DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions

Traditional ground-penetrating radar (GPR) data inversion leverages iterative algorithms which suffer from high computation costs and low accuracy when applied to complex subsurface scenarios. Existing deep learning-based methods focus on the ideal homogeneous subsurface environments and ignore the interference due to clutters and noise in real-world heterogeneous environments. To address these issues, a two-stage deep neural network (DNN), called DMRF-UNet, is proposed to reconstruct the permittivity distributions of subsurface objects from GPR B-scans under heterogeneous soil conditions. In the first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1) is built to remove the clutters due to inhomogeneity of the heterogeneous soil. Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns the inverse mapping relationship and reconstructs the permittivity distribution of subsurface objects. To avoid information loss, an end-to-end training method combining the loss functions of two stages is introduced. A wide range of subsurface heterogeneous scenarios and B-scans are generated to evaluate the inversion performance. The test results in the numerical experiment and the real measurement show that the proposed network reconstructs the permittivities, shapes, sizes, and locations of subsurface objects with high accuracy. The comparison with existing methods demonstrates the superiority of the proposed methodology for the inversion under heterogeneous soil conditions

Synthetic associative learning in engineered multicellular consortia

Associative learning is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was early recognized to be a general trait of complex multicellular organisms but also found in "simpler" ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display associative learning responses to two classes of stimuli and displaying long and short-term memory (i. e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.Comment: 5 figure

Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We employ a dimensionless form of the THM governing equations that is particularly well suited for the inverse problem, and we leverage the sequential multiphysics PINN solver we developed in previous work. We validate the proposed inverse-modeling approach on multiple benchmark problems, including Terzaghi's isothermal consolidation problem, Barry-Mercer's isothermal injection-production problem, and nonisothermal consolidation of an unsaturated soil layer. We report the excellent performance of the proposed sequential PINN-THM inverse solver, thus paving the way for the application of PINNs to inverse modeling of complex nonlinear multiphysics problems

Estimation of Unsaturated Flow Parameters by Inverse Modeling and GPR Tomography

The main goal of this work was to evaluate the possibility of estimating the flow parameters and geological structure of the unsaturated zone, also called vadose zone, using both geophysical and hydrological data and methods. The vadose zone at Moreppen field site located near Oslo’s Gardermoen airport was used as the case study. Moreppen field site has been the subject of numerous studies related to sedimentological, hydrological, geophysical and geochemical processