A probabilistic approach to risk assessment of slow slope movements

Hazard

A novel intelligent system for securing cash levels using Markov random fields

Financial support from the Spanish Ministry of Universities "Disruptive group decision making systems in fuzzy context: Applications in smart energy and people analytics" (PID2019-103880RB-I00), and Junta de Andalucia (SEJ340) is gratefully acknowledged.The maintenance of cash levels under certain security thresholds is key for the health of the banking sector. In this paper, the monitoring process of branch network cash levels is performed using a single intelligent system which should provide an alert when there are cash shortages at any point of the network. Such an integral solution would provide a unified insight that guarantees that branches with similar cash features are secured as a whole. That is to say, a triggered alarm at a specific branch would indicate that attention must also be paid to similar (in-cash-feature) branches. The system also incorporates a (complementary) specific treatment for individual branches. The Early Warning System for securing cash levels presented in this paper (cash level EWS) is deliberately free of local demographic specifications, thereby overcoming the current lack of worldwide definitions for local demographics. This aspect would be particularly valuable for banking institutions with branch networks all over the world. A further benefit is the cost reductions that are a result of replacing several approaches with a single global one. Instead of local demographic parameters, a solid theoretical model based on Markov random fields (MRFs) has been developed. The use of MRFs means a reduction in the amount of information required. This would mean a higher processing speed as well as a significant reduction in the amount of storage capacity required. To the best of the author's knowledge, this is the first time that MRFs have been applied to cash monitoring.Spanish Ministry of Universities PID2019-103880RB-I00Junta de Andalucia SEJ34

Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning

Predicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects

Stiffness and strength of stabilized organic soils—part i/ii: Experimental database and statistical description for machine learning modelling

This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper.</jats:p

Probabilistic calibration of a dynamic model for predicting rainfall-controlled landslides

Italy has a number of regions with mid to high vulnerable areas from a hydrogeological point of view. The causes are the result of both the fragility of territory and the anthropic influence on its continuous modifications. A quantitative landslide risk analysis is then necessary to avoid or reduce human life and property losses. In particular, the prediction of landslide occurrence should be estimated taking into account the uncertainties affecting the analysis process. In this paper, a specific type of landslide, triggered by rainfall and characterized by the viscous behavior of soil, is discussed and analyzed. The goal is to illustrate the applicability of a probabilistic approach, based on Bayesian theorem, which aims at developing an advanced analysis, and to predict slow-slope movements. The proposed methodology relies on the probabilistic calibration of a well-defined, viscoplastic-dynamic model that is able to predict the soil mass displacement evolution from groundwater level inputs and return a value of a mobilized friction angle. Making use of a well-established and highly reliable monitoring database of the Alvera landslide, the model is probabilistically calibrated by the Markov-chain Monte Carlo method. Starting from the prior and the likelihood, this numerical method allows sampling of the posterior, which represents the solution of probabilistic calibration given in the form of probability density functions for each model parameter, including the corresponding correlation structure. Furthermore, the uncertainty related to model predictions is fully described

Hazard assessment of slow slope movements

In recent years, landslide risk assessment has gained significant and ever increasing importance. In fact, soil and rock movements are natural threats that represent the major risk for both the population and infrastructure, particularly due to the anthropic influence on the continuous modifications of the territory. This is a typical scenery of the North Apennines region, in Italy. This area is in fact characterized by a high frequency of landslide events that often cause economic losses associated to human activities. From a geological point of view the North Apennines can be represented in a schematic way as a chain of stratums developed as a result of the collision of two Continental blockades. The formations show the following sequence, from bottom to top: sandstones-marls succession (Tuscan-Umbrian Domain), clays-marly clays (Subligurian and Ligurian Domains) and sedimentary material (clays-sandy clays, Epiligurian Domain). Most landslides occurring in this area consist of shallow movements, which involve fine, essentially clay material and the common movement is a translational or a roto-translational sliding. According to the Varnes classification, they can be identified as extremely slow or very slow movements, with velocities typically of few centimetres per year. The main triggering factor is hydrologic, since movements are usually strictly connected to ground water level fluctuations. The availability of a well established and highly reliable monitoring database of a few landslides located in the area \u2013 composed of inclinometer and piezometer records \u2013 has enabled the investigation of a new approach to predict soil movements. This paper discusses the case of a extensively monitored landslide. A well-defined dynamic-viscous model capable of returning a displacement prediction from a groundwater level input was considered. The deterministic solution of the inverse problem was performed by segmenting the historic data in start-end motions, allowing for the generation of empirical probability density functions of model initial condition parameters. By sampling these empirical functions using Monte-Carlo simulations the remaining model parameters were retrieved by the nonlinear least squares. In this way, all parameters were represented using a probability density function. Once the deterministic solution to the inverse problem is completed, it follows to solve the probabilistic inverse problem by the Bayesian approach. At this stage, both the prior and likelihood have been obtained, which permits the use of Markov-Chain Monte Carlo methods to sample the posterior, given in the form of probability density function for each model parameter conditioned on site specific data, including their corresponding correlation structure. Such approach represents a more rational tool for future risk management, as it enables to take into account the available information in a more effective way and to quantify the uncertainty related to the predictions

Un approccio probabilistico per la modellazione di movimenti lenti di versante

Negli ultimi anni, la valutazione del rischio da frane ha assunto un significato ed un'importanza sempre crescenti. I movimenti di versante, sia in terra sia in roccia, sono, infatti, tra i maggiori pericoli naturali e rappresentano una seria minaccia per la popolazione e le proprietà. Una particolare tipologia di frana, molto diffusa nel territorio italiano, consiste in movimenti lenti, traslativi, relativamente superficiali, che coinvolgono materiale fine, essenzialmente argilla. Il fattore scatenante principale è idrologico, per cui gli spostamenti sono connessi alle fluttuazioni del livello di falda. Per studiare il comportamento di tali frane è stato messo a punto un modello dinamico visco-plastico, capace di prevedere uno spostamento a partire da un valore del livello piezometrico e di restituire una stima dell'angolo di resistenza al taglio mobilitato. Facendo uso di un affidabile ed eccezionalmente prolungato database di monitoraggio relativo ad un ben documentato caso di studio, è stata eseguita una calibrazione probabilistica del modello attraverso un approccio di tipo Bayesiano. In questo modo, la soluzione del problema inverso viene espressa in forma di funzione di densità di probabilità per ognuno dei parametri del modello, includendo la loro struttura di correlazione. Un simile approccio rappresenta uno strumento di analisi più razionale per la gestione del rischio, in quanto permette da un lato di quantificare le incertezze legate alle previsioni del modello, dall'altro di esportare ed utilizzare la conoscenza sui parametri acquisita in uno specificato sito in altri casi di frana, in una maniera quantitativa e rigorosa

Uncertainty quantification in the calibration of a dynamic viscoplastic model of slow slope movements

Most landslides occurring in Italy consist of shallow translational movements, which involve fine, essentially clayey material. They are usually characterized by low velocities, typically of few centimeters per year. The main triggering factor is hydrologic, since movements are usually strictly connected to groundwater level fluctuations. This slow and periodical trend can be interpreted by a viscous soil response, and in order to catch the actual kinematics of the soil mass behavior, a dynamic analysis should be adopted. This paper discusses the case of the Alverà mudslide, located in the Northern Alps (Italy), for which a very detailed and almost 9-year-long monitoring database, including displacements and groundwater levels records, is available. A well defined dynamic viscoplastic model, capable of returning a displacement prediction and a mobilized shear strength angle estimate from a groundwater level input, was considered. A first deterministic calibration proved the ability of the model to reproduce the mudslide overall displacements trend if a suitable reduction of the mobilized angle is allowed. Then, an uncertainty quantification analysis was performed by measuring the model parameters variability, and all parameters could be represented using a probability density function and a correlation structure. As a consequence, it was possible to define a degree of uncertainty for model predictions, so that an assessment of the model reliability was obtained. The final outcome is believed to represent an important advancement in relation to hazard assessment and for future landslide risk management