ANN Model for Prediction of Rockfill Dam Slope Stability

Dam safety and potential failure is one of the issues with the highest risk in water resources management. The dam slope stability is adversely influenced by the natural seepage process in the dam. Thus, monitoring of the pore and total pressures in the dam core is essential in the seepage process analysis. It is possible during the dam operation period to have one or more cells malfunctioning, after years of operation. Sometimes it is technically not possible to replace the cell or the costs of the replacement are too high and not economically justified. At the Pridvorica Dam, several instruments - cells for pore and total pressure monitoring malfunctioned. The objective of this study is to develop a neural network model for the prediction of the pore and total pressure on the malfunctioning cells and to demonstrate its quick and effective practical application for identifying complex non-linear relationship between the input and output variables. The proposed approach can be a very helpful tool for modeling of the stochastic behavior of the dam in order to give adequate warning of soil pressures to prevent failures

Studying seepage in a body of earth-fill dam by (Artifical Neural Networks) ANNs

Thesis (Master)--Izmir Institute of Technology, Civil Engineering, Izmir, 2006Includes bibliographical references (leaves: 73-75)Text in English; Abstract: Turkish and Englishx, 75 leavesDams are structures that are used especially for water storage , energy production, and irrigation. Dams are mainly divided into four parts on the basis of the type and materials of construction as gravity dams, buttress dams, arch dams, and embankment dams. There are two types of embankment dams: earthfill dams and rockfill dams. In this study, seepage through an earthfill dam's body is investigated using an artificial neural network model. Seepage is investigated since seepage both in the dam's body and under the foundation adversely affects dam's stability. This study specifically investigated seepage in dam.s body. The seepage in the dams body follows a phreatic line. In order to understand the degree of seepage, it is necessary to measure the level of phreatic line. This measurement is called as piezometric measurement. Piezometric data sets which are collected from Jeziorsko earthfill dam in Poland were used for training and testing the developed ANN model. Jeziorsko dam is a non-homogeneous earthfill dam built on the impervious foundation. Artificial Neural Networks are one of the artificial intelligence related technologies and have many properties. In this study the water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the artificial neural network model. In the line of the purpose of this research, the locus of the seepage path in an earthfill dam is estimated by artificial neural networks. MATLAB 6 neural network toolbox is used for this study.Text in English; Abstract: Turkish and English

Dam Safety. Overtopping and Geostructural Risks

This reprintshows recent advances in dam safety related to overtopping and the prevention, detection, and risk assessment of geostructural risks. Related to overtopping, the issues treated are: the throughflow and failure process of rockfill dams; the protection of embankment dams against overtopping by means of a rockfill toe or wedge-shaped blocks; and the protection of concrete dams with highly convergent chutes. In the area of geostructural threats, the detection of anomalies in dam behavior from monitoring data using a combination of machine learning techniques, the numerical modeling of seismic behavior of concrete dams, and the determination of the impact area downstream of ski-jump spillways are also studied and discussed. In relation to risk assessment, three chapters deal with the development of fragility curves for dikes and dams in relation to various failure mechanisms

A multi-target prediction model for dam seepage field

Prediction of dam behavior based on monitoring data is important for dam safety and emergency management. It is crucial to analyze and predict the seepage field. Different from the mechanism-based physical models, machine learning models predict directly from data with high accuracy. However, current prediction models are generally based on environmental variables and single measurement point time series. Sometimes point-by-point modeling is used to obtain multi-point prediction values. In order to improve the prediction accuracy and efficiency of the seepage field, a novel multi-target prediction model (MPM) is proposed in which two deep learning methods are integrated into one frame. The MPM model can capture causal temporal features between environmental variables and target values, as well as latent correlation features between different measurement points at each moment. The features of these two parts are put into fully connected layers to establish the mapping relationship between the comprehensive feature vector and the multi-target outputs. Finally, the model is trained for prediction in the framework of a feed-forward neural network using standard back propagation. The MPM model can not only describe the variation pattern of measurement values with the change of load and time, but also reflect the spatial distribution relationship of measurement values. The effectiveness and accuracy of the MPM model are verified by two cases. The proposed MPM model is commonly applicable in prediction of other types of physical fields in dam safety besides the seepage field

