Monitoring instrumentation plays a major role in the study of natural phenomena and analysis for risk prevention purposes, especially when facing the management of critical events. Within the ...geotechnical field, data collection has traditionally been performed with a manual approach characterized by time-expensive on-site investigations and monitoring devices activated by an operator. Due to these reasons, innovative instruments have been developed in recent years in order to provide a complete and more efficient system thanks to technological improvements. This paper aims to illustrate the advantages deriving from the application of a monitoring approach, named Internet of natural hazards, relying on the Internet of things principles applied to monitoring technologies. One of the main features of the system is the ability of automatic tools to acquire and elaborate data independently, which has led to the development of dedicated software and web-based visualization platforms for faster, more efficient and accessible data management. Additionally, automatic procedures play a key role in the implementation of early warning systems with a near-real-time approach, providing a valuable tool to the decision-makers and authorities responsible for emergency management. Moreover, the possibility of recording a large number of different parameters and physical quantities with high sampling frequency allows to perform meaningful statistical analyses and identify cause-effect relationships. A series of examples deriving from different case studies are reported in this paper in order to present the practical implications of the IoNH approach application to geotechnical monitoring.
A hypoplastic approach to constitutive modelling was developed by Kolymbas 1996 considering a non-linear tensor function in the form of strain and stress rate. However, the implicit formulation of ...the hypoplastic model with indirect material parameters severely limits its applicability to real-world geotechnical problems. In many cases, the numerical analysis of geotechnical problems relies on simple elastoplastic constitutive models that cannot model a wide range of soil response aspects. One promising paradigm of constitutive modelling in geotechnics is hypoplasticity, but many of the hypoplastic models belong to advanced models. In the article, we present the simple hypoplastic model as an alternative to the widely used Mohr-Coulomb elastoplastic model.
The authors present the law protection of cultural monuments in the Slovak Republic and the outputs of an interesting case study, more specifically the creation of a 3D model with real textures of ...Hricov Castle, which was created in 2019 with additional improvements done in 2022. Ruins of Hricov Castle are located in the village of Hricovske Podhradie, 15 km from Zilina. For model creation, devices obtained within the project of the Broker centre of air transport for the transfers of technology and knowledge into transport and transport infrastructure was used. The 3D model of Hricov Castle created by team leader Tomas Cesnek currently presents one of the most realistic Slovak digital 3D models created by laser scanning technology. This model will be a part of the permanent exhibition of Hricov Castle in the historical toll building from 1556 in Dolny Hricov, Slovakia, the reconstruction of which is ensured by the members The Association of Friends of Hricov Castle. Model creation methodology and the use of special textures should be used to produce more realistic laser scan 3D models.
During recent years, the availability of innovative monitoring instrumentation has been a fundamental component in the development of efficient and reliable early warning systems (EWS). In fact, the ...potential to achieve high sampling frequencies, together with automatic data transmission and elaboration are key features for a near-real time approach. This paper presents a case study located in Central Italy, where the realization of an important state route required a series of preliminary surveys. The monitoring system installed on site included manual inclinometers, automatic modular underground monitoring system (MUMS) inclinometers, piezometers, and geognostic surveys. In particular, data recorded by innovative instrumentation allowed for the detection of major slope displacements that ultimately led to the landslide collapse. The implementation of advanced tools, featuring remote and automatic procedures for data sampling and elaboration, played a key role in the critical event identification and prediction. In fact, thanks to displacement data recorded by the MUMS inclinometer, it was possible to forecast the slope failure that was later confirmed during the following site inspection. Additionally, a numerical analysis was performed to better understand the mechanical behavior of the slope, back-analyze the monitored event, and to assess the stability conditions of the area of interest.
The introduction of Machine Learning (ML) in the geotechnical community has led to numerous applications for monitoring data elaboration. These techniques demonstrate promising performance in ...comparison to conventional methods aimed at determining the future behavior of a landslide. In this context, it is fundamental to have access to reliable methodologies and procedures to assess the quality of algorithms' predictions. This article proposes an improved method for evaluating ML algorithms applied to landslide time series analysis. The method relies on modified metrics that are sensible to biased classification due to imbalanced datasets, also enabling the evaluation of both regression and classification models using the same criteria. The calculated metrics include Accuracy, Precision, Recall, and F1-Score, each one representing a different aspect of the forecasting model effectiveness. Results obtained from the application of the proposed method to datasets collected by automated monitoring systems proved to be informative of the performance of the model and provides the means for objective comparison with other forecasting algorithms, making it a valuable tool to improve the prediction process reliability. In particular, the custom metrics allowed for a better evaluation of algorithms skewed in favor of the dominant class/classes, which are common occurrence in landslide displacement datasets. In these cases, the proposed approach highlighted the inability of the forecasting model in predicting critical events, presenting a more accurate representation of its performances compared to results obtained with standard approaches.
•Informative and agreed-upon metrics are important for evaluating complex algorithms.•Landslide datasets and time series are often unbalanced (few large displacements).•Modified metrics works for both classifiers and regression models, for comparisons.•New metrics are suited for unbalanced data and potentially unbalanced predictors.
The forecast of a landslide's time of failure and the definition of alert thresholds are fundamental aspects in the study of natural hazards. However, these tasks are particularly difficult due to ...the large number of parameters and factors involved, and are therefore usually performed with a site-specific approach. This work describes an attempt to generalize the behavior of a landslide approaching collapse, with particular attention to the definition of a general criterion to define alert thresholds. The procedure started with the creation of a database of displacement data recorded for historical landslides, then the inverse velocity model was applied to these datasets to evaluate the time of failure under the assumption of linear behavior during the accelerating phase. A model calibration was conducted to best describe the monitored data and highlight any non-linear trend. A curve describing the velocity versus time relationship was then computed for each single slope failure case using the parameter obtained through this operation. In the final step of the study, these curves were processed with a normalization procedure, thus obtaining a dimensionless velocity-related coefficient. This parameter allowed the comparison of different landslide datasets on a single graph, which can be used as a general reference to define alert thresholds for emergency purposes. In order to test the criterion's ability to represent landslide behavior, the procedure was also applied to a different case by simulating progressive data acquisition.
•The inverse velocity method can be a reliable approach for time-of-failure forecast.•Model calibration displays a possible non-linearity of the landslide evolution.•Velocity normalization allows the definition of a common behaviour of different landslides.•Dimensionless velocity can be a useful parameter to determine alert thresholds.
Over the past years, the growing number of natural hazards all over the world has led to an increasing focus on activities aimed at studying and controlling the occurrence of these phenomena. In this ...context, monitoring systems have become a fundamental component for Landslide Early Warning Systems, allowing to understand the evolution of these processes and assess the need for dedicated mitigation measures. This result is achieved thanks to several technological advancements that led to the introduction of more accurate and reliable sensors, as well as automatic procedures for data acquisition and elaboration. However, despite these improvements, the data interpretation process is still a challenging task, in particular when it comes to the identification of critical events and failure forecasting operations. This paper presents a methodology developed to assess if a potentially critical event is displaying a significant deviation from previously sampled data, or if it could be classified as a false alarm. The process relies on the definition of a threshold value based on the landslide behavior preceding the event of interest. In particular, the reference value derives from the evaluation of equivalent displacements, defined as the displacements previously observed in a time interval equal to the one showed by the potentially critical event. This paper reports a series of examples referring to different case studies, involving both false alarms and real collapses, underlining the effectiveness of the proposed model as a useful tool to evaluate the landslide behavior with a near-real-time approach.
Ground temperature at shallow depth (< 50 m) is not stable, nor in space, neither in time, and its behaviour is the result of superimposition of effects of heat pulses of different origin: solar, ...geothermal and anthropic. The correct assessment of ground temperature is a crucial point when designing a shallow geothermal energy system. In geothermal closed loop projects, more the borehole heat exchangers are short, more the contribution of the ground temperature variability on the heat exchange is prominent. Monitoring ground temperature can be very useful to correctly understand the behaviour of a shallow geothermal reservoir subjected to heat extraction and/or injection by a ground source heat pump system. The present work illustrates the practical aspects and main issues occurred in the installation, testing and working phases of a monitoring system realised to record ground temperature in a geothermal application. The case study is a field of eight coaxial borehole heat exchangers, 30 m long, connected to a novel prototype of dual source (air and ground) heat pump.
Landslides represent a serious hazard in many areas around the world, potentially leading to human losses and significant damages to structures and buildings. For this reason, over the years a ...consistent number of studies and researches have been carried out to analyse these natural phenomena and their evolution. This study presents the application of an automatic procedure specifically developed to identify the onset of landslide acceleration by analysing monitoring displacement data with a multi-criteria approach. The proposed procedure aims to identify this point by applying a four-level validation process on a pre-determined dataset. Once the analysis returns a positive result for a certain number of monitoring data, it is possible to state that the landslide reached the accelerating phase of its evolution, thus allowing to define a specific point representing the onset of acceleration. The method was applied to several historical case studies taken from scientific literature, in order to test its practicability and effectiveness. This procedure could be especially useful in Early Warning Systems where time of failure forecasting models are implemented, allowing to improve their performances by providing an automated and reliable procedure to define the beginning of potentially critical landslide events.
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