An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

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

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

Hydrologic prediction using pattern recognition and soft-computing techniques

Several studies indicate that the data-driven models have proven to be potentially useful tools in hydrological modeling. Nevertheless, it is a common perception among researchers and practitioners that the usefulness of the system theoretic models is limited to forecast applications, and they cannot be used as a tool for scientific investigations. Also, the system-theoretic models are believed to be less reliable as they characterize the hydrological processes by learning the input-output patterns embedded in the dataset and not based on strong physical understanding of the system. It is imperative that the above concerns needs to be addressed before the data-driven models can gain wider acceptability by researchers and practitioners.In this research different methods and tools that can be adopted to promote transparency in the data-driven models are probed with the objective of extending the usefulness of data-driven models beyond forecast applications as a tools for scientific investigations, by providing additional insights into the underlying input-output patterns based on which the data-driven models arrive at a decision. In this regard, the utility of self-organizing networks (competitive learning and self-organizing maps) in learning the patterns in the input space is evaluated by developing a novel neural network model called the spiking modular neural networks (SMNNs). The performance of the SMNNs is evaluated based on its ability to characterize streamflows and actual evapotranspiration process. Also the utility of self-organizing algorithms, namely genetic programming (GP), is evaluated with regards to its ability to promote transparency in data-driven models. The robustness of the GP to evolve its own model structure with relevant parameters is illustrated by applying GP to characterize the actual-evapotranspiration process. The results from this research indicate that self-organization in learning, both in terms of self-organizing networks and self-organizing algorithms, could be adopted to promote transparency in data-driven models.In pursuit of improving the reliability of the data-driven models, different methods for incorporating uncertainty estimates as part of the data-driven model building exercise is evaluated in this research. The local-scale models are shown to be more reliable than the global-scale models in characterizing the saturated hydraulic conductivity of soils. In addition, in this research, the importance of model structure uncertainty in geophysical modeling is emphasized by developing a framework to account for the model structure uncertainty in geophysical modeling. The contribution of the model structure uncertainty to the predictive uncertainty of the model is shown to be larger than the uncertainty associated with the model parameters. Also it has been demonstrated that increasing the model complexity may lead to a better fit of the function, but at the cost of an increasing level of uncertainty. It is recommended that the effect of model structure uncertainty should be considered for developing reliable hydrological models

Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better

Cone Penetration Testing 2022

This volume contains the proceedings of the 5th International Symposium on Cone Penetration Testing (CPT’22), held in Bologna, Italy, 8-10 June 2022. More than 500 authors - academics, researchers, practitioners and manufacturers – contributed to the peer-reviewed papers included in this book, which includes three keynote lectures, four invited lectures and 169 technical papers. The contributions provide a full picture of the current knowledge and major trends in CPT research and development, with respect to innovations in instrumentation, latest advances in data interpretation, and emerging fields of CPT application. The paper topics encompass three well-established topic categories typically addressed in CPT events: - Equipment and Procedures - Data Interpretation - Applications. Emphasis is placed on the use of statistical approaches and innovative numerical strategies for CPT data interpretation, liquefaction studies, application of CPT to offshore engineering, comparative studies between CPT and other in-situ tests. Cone Penetration Testing 2022 contains a wealth of information that could be useful for researchers, practitioners and all those working in the broad and dynamic field of cone penetration testing

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors