with long rods implies an opposite rotational direction. These results are important references to understand the controversial surface tilting behavior in in situ landslide monitoring cases and imply the correlation between the depth of the slip surface of the slope and the surface tilting in in situ landslide monitoring cases, which can be used as the standard for tilt sensor installation in field monitoring.

Rainfall-induced landslides are a frequent and often catastrophic geological disaster, and the development of accurate early warning systems for such events is a primary challenge in the field of risk reduction. Understanding of the physical mechanisms of rainfall-induced landslides is key for early warning and prediction. In this study, a real-time multivariate early warning method based on hydro-mechanical analysis and a long-term sequence of real-time monitoring data was proposed and verified by applying the method to predict successive debris flow events that occurred in 2017 and 2018 in Yindongzi Gully, which is in Wenchuan earthquake region, China. Specifically, long-term sequence slope stability analysis of the in situ datasets for the landslide deposit as a benchmark was conducted, and a multivariate indicator early warning method that included the rainfall intensity-probability ( I-P ), saturation ( S i ), and inclination ( I r ) was then proposed. The measurements and analysis in the two early warning scenarios not only verified the reliability and practicality of the multivariate early warning method but also revealed the evolution processes and mechanism of the landslide-generated debris flow in response to rainfall. Thus, these findings provide a new strategy and guideline for accurately producing early warnings of rainfall-induced landslides.

A low-cost and simple method of monitoring rainfall-induced landslides is proposed, with the intention of developing an early-warning system (Uchimura et al. 2015). Surface tilt angles of a slope are monitored using this method, which incorporates a Micro Electro Mechanical Systems (MEMS) tilt sensor and a volumetric water content sensor. In several case studies, the system detected distinct tilt behaviour in the slope in pre-failure stages. Based on these behaviours and a conservative approach, it is proposed that a precaution for slope failure be issued at a tilting rate of 0.01°/h, and warning of slope failure issued at a rate of 0.1°/h. The development of this system can occur at a significantly reduced cost compared with current and comparable monitoring methods, which such as extensometer or borehole inclinometers. Increasing the number of installed sensors, thus increasing the accuracy of the early warning thresholds and predictions, so that given the cost reduction, slopes can be monitored at many points, resulting in detailed observation of slope behaviours, but the potentially large number of monitoring points for each slope does induce a financial restriction. Therefore, the selection of sensor positions needs to be carefully considered for an effective early warning system. These case studies will henceforth be helpful in determining the installation of the sensor array of early warning system.

An early warning monitoring system is one of the most effective ways to reduce disasters induced by slope instabilities. The 2008 Ms 8.0 Wenchuan earthquake that occurred in Sichuan province, China, induced more than 197,000 slope failures and landslides. Othervise, there are more than 270.000 potential slope failures in Japan. To reduce vulnerability to such slope and landslide hazards, a low cost that compared to a traditional instrumentation of inclinometers and extensometer, and an effective earl warning system becomes important. For this purpose. a new monitoring method of distributed tilt sensors developed by authors, was adopted by local government of Japan and China. This is a newly simple multi-point method of monitoring landslides and slope failures, with the intention of developing an early-warning system. Surface tilt angles of a slope are monitored using this method. which incorporates a Micro Electro Mechanical Systems (MEMS) tilt sensor and a volumetric water content sensor. This system was applied to many landslides and slopes in Japan and China recently. In several case studies, including a slope failure test conducted on a natural slope using artificial heavy rainfall, the system detected distinct tilt behavior in the slope in pre-failure stages. Based on these behaviors and a conservative approach, it is proposed that a precaution for slope failure be issued at a tilting rate of 0.01 degrees/hr., and warning of slope failure issued at a rate of 0.1 degrees/hr. (Uchimura et al. 2015). The development of this system can occur at a significantly reduced cost compared with curmnt and comparable monitoring methods.

After the Wenchuan earthquake on May 12th, co-seismic landslides and fractured slopes were more susceptible to rainfall-induced shallow mass re-mobilization and post-earthquake disasters were gained widespread significance for the disaster mitigation. However, despite the rainfall thresholds, the hydrological parameters of rainfall induced mass re-mobilization in natural environment of Wenchuan earthquake regions is not well understood and widely used for disaster early warning. In this study, shallow rainfall triggered slope failures under partially saturated conditions in the hollows of the gully was proved by instrumental evidence of in situ experimental tests in a natural co-seismic landslide for simulating the rainfall triggered erosion process of shallow failures in debris flow catchment. In addition, the results revealed the transient process and unsaturated condition for mass movement in response to rainfall, and demonstrated the importance of hydrological parameters includes soil matrix suction and moisture content for shallow slope failure in the hollows, and the stability analysis suggested a hydro-mechanical thresholds including water contents and matrix suction based on the mechanism of slope failure for early warning of the mass-remobilization in hollows of debris flows. These findings were expecting for contribution effectively on improvement of early warning accuracy for rainfall induced shallow landslides and debris flows in earthquake hit region.

A low-cost and simple method of monitoring rainfall-induced landslides is proposed that compared to a traditional instrumentation of inclinometers and extensometer, with the intention of developing an early-warning system. Surface tilt angles of a slope are monitored using this method, which incorporates a micro electro mechanical systems (MEMS) tilt sensor and a volumetric water content sensor. In several case studies, including a slope failure test conducted on a natural slope using artificial heavy rainfall, the system detected distinct tilt behavior in the slope in pre-failure stages. Based on these behaviors and a conservative approach, it is proposed that a precaution for slope failure be issued at a tilting rate of 0.01°/h, and warning of slope failure issued at a rate of 0.1°/h. The development of this system can occur at a significantly reduced cost (approximately one-third) compared with current and comparable monitoring methods. Given the cost reduction, slopes can be monitored at many points, resulting in detailed observation of slope behaviors, but the potentially large number of monitoring points for each slope does induce a financial restriction. Therefore, the selection of sensor positions needs to be carefully considered for an effective early warning system.

Prevention and mitigation of rainfall induced geological hazards after the Ms=8 Wenchuan earthquake on May 12th, 2008 were significant for rebuild of earthquake hit regions. After the Wenchuan earthquake, there were tens of thousands of fractured slopes which were broken and loosened by the ground shaking, they were very susceptible to heavy rainfall and change forms into potential debris flows. In order to carry out this disaster reduction and prediction effectively in Longmenshan region, careful real-time monitoring and pre-warning of mountain hazards in both regional and site-specific scales is reasonable as alternatives in Wenchuan earthquake regions. For pre-warning the failure of fractured slopes induced by rainfall, the threshold value or the critical value of the precipitation of hazards should be proposed. However, the identification of critical criterion and parameters to pre-warning is the most difficult issue in mountainous hazards monitoring and pre-warning system especially in the elusive and massive fractured slopes widespread in Wenchuan earthquake regions. In this study, a natural coseismic fractured landslide in the Taziping village, Hongkou County, Dujianyan City, was selected to conduct the field experimental test, in order to identify the threshold parameters and critical criterion of the fractured slopes of Taziping. After the field experimental test, the correlation of rainfall intensity, rainfall duration and accumulative rainfall was investigated. The field experimental test was capable of identifying the threshold factors for failure of rainfall-induced fractured slopes after the giant earthquake.

Two common empirical estimate methods, PAN Jiazheng method and IWHR empirical formula method, were used to calculate the height of reservoir bank landslide surge of Wu River.By comparing the results from two estimate methods, it is found that the height of surge calculated by IWHR empirical formula method is far less than that of PAN Jiazheng method.It is suggested that Pan Jiazheng method should be used as the main empirical method in the estimation of landslide while the IWHR empirical formula method as an auxiliary method.By comparing the results of surge height before pressing foot at the toe of slope and after, it is indicated that height of surge decreased obviously after pressing foot at the toe of slope.The method of pressing foot at the toe of slope can prevent the disaster of reservoir bank landslide surge.