Application of inverse analysis to geotechnical problems, from soil behaviour to large deformation modelling

2017 - 2018Large deformation analysis has become recently centre of attraction in geotechnical design. It is used to predict geotechnical boundary value problems such as, excessive movement of soil masses like landslides or soil-structure interaction like pile installations. Wrong understanding and simulation of each mentioned problem could lead to significant costs and damages, therefore, robust approaches of modelling are needed. Throughout the past decades many numerical methods aiming to simulate large deformations have been introduced as for example, Discrete Element Method (DEM), Smooth Particle Hydrodynamics (SPH), Updated Lagrangian Finite Element Method (UL-FEM) and Material Point Method (MPM). They are varying in basic theories, capabilities and accuracy. But, the complexity is the feature which is quite common in all them and it is attributed to the unclear response of soil body under excessive deformations. As a result these methods are involving many uncertainties in input parameters. Determination of these parameters is always difficult, because reproducing larg deformations in the laboratory is difficult and needs advanced and expensive facilities. As a result the introduction of a methodology for estimation of the model parameters adopted for large deformation analysis is extremely needed. Inverse analysis approaches have proved to be able to overcome complex engineering problem in different fields. In geotechnical engineering, inverse analysis is typically employed to back-calculate the input parameter set of a model to best reproduce monitored observations. Accordingly, its application attempts to clarify the effective soil conditions and allows for an update of the design based on the insitu measurements. Numerous researches have been fulfilled to evaluate the performance of this approach in geotechnical problem, however, rarely the application of this methodology to the problems involving large deformations have been addressed. This thesis is addressing these issues by combining inverse analysis methods with advanced numerical methods and soil constitutive models. The proposed methodology is applied to two popular large deformation engineering problem i.e. landslides and soil-structure interaction, particularly cone penetration tests modelling. Different case studies are addressed; two methods of Smoothed Particle Hydrodynamic and Material Point Method are adopted as numerical models, depending on the case study. Similarly, various constitutive models ranging from the simple Mohr-Coulomb to the advanced ones such as Hardening soil and Hypoplastic model are employed. The employed inverse analysis algorithm also varies by the type of the numerical models and required computation time of the forward model. Particularly, two algorithm are selected, a gradient-based method (modified Gauss-Newton method) and an evaluation based one (Species- based Quantum Particle Swarm Optimization). In each case the strength and shortcoming of the adopted methods as well as the role played by the adopted benchmarks and the type of observation in model calibration is assessed. A concept of in-situ recalibration of the model is defined and its importance is highlighted. This method is used to determine advanced constitutive model parameters using in-situ tests and geometrical observations. As a conclusion, the research shows how using an inverse analysis algorithm may improve the modelling of geotechnical problems involving large deformations and, particularly, facilitate model calibration and discovering the shortcoming and strength of the numerical models. [edited by Author]XXXI cicl

Study of hybrid strategies for multi-objective optimization using gradient based methods and evolutionary algorithms

Most of the optimization problems encountered in engineering have conflicting objectives. In order to solve these problems, genetic algorithms (GAs) and gradient-based methods are widely used. GAs are relatively easy to implement, because these algorithms only require first-order information of the objectives and constraints. On the other hand, GAs do not have a standard termination condition and therefore they may not converge to the exact solutions. Gradient-based methods, on the other hand, are based on first- and higher-order information of the objectives and constraints. These algorithms converge faster to the exact solutions in solving single-objective optimization problems, but are inefficient for multi-objective optimization problems (MOOPs) and unable to solve those with non-convex objective spaces. The work in this dissertation focuses on developing a hybrid strategy for solving MOOPs based on feasible sequential quadratic programming (FSQP) and nondominated sorting genetic algorithm II (NSGA-II). The hybrid algorithms developed in this dissertation are tested using benchmark problems and evaluated based on solution distribution, solution accuracy, and execution time. Based on these performance factors, the best hybrid strategy is determined and found to be generally efficient with good solution distributions in most of the cases studied. The best hybrid algorithm is applied to the design of a crushing tube and is shown to have relatively well-distributed solutions and good efficiency compared to solutions obtained by NSGA-II and FSQP alone

Machine learning in concrete technology: A review of current researches, trends, and applications

Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy

ROBUST MODEL DEVELOPMENT FOR EVALUATION OF EXISTING STRUCTURES

In the context of scientific computing, validation aims to determine the worthiness of a model in supporting critical decision making. This determination must occur given the imperfections in the mathematical representation resulting from the unavoidable idealizations of physics phenomena. Uncertainty in parameter values furthers the validation problems due to the inevitable lack of information about material properties, boundary conditions, loads, etc. which must be taken into account in making predictions about structural response. The determination of worthiness then becomes assessing whether an unavoidably imperfect mathematical model, subjected to poorly known input parameters, can predict sufficiently well in its intended purpose. The maximum degree of uncertainty in the model\u27s input parameters which the model can tolerate and still produce predictions within a predefined error tolerance is termed as robustness of the model. A trade-off exists between a model’s robustness to unavoidable uncertainty and its agreement with experiments, i.e. fidelity. This dissertation introduces the concept of satisfying boundary to evaluate such a trade-off. This boundary encompasses the model predictions that meet prescribed error tolerances. Decisions regarding allocation of resources for additional experiments to reduce uncertainty, relaxation of error tolerances, or the required confidence in the model predictions can be arrived at with the knowledge of this trade-off. This new approach for quantifying robustness based on satisfying boundaries is demonstrated on an application to a nonlinear finite element model of a historic masonry monument Fort Sumter

Seismic Inversion and Uncertainty Analysis using Transdimensional Markov Chain Monte Carlo Method

We use a transdimensional inversion algorithm, reversible jump MCMC (rjMCMC), in the seismic waveform inversion of post-stack and prestack data to characterize reservoir properties such as seismic wave velocity, density as well as impedance and then estimate uncertainty. Each seismic trace is inverted independently based on a layered earth model. The model dimensionality is defined as the number of the layers multiplied with the number of model parameters per layer. The rjMCMC is able to infer the number of model parameters from data itself by allowing it to vary in the iterative inversion process, converge to proper parameterization and prevent underparameterization and overparameterization. We also use rjMCMC to enhance uncertainty estimation since it can transdimensionally sample different model spaces of different dimensionalities and can prevent a biased sampling in only one space which may have a different dimensionality than that of the true model space. An ensemble of solutions from difference spaces can statistically reduce the bias for parameter estimation and uncertainty quantification. Inversion uncertainty is comprised of property uncertainty and location uncertainty. Our study revealed that the inversion uncertainty is correlated with the discontinuity of property in such a way that 1) a smaller discontinuity will induce a lower uncertainty in property at the discontinuity but also a higher uncertainty of the location of that discontinuity and 2) a larger discontinuity will induce a higher uncertainty in property at the discontinuity but also a higher ``certainty'' of the location of that discontinuity. Therefore, there is a trade-off between the property uncertainty and the location uncertainty. To our surprise, there is a lot of hidden information in the uncertainty result that we can actually take advantage of due to this trade-off effect. On the basis of our study using rjMCMC, we propose to use the inversion uncertainty as a novel attribute in an optimistic way to characterize the magnitude and the location of subsurface discontinuities and reflectors

Landslide susceptibility mapping using remote sensing data and geographic information system-based algorithms

Whether they occur due to natural triggers or human activities, landslides lead to loss of life and damages to properties which impact infrastructures, road networks and buildings. Landslide Susceptibility Map (LSM) provides the policy and decision makers with some valuable information. This study aims to detect landslide locations by using Sentinel-1 data, the only freely available online Radar imagery, and to map areas prone to landslide using a novel algorithm of AB-ADTree in Cameron Highlands, Pahang, Malaysia. A total of 152 landslide locations were detected by using integration of Interferometry Synthetic Aperture RADAR (InSAR) technique, Google Earth (GE) images and extensive field survey. However, 80% of the data were employed for training the machine learning algorithms and the remaining 20% for validation purposes. Seventeen triggering and conditioning factors, namely slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, Normalized Difference Vegetation Index (NDVI), rainfall, land cover, lithology, soil types, curvature, profile curvature, Stream Power Index (SPI) and Topographic Wetness Index (TWI), were extracted from satellite imageries, digital elevation model (DEM), geological and soil maps. These factors were utilized to generate landslide susceptibility maps using Logistic Regression (LR) model, Logistic Model Tree (LMT), Random Forest (RF), Alternating Decision Tree (ADTree), Adaptive Boosting (AdaBoost) and a novel hybrid model from ADTree and AdaBoost models, namely AB-ADTree model. The validation was based on area under the ROC curve (AUC) and statistical measurements of Positive Predictive Value (PPV), Negative Predictive Value (NPV), sensitivity, specificity, accuracy and Root Mean Square Error (RMSE). The results showed that AUC was 90%, 92%, 88%, 59%, 96% and 94% for LR, LMT, RF, ADTree, AdaBoost and AB-ADTree algorithms, respectively. Non-parametric evaluations of the Friedman and Wilcoxon were also applied to assess the models’ performance: the findings revealed that ADTree is inferior to the other models used in this study. Using a handheld Global Positioning System (GPS), field study and validation were performed for almost 20% (30 locations) of the detected landslide locations and the results revealed that the landslide locations were correctly detected. In conclusion, this study can be applicable for hazard mitigation purposes and regional planning

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Fuzzy Sets Applications in Civil Engineering Basic Areas

Civil engineering is a professional engineering discipline that deals with the design, construction, and maintenance of the physical and naturally built environment, including works like roads, bridges, canals, dams, and buildings. This paper presents some Fuzzy Logic (FL) applications in civil engeering discipline and shows the potential of facilities of FL in this area. The potential role of fuzzy sets in analysing system and human uncertainty is investigated in the paper. The main finding of this inquiry is FL applications used in different areas of civil engeering discipline with success. Once developed, the fuzzy logic models can be used for further monitoring activities, as a management tool

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