Trends and Prospects in Geotechnics

The Special Issue book presents some works considered innovative in the field of geotechnics and whose practical application may occur in the near future. This collection of twelve papers, in addition to their scientific merit, addresses some of the current and future challenges in geotechnics. The published papers cover a wide range of emerging topics with a specific focus on the research, design, construction, and performance of geotechnical works. These works are expected to inspire the development of geotechnics, contributing to the future construction of more resilient and sustainable geotechnical structures

A Hybrid of Artificial Neural Networks and Particle Swarm Optimization Algorithm for Inverse Modeling of Leakage in Earth Dams

A new intelligent hybrid method for inverse modeling (Parameter Identification) of leakage from the body and foundation of earth dams considering transient flow model has been presented in this paper. The main objective is to determine the permeability in different parts of the dams using observation data. An objective function which concurrently employs time series of hydraulic heads and flow rates observations has been defined to overcome the ill-posedness issue (nonuniqueness and instability of the identified parameters). A finite element model which considers all construction phases of an earth dam has been generated and then orthogonal design, back propagation artificial neural network and Particle Swarm Optimization algorithm has been used simultaneously to perform inverse modeling. The suggested method has been used for inverse modeling of seepage in Baft dam in Kerman, Iran as a case study. Permeability coefficients of different parts of the dam have been inspected for three distinct predefined cases and in all three cases excellent results have been attained. The highly fitting results confirm the applicability of the recommended procedure in the inverse modeling of real large-scale problems to find the origin of leakage channels which not only reduces the calculation cost but also raises the consistency and efficacy in such problems

Model order reduction methods for sensor data assimilation to support the monitoring of embankment dams

Tesi en modalitat de cotutela; Universitat Politècnica de Catalunya i Université libre de BruxellesThe latest monitoring and asset management technologies for large infrastructures involve digital representations that integrate information and physical models, exist in parallel to the real-life structures, and are continuously updated based on assimilated sensor data, in order to accurately represent the actual conditions in the structures. This type of technology is often referred to as Digital Twin. The implementation of such cutting-edge technology in monitoring assets like tailings dams, or embankment dams in general, and other large structures, implies the development of highly efficient numerical tools that, combined with sensor data, may support rapid, informed decision making. For the particular case of embankment dams, enabling this type of technology requires an efficient numerical model that describes the coupled hydro-mechanical phenomena, pertinent to a dam functioning and safety. This may for instance be a Finite Elements (FE) model, describing the groundwater flow through unsaturated porous geomaterials. The process of updating and calibrating a model, such as the above mentioned FE model, based on sensor data is typically referred to as data assimilation. Often, this is achieved via an optimization approach, where a specific problem is solved multiple times for various parametric values, in search for the values that best describe the sensor data. The bottleneck in this type of application is typically the cost of multiple evaluations of the model, that may become prohibitive when the underlying FE model is large. In order to enable such applications, the present work proposes Model Order Reduction (MOR) methods tailored to the hydro-mechanical nonlinear problem at hand. MOR aims at the creation of a surrogate model that seeks an approximation of the FE solution in a reduced-order space. This is achieved by applying an offline-online strategy. In the offline stage, the solution manifold of the full-order problem is sampled, in order to identify a low-order affine subspace, where an accurate approximation of the full-order solution can be captured. To tackle the nonlinearities related to partially saturated conditions in the soil, a similar strategy must be employed in order to define reduced-order spaces where an affine system approximation may be recovered. The resulting Reduced Order Model (ROM) may be used as an efficient surrogate to the FE model in any problem that requires fast and/or repetitive solutions. In this work, MOR techniques are implemented to solve the coupled nonlinear transient problem under consideration. ROMs are created to solve problems that pertain to tailings dams and embankment dams monitoring. The efficiency and the accuracy of these models are demonstrated by solving inverse problems for parametric identification. MOR is found to be a reliable tool, significantly accelerating the inverse identification process while resulting to accurate solutions.Las últimas tecnologías de monitorización y gestión de proyectos como grandes infraestructuras implican modelos digitales que integran información y modelos físicos, existen en paralelo a las estructuras reales y se actualizan continuamente en función de datos de sensores asimilados, con el fin de representar con precisión las condiciones reales de las estructuras. Este tipo de tecnología suele denominarse Digital Twin. La aplicación de esta tecnología de vanguardia en la gestión de grandes obras de infraestructura como las presas de residuos mineros, o las presas de tierra o de materiales sueltos en general, y otras estructuras de gran tamaño, implica el desarrollo de herramientas numéricas muy eficientes que, combinadas con los datos de los sensores, permiten una toma de decisiones rápida e informada. Para el caso particular de las presas de terraplén, habilitar este tipo de tecnología requiere un modelo numérico eficiente que describa los fenómenos hidromecánicos acoplados, pertinentes para el funcionamiento y la seguridad de una presa. Puede tratarse, por ejemplo, de un modelo de elementos finitos (EF) que describa el flujo de agua subterránea a través de geomateriales porosos no saturados. El proceso de actualización y calibración de un modelo, como el modelo de elementos finitos mencionado anteriormente, basado en los datos de los sensores se denomina normalmente asimilación de datos. A menudo, esto se consigue mediante un enfoque de optimización, en el que un problema específico se resuelve múltiples veces para varios valores paramétricos, en busca de los valores que mejor describen los datos de los sensores. El obstáculo en este tipo de aplicaciones suele ser el coste de las múltiples evaluaciones del modelo, que puede llegar a ser prohibitivo cuando el modelo de EF es grande. Para permitir este tipo de aplicaciones, el presente trabajo propone métodos de reducción del orden del modelo (MOR) adaptados al problema hidromecánico no lineal en cuestión. MOR tiene como objetivo la creación de un modelo sustituto que busca una aproximación de la solución de EF en un espacio de orden reducido. Esto se consigue aplicando una estrategia offline-online. En la etapa offline, se muestrea el colector de soluciones del problema de orden completo, con el fin de identificar un subespacio afín de orden reducido, en el que se pueda capturar una aproximación precisa de la solución de orden completo. Para abordar las no linealidades relacionadas con las condiciones de saturación parcial del suelo, debe emplearse una estrategia similar para definir espacios de orden reducido en los que pueda recuperarse una aproximación del sistema afín. El Modelo de Orden Reducido (MOR) resultante puede ser utilizado como un sustituto eficiente del modelo de EF en cualquier problema que requiera soluciones rápidas y/o repetitivas. En este trabajo, se implementan técnicas de MOR para resolver el problema transitorio no lineal acoplado que se está considerando. Los MOR se crean para resolver problemas relacionados con la monitorización de presas de relaves y presas de terraplén. La eficacia y la precisión de estos modelos se demuestran mediante la resolución de problemas inversos para la identificación paramétrica. El MOR resulta ser una herramienta fiable, que acelera significativamente el proceso de identificación inversa y da lugar a soluciones precisas.Postprint (published version

Model order reduction methods for sensor data assimilation to support the monitoring of embankment dams

Tesi en modalitat de cotutela; Universitat Politècnica de Catalunya i Université libre de BruxellesThe latest monitoring and asset management technologies for large infrastructures involve digital representations that integrate information and physical models, exist in parallel to the real-life structures, and are continuously updated based on assimilated sensor data, in order to accurately represent the actual conditions in the structures. This type of technology is often referred to as Digital Twin. The implementation of such cutting-edge technology in monitoring assets like tailings dams, or embankment dams in general, and other large structures, implies the development of highly efficient numerical tools that, combined with sensor data, may support rapid, informed decision making. For the particular case of embankment dams, enabling this type of technology requires an efficient numerical model that describes the coupled hydro-mechanical phenomena, pertinent to a dam functioning and safety. This may for instance be a Finite Elements (FE) model, describing the groundwater flow through unsaturated porous geomaterials. The process of updating and calibrating a model, such as the above mentioned FE model, based on sensor data is typically referred to as data assimilation. Often, this is achieved via an optimization approach, where a specific problem is solved multiple times for various parametric values, in search for the values that best describe the sensor data. The bottleneck in this type of application is typically the cost of multiple evaluations of the model, that may become prohibitive when the underlying FE model is large. In order to enable such applications, the present work proposes Model Order Reduction (MOR) methods tailored to the hydro-mechanical nonlinear problem at hand. MOR aims at the creation of a surrogate model that seeks an approximation of the FE solution in a reduced-order space. This is achieved by applying an offline-online strategy. In the offline stage, the solution manifold of the full-order problem is sampled, in order to identify a low-order affine subspace, where an accurate approximation of the full-order solution can be captured. To tackle the nonlinearities related to partially saturated conditions in the soil, a similar strategy must be employed in order to define reduced-order spaces where an affine system approximation may be recovered. The resulting Reduced Order Model (ROM) may be used as an efficient surrogate to the FE model in any problem that requires fast and/or repetitive solutions. In this work, MOR techniques are implemented to solve the coupled nonlinear transient problem under consideration. ROMs are created to solve problems that pertain to tailings dams and embankment dams monitoring. The efficiency and the accuracy of these models are demonstrated by solving inverse problems for parametric identification. MOR is found to be a reliable tool, significantly accelerating the inverse identification process while resulting to accurate solutions.Las últimas tecnologías de monitorización y gestión de proyectos como grandes infraestructuras implican modelos digitales que integran información y modelos físicos, existen en paralelo a las estructuras reales y se actualizan continuamente en función de datos de sensores asimilados, con el fin de representar con precisión las condiciones reales de las estructuras. Este tipo de tecnología suele denominarse Digital Twin. La aplicación de esta tecnología de vanguardia en la gestión de grandes obras de infraestructura como las presas de residuos mineros, o las presas de tierra o de materiales sueltos en general, y otras estructuras de gran tamaño, implica el desarrollo de herramientas numéricas muy eficientes que, combinadas con los datos de los sensores, permiten una toma de decisiones rápida e informada. Para el caso particular de las presas de terraplén, habilitar este tipo de tecnología requiere un modelo numérico eficiente que describa los fenómenos hidromecánicos acoplados, pertinentes para el funcionamiento y la seguridad de una presa. Puede tratarse, por ejemplo, de un modelo de elementos finitos (EF) que describa el flujo de agua subterránea a través de geomateriales porosos no saturados. El proceso de actualización y calibración de un modelo, como el modelo de elementos finitos mencionado anteriormente, basado en los datos de los sensores se denomina normalmente asimilación de datos. A menudo, esto se consigue mediante un enfoque de optimización, en el que un problema específico se resuelve múltiples veces para varios valores paramétricos, en busca de los valores que mejor describen los datos de los sensores. El obstáculo en este tipo de aplicaciones suele ser el coste de las múltiples evaluaciones del modelo, que puede llegar a ser prohibitivo cuando el modelo de EF es grande. Para permitir este tipo de aplicaciones, el presente trabajo propone métodos de reducción del orden del modelo (MOR) adaptados al problema hidromecánico no lineal en cuestión. MOR tiene como objetivo la creación de un modelo sustituto que busca una aproximación de la solución de EF en un espacio de orden reducido. Esto se consigue aplicando una estrategia offline-online. En la etapa offline, se muestrea el colector de soluciones del problema de orden completo, con el fin de identificar un subespacio afín de orden reducido, en el que se pueda capturar una aproximación precisa de la solución de orden completo. Para abordar las no linealidades relacionadas con las condiciones de saturación parcial del suelo, debe emplearse una estrategia similar para definir espacios de orden reducido en los que pueda recuperarse una aproximación del sistema afín. El Modelo de Orden Reducido (MOR) resultante puede ser utilizado como un sustituto eficiente del modelo de EF en cualquier problema que requiera soluciones rápidas y/o repetitivas. En este trabajo, se implementan técnicas de MOR para resolver el problema transitorio no lineal acoplado que se está considerando. Los MOR se crean para resolver problemas relacionados con la monitorización de presas de relaves y presas de terraplén. La eficacia y la precisión de estos modelos se demuestran mediante la resolución de problemas inversos para la identificación paramétrica. El MOR resulta ser una herramienta fiable, que acelera significativamente el proceso de identificación inversa y da lugar a soluciones precisas.Enginyeria civi

Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people’s safety in many countries; therefore, modeling and forecasting the hydropower dam’s deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian process, M5’ model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam’s deforma tions.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C2

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis