Physics-based simulation of debris flows over complex terrain is essential for hazard assessment, but repeated numerical integration is costly when many scenarios must be explored. We develop a general deep-learning surrogate modelling framework for two-dimensional (2D) debris-flow propagation, here applied to the Morino-Rendinara area (central Italy) using a three-dimensional (3D) Fourier Neural Operator (FNO) trained on synthetic simulations generated by a validated in-house finite-volume shallow-water solver. The solver reproduces debris-flow propagation over complex terrain and is specifically developed for artificial intelligence (AI) applications. It is based on a depth-averaged 2D formulation using the Harten-Lax-van Leer-Contact (HLLC) approximate Riemann solver, hydrostatic reconstruction, positivity-preserving wet-dry treatment, and Voellmy-type basal friction, and was verified through analytical benchmarks, numerical tests, and back-analyses of real events. The dataset was built from four site-specific release settings derived from real topography, combining different released volumes and bulk densities while preserving local geomorphological and rheological characteristics. Each simulation was stored as a full spatio-temporal tensor and used to train an FNO conditioned on coordinates, topography, friction parameters, bulk density, and initial release thickness. Training used a novel loss to emphasize active-flow areas and improve velocity reconstruction, and was performed using a graphics processing unit (GPU). The surrogate shows effective generalization to within-distribution validation samples, with global relative mean squared errors of 5.49% for flow thickness, 5.34% for velocity component u, and 2.60% for v, and mean R2 values of 0.95, 0.94, and 0.97. For a representative sample, the surrogate predicts the full spatio-temporal solution in 0.52 s, versus about 47 s for the first-order finite-volume solver, corresponding to a speed-up of about 91 & times;, with an even larger gap expected for higher-order solvers, since, whilst the computation time of the solver increases as its complexity increases, the computation time of the FNO remains essentially unchanged. These results indicate that the proposed FNO is a reliable site-specific surrogate for rapid approximation of 2D debris-flow dynamics over real terrain, with potential for uncertainty propagation, Monte Carlo analysis, large-ensemble simulation, and hazard-oriented scenario assessment.

Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the (x,t) domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to h(x,t) and u(x,t), thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative L2 errors of about 6–7% for flow depth and 10–15% for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately 36× with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments.

Debris flow events are complex natural phenomena that are challenging to predict, especially when data are limited or uncertain. This study presents a novel probabilistic approach using Bayesian Neural Networks (BNN) to predict possible volumes of debris flow accumulation by using synthetic and real-world data. Synthetic datasets are created based on statistical distributions informed by geomorphological and hydrological knowledge, allowing the model to learn typical behaviors when real data is scarce. BNN provide uncertainty quantification by modeling neural weights as probability distributions. The model resulting from validation on synthetic data and two real datasets from China and South Korea show strong predictive performance (R2 > 0.98) and close alignment between predicted and observed volumes, even in the presence of outliers. The key strength of this integrated approach lies in its integration of synthetic data generation, real data augmentation based on Bootstrapping, expert knowledge and Bayesian deep learning to overcome limitations of traditional statistical models, improving debris flow forecasting and enabling more informed and resilient risk management strategies.

Deep-seated landslides are found across various geological settings, with their velocity influenced by processes such as rainfall infiltration and earthquakes shaking, which alter pore pressure and shear stress at the landslide base. This paper examines the Borrano and Ponzano landslides in Abruzzo, central Italy, as representative examples of deep-seated landslides. Our analysis focuses on their geometry, mechanisms, and velocity changes using field, subsurface, and remote sensing. Both landslides, involving Laga Formation rocks, feature a concave basal sliding surface and exhibit rotational sliding with continuous movement since at least 2015. Displacement rates exceeded 10 mm/year between 2015 and 2022, with the Ponzano landslide moving 15 m in a few days during a slip event in February 2017. Time series analysis shows a substantial velocity change at both landslides following the August 24, 2016, earthquake and 2 months of above-average rainfall, with the Ponzano landslide slipping 15 m 6 months later. While our data does not provide a conclusive origin for this rapid slip event, the earlier changes suggest that earthquakes and rainfall can perturb landslide motion, possibly driving strain-induced material weakening and eventual failure.

In the last decade, the widespread hydrogeological instability has caused anomalies in several aspects of the natural earth life. Among them, the increase in landslides is an important concern for both the civil and scientific communities. In fact, the frequency of landslide events is progressively increasing, and with it, the demand for monitoring systems. Monitoring systems, on the other hand, can be implemented in a plethora of methods. In this regard, Wireless Sensor Networks (WSNs) are among the most innovative solutions, as they enable wireless landslide monitoring through the measurement of crucial parameters and their status assessment through long-range wireless technologies, such as the LoRa. This paper focuses on the implementation of a multi-sensorial WSN in the inhabited Macchia da Sole (TE), Italy, mountain locality, to monitor the main factors behind the landslide hazard, including the measurement of building cracks and underground water level. The main contribution of this paper is the level of autonomy achieved, as expressed by the average power consumption of 1.4 mW per node. Furthermore, the system integrates more types of sensors, and it is used in a zone with poor signal coverage.

Landslides pose significant risks to human life and infrastructure, driven by a complex interplay of geological and hydrological factors. This study investigates the ongoing slope instability affecting the village of Borrano, in Central Italy, where large-scale landslides are triggered or reactivated by extreme rainfall and seismic activity. A multidisciplinary approach was employed, integrating traditional geological surveys, direct investigations, and advanced geophysical techniques—including electrical resistivity tomography (ERT) and seismic refraction tomography (SRT)—to characterize subsurface structures. Additionally, Sentinel-1 interferometric synthetic aperture radar (InSAR) was employed to parametrize the deformation rates induced by the landslide. The results reveal a complex geological framework dominated by the Teramo Flysch, where weak clayey facies and structurally controlled dip-slopes predispose the area to gravitational instability. ERT and SRT identified resistivity and velocity contrasts associated with shallow and depth sliding surfaces. At the same time, satellite-based synthetic aperture radar (SAR) data confirmed persistent slow movements, with vertical displacement rates between −10 and −24 mm/year. These findings underscore the importance of lithological heterogeneity and structural settings in the evolution of landslides. The integrated geophysical and remote sensing approach enhances the understanding of slope dynamics. It can be used to cross-check interpretations, capture displacement trends, characterize the internal structure of unstable slopes, and resolve the limitations of each method. This synergy provides a more comprehensive assessment of complex slope instability, offering valuable insights for hazard mitigation strategies in landslide-prone areas.

Landslides and rockfalls can negatively impact human activities and cause radical changes to the surrounding environment. For example, they can destroy entire buildings and roadway infrastructure, block waterways and create sudden dams, resulting in upstream flooding and increased flood risk downstream. In extreme cases, they can even cause loss of life. External factors such as weathering, vegetation and mechanical stress alterations play a decisive role in their evolution. These actions can reduce strength, which can have an adverse impact on the slope’s ability to withstand failure. For rockfalls, this process also affects fragmentation, creating variations in the size, shape and volume of detached blocks, which influences propagation and impact on the slope. In this context, the Morino-Rendinara landslide is a clear example of rockfall propagation influenced by fragmentation. In this case, fragmentation results from tectonic stresses acting on the materials as well as specific climatic conditions affecting rock mass properties. This study explores how different fragmentation scales influence both velocity and landslide propagation along the slope. Using numerical models, based on lumped mass approach and stochastic analyses, various scenarios of rock material fracturing were examined and their impact on runout was assessed. Different scenarios were defined, varying only the fragmentation degree and different random seed sets at the beginning of simulations, carried out using the Rock-GIS tool. The results suggest that rock masses with high fracturing show reduced cohesion along joints and cracks, which significantly lowers their shear strength and makes them more prone to failure. Increased fragmentation further decreases the bonding between rock blocks, thereby accelerating landslide propagation. Conversely, less fragmented rocks retain higher resistance, which limits the extent of movement. These processes are influenced by uncertainties related to the distribution and impact of different alteration grades, resulting from variable tectonic stresses and/or atmospheric weathering. Therefore, a stochastic distribution model was developed to integrate the results of all simulations and to reconstruct both the landslide propagation and the evolution of its deposits. This study emphasizes the critical role of fragmentation and the volume involved in rockfalls and their runout behaviour. Furthermore, the method provides a framework for enhancing risk assessment in complex geological environments and for developing mitigation strategies, particularly regarding runout distance and block size.

Deep gravitational slope deformations (DsGSDs) are a geological and engineering challenge with important implications for slope stability, the reliability of existing infrastructures, land use and, above all, the safety of settlements. This paper focuses on the DsGSD phenomenon that affects a large part of the Borrano hamlet, located in the municipality of Civitella del Tronto (Abruzzo Region, Central Italy). This instability is characterized by slow movements of large volumes of material. The main factors initiating deformations are a combination of geological and hydrogeological aspects. These factors include the complex local stratigraphy, composed of pelitic and arenaceous facies at high slope dip angles, and extreme natural events such as heavy rainfall and earthquakes. This study employs a multidisciplinary approach integrating in field activities such as remote-controlled surface monitoring (clinometers and strain gauges), in-depth monitoring (inclinometers and piezometers), aero-photogrammetric analysis and numerical modelling. These techniques permitted us to characterize the evolution of the slope and to identify both the critical sliding surfaces and the mechanisms governing the ground movements. Soil deformations were mainly observed in the central zone of the hamlet. Significant deformations were recorded along planes of weakness at depth between arenaceous and pelitic materials. These planes represent contact zones between the clayey–marly facies, characterized by low strength, and the arenaceous facies, characterized by higher stiffness, creating a mechanical contrast that favours the development of large deformations. The numerical analyses confirmed good correlation with the monitoring data, revealing in detail the instability of both local and territorial processes. The 3D numerical analysis showed how the movements are controlled by planes of weakness, highlighting the key rule of geological discontinuities.

Debris flows represent a significant geohydrological hazard, impacting the surrounding environment and threatening human settlements by altering ecological equilibria. The formation of temporary, often unstable, natural dams that obstruct normal river flow and create secondary flood risks poses a complex and prolonged threat to the sustainable management of water resources. Non-invasive risk assessment and analysis tools are therefore essential for addressing this challenge effectively. In this context, this study uses an end-to-end numerical modelling approach validated on an actual river obstructed in past by a debris flow. The simulation focused on sustainable risk management after the landslide dam rupture. This computational methodology is a non-invasive technology that provides a fundamental alternative to costly and environmentally invasive field techniques for assessing the risk of complex river systems. Two separate numerical simulations were carried out using the HEC-RAS code. The first simulation used the integrated sediment transport module to quantify the dynamics of solid material deposition and dilution. The second simulation modelled secondary flooding scenarios using the dam break simulation module. The aim of integrating these non-invasive simulations is to analyse the interaction between the river and debris accumulation, understand the river's natural regeneration capacity and determine the hydraulic response to sudden dam failure. These results are essential for geohydrological risk assessment and mitigation, thereby improving the effectiveness of prevention measures and systemic resilience against landslides.

Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated.

The evaluation of landslide hazards in seismic areas is based on a deterministic analysis, which is unable to account for various uncertainties in the analysis process. This paper focuses on the probabilistic local seismic hazard analysis and extends the results to the landslide hazard analysis to consider both the uncertainties of the ground deformations and the strengths. The work studies the areas between Nibbiano and Sant’Erasmo hamlets in the Camerino municipality located in central Italy, where all constructions present evidence of damage caused by both the seismic sequence of 2016–2017 and the slope instability. An exhaustive geological and geophysical investigation has clarified the geological, geomorphological, and hydrogeological characteristics of the area, enabling a new characterization of material stress-strain behaviour. The study reveals that the low stiffness of the debris covers, and their fair degree of permeability contribute to potential instability scenarios triggered by both intense rainfall and the effects of strong earthquakes. The goal was to utilize the results to support local urban planning because in-depth knowledge of the possible evolutionary scenarios of the slopes is fundamental to the management of the degree of danger for structures, especially for people. Moreover, it was shown once again how a multi-source approach, with different investigation techniques, cannot be ignored for the study of the evolution of complex landslides.

The Sand Hypoplastic (SH) constitutive law by von Wolffersdorff (1996) is a widely used hypoplastic model for soil mechanics. This model includes eight parameters, usually calibrated using the oedometric (OE) and the drained isotropically consolidated triaxial tests (CD). However, previous studies show that the SH models calibration in the CD test has conflicting requirements in predicting the evolution of stresses and strains. In this work, we study the SH model calibration over a wide range of testing conditions using 12 OE and 25 CD tests by Wichtmann and Triantafyllidis (2016) on the Karlsruhe sands. The parameter space is extensively explored via genetic algorithm optimization (GA) using the recently developed open-source software GA-cal (available at https://github.com/FraJoMen/GA-cal ). This exploration allowed us to study the SH model’s predictive limits and to identify, using a multi-objective analysis, the main parameters governing the compromise between the accurate prediction of stresses versus strain in the CD tests.

Creating a digital model is one of the aims of the geotechnical engineers, to predict the land sliding hazard which occur in different regions in the world. In February 2017, an extensive landslide occurred in the hamlet of Ponzano as a sloped area in the Abruzzo region in Italy. In this regard, predicting the land sliding hazard is one of the important issues to prevent hazard to the civilizations. In this project we created a model in the 3D dimension through the Plaxis 3D numerical solution software from the data based of the region Abruzzo in 2007 to evaluate the land sliding hazard before happened and then compare the results with the data from the drone data surveyed recently in the 2022. In this regard, the data from the Abruzzo resources from 2007 imported into the QGIS as the open-source cross-platform software to analysis the geospatial data and then imported into the Recap software to work on the point cloud data and then imported into the Civil 3D software to create a solid surface from the TIN surface and finally since the solid surface contains a large number of irrelative details, they were imported into the Rhinoceros software to create a NURBS surface to be smoothed for better performance in the analysis. The NURBS surface imported into the Plaxis and all of the geometry and geotechnical engineering parameters by considering the investigated geotechnical survey that was conducted in parallel in the area, defined for the model. The "Hardening Soil" model considered for the 1st layer as the "clay and lime" and the "Hoek-Brown" model defined for the 2nd layer as the "marl-flysch". A fine mesh elements distribution assessed also for the model. The phases defined as the "gravity" to define the unit weight of the soil layers, the "plastic" phase to calculate the instant deformations and the "consolidation" to analyze the plastic deformations in the sloped area of the model. In parallel, the drone data achieved in 2022, were imported into the CloudCompare as the 3D point cloud processing software and different methods such as the "segment", "statistical outlier Filter", "CSF filter", "noise filter", "cross section" etc. were performed to clean the data and then imported into the Recap software to work on data and then imported into the Civil 3D software to create solid surface of the current data after the land sliding. In this regard, to evaluate the displacement occurred from the year 2007 toward the 2022, a TIN volume surface as the colored map created through the Civil 3D software to show the displacements in the z direction and all of the results were compared with the Plaxis 3D numerical solution software. The results showed that the colored map with the displacement in the positive and negative direction of the z is the same of the analyzed model and the values match each other's and we created a digital model of the selected area to predict the land sliding hazard in the region in the following.

Landslide events pose a significant threat to human settlements, with potentially devastating consequences for infrastructure and public safety. In the years, several ways have been developed to monitor these phenomena; among them, the use of Wireless Sensor Networks (WSNs) is a widely considered choice because of their flexibility and suitability with Internet of Things (IoT) based structures. In this applied research work, a Long Range (LoRa) based WSN for landslide monitoring in the locality of Tortoreto, Italy, is presented. This work has been carried out in collaboration with the municipality in order to ensure the correct choice of an action plan in case of a main landslide event and to observe the natural displacement of the ground. The monitoring solution is aimed to offer reliability at reduced complexity and cost, allowing a preliminary data acquisition campaign for the definition of safety procedures. The system has been in operation since October 2023 with consistent uptime, allowing to gather important information on the movement of the householding foundations. Each node is composed of two sensors and a conditioning circuit for the reading of an external sensor. The single node has an average power consumption in the order of hundreds of microwatt.

Debris flows are a dynamic and hazardous geological phenomenon, as by definition, debris flows are rapid, gravity-driven flows of saturated materials that often contain a mixture of water, rock, soil, and organic matter. They are highly destructive and occur in steep channels, posing a significant threat to infrastructure and human life. The dynamics of debris flows are complex due to their non-Newtonian behaviour and varying sediment–water interactions, making accurate modelling essential for risk mitigation and emergency planning. This paper reports and discusses the results of numerical simulations of back analyses aimed at studying the reconstruction of a real rapid debris flow. The selected test case is the event that occurred on 12 and 16 March 2021 along the Rio Sonno channel, a tributary of the Liri River, following the landslide event of Rendinara (Municipality of Morino, Abruzzo Region, Italy). There are significant flow sources in the area, fed by a highly fractured carbonaceous aquifer that extends immediately upslope of the detachment zone. The continuous flow influences the saturation level in the fine-grained sediments and favours the triggering of the debris flow. This phenomenon was simulated using the commercial RAMMS code, and the rheological model selected was “Voellmy fluid friction”. The modelling approaches used in this research are valid tools to estimate the volumes of materials involved in the flow-feeding process and for the purpose of possible mitigation works (debris flow-type dams, weirs, flow diversion, etc.).

Cascading landslides represent a dynamic and hazardous geological phenomenon, char-15 acterized by a sequential chain of slope failures triggered by various factors such as heavy rainfall, 16 seismic activity, or anthropogenic activities. These events can amplify the damage caused by the 17 initial trigger and propagate instability along a slope, often resulting in significant environmental 18 and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici mountains 19 along the border of Abruzzo and Lazio region (Italy), serves as a notable example of the complexities 20 and devastating consequences associated with such events. In March 2021, a substantial debris flow 21 event in Morino obstructed the Liri River, marking the latest step in a series of landslide events 22 characterized by a complexity far beyond initial expectations. This study employs a multidiscipli-23 nary approach, combining conventional techniques and advanced technologies, to understand the 24 complexities of the Morino-Rendinara landslide. Field activities, UAV image acquisition, SAR inter-25 ferometry based on SENTINEL-1 images and pixel offset analysis based on high-resolution Google 26 Earth images, offer insights into the geological and hydrogeological setting of the unstable slope, 27 landslide geometry, mechanism and kinematics. To supplement the analysis a specific FEM slope 28 stability analyses is used to reconstruct the deep geometry of the system emphasizing the modula-29 tion action of groundwater flow to the slope stability

Landslides represent a major problem in human activities, especially in the proximity of cities or infrastructure. In this paper, we present the peculiar case of Pietracamela, Italy. Pietracamela is a small village located in the Central Apennines, a few kilometers north of the Gran Sasso Mountain. The peculiarity of the case study is the fact that the village is simultaneously affected by two different types of slope instabilities. The southwest sector, representing the historical part of the village, has been affected by large rockfalls generated from the “Capo le Vene” cliff located in the south of the village. The northeastern sector of the village represents the most recent urbanized area and is involved in a deep-seated landslide that, in the last decades, has damaged buildings and infrastructure. In this context, we have used two different types of remote sensing techniques to study the two phenomena. The rockfall area has been surveyed through the use of an Unmanned Aerial Vehicle (UAV) that allowed the definition of main joint sets and the volume of blocks associated with the most recent (2011) rockfall event. Three main joint sets have been highlighted, which are responsible for the failure of the “Capo le Vene” cliff. The volume of blocks that failed during the last rock failure in 2011 ranged from a few to 1500 m3. The deep-seated landslide has been studied by analyzing borehole data and 20 years of InSAR data from ERS1/2, ENVISAT, COSMO-SkyMed, and SENTINEL-1. It has been highlighted by InSAR analysis that the northeast sector of the village shows a perfectly linear displacement trend that generates movements up to about 230 mm (about 1 cm/year).

Creating a digital model is one of the aims of the geotechnical engineers, to predict the land sliding hazard which occur in different regions in the world. In February 2017, an extensive landslide occurred in the hamlet of Ponzano as a sloped area in the Abruzzo region in Italy. In this regard, predicting the land sliding hazard is one of the important issues to prevent hazard to the civilizations. In this project we created a model in the 3D dimension through the Plaxis 3D numerical solution software from the data based of the region Abruzzo in 2007 to evaluate the land sliding hazard before happened and then compare the results with the data from the drone data surveyed recently in the 2022. In this regard, the data from the Abruzzo resources from 2007 imported into the QGIS as the open-source cross-platform software to analysis the geospatial data and then imported into the Recap software to work on the point cloud data and then imported into the Civil 3D software to create a solid surface from the TIN surface and finally since the solid surface contains a large number of irrelative details, they were imported into the Rhinoceros software to create a NURBS surface to be smoothed for better performance in the analysis. The NURBS surface imported into the Plaxis and all of the geometry and geotechnical engineering parameters by considering the investigated geotechnical survey that was conducted in parallel in the area, defined for the model. The “Hardening Soil” model considered for the 1st layer as the “clay and lime” and the “Hoek-Brown” model defined for the 2nd layer as the “marl-flysch”. A fine mesh elements distribution assessed also for the model. The phases defined as the “gravity” to define the unit weight of the soil layers, the “plastic” phase to calculate the instant deformations and the “consolidation” to analyze the plastic deformations in the sloped area of the model. In parallel, the drone data achieved in 2022, were imported into the CloudCompare as the 3D point cloud processing software and different methods such as the “segment”, “statistical outlier Filter”, “CSF filter”, “noise filter”, “cross section” etc. were performed to clean the data and then imported into the Recap software to work on data and then imported into the Civil 3D software to create solid surface of the current data after the land sliding. In this regard, to evaluate the displacement occurred from the year 2007 toward the 2022, a TIN volume surface as the colored map created through the Civil 3D software to show the displacements in the z direction and all of the results were compared with the Plaxis 3D numerical solution software. The results showed that the colored map with the displacement in the positive and negative direction of the z is the same of the analyzed model and the values match each other’s and we created a digital model of the selected area to predict the land sliding hazard in the region in the following.

Rockfall phenomena are caused by the exposure of rock masses to weather and erosion. Over time, a rock or boulder can become unstable and fall along a slope. To monitor these gravitative elements, several methodologies based on sensor networks have been developed in recent times; among these we find geotechnical monitoring Wireless Sensor Network (WSN). The flexibility of these structures makes them a well-suited solution. In this paper, a multi-technological rockfall-oriented WSN is presented; this system is composed of several Long Range (LoRa) based sensors that retrieve various geotechnical parameters, along with other cellular based nodes for rock impact monitoring. Sensors of different technologies are integrated in the network to obtain a wider spectrum of monitored events in order to enable a complete observation possibility for the status of the rock formations. The entire system follows an Internet of Things (IoT) scheme, where data is accessible anytime from an online platform. The experimental system is installed in the locality of San Demetrio Ne' Vestini, Italy, in particular at the Grotte di Stiffe site.

The values of the physical–mechanical properties of any soil are affected by uncertainties both due to experimental measurements and the impossibility of knowing them, in detail, at every point of the spatial domain. Accordingly, this work focuses on uncertainty in shear wave velocity (Vs) and its impact on the seismic response. The Monte Carlo method, based on pseudo-random number generation, was selected. To understand which random distributions could identify the site’s real conditions, the Fourier spectrum frequencies were calculated for each realization and were compared with the predominant natural site frequency. The experimental range data were used to calculate the spectral average acceleration and the horizontal amplification factors. The simulations were performed and interpreted by a modified version of VisualQ4M software based on 2D Quad4M, including the generation of pseudo-random numbers and pre- and post-data processing. A site at a small scale, in the territory of the city of L’Aquila (Italy), was selected as the test case. This paper demonstrates, from a numerical point of view, that both a simple local topographic modification due to excavation and the uncertainties of the numerical values, even of the shear wave velocity alone, can have an important impact on the local seismic amplification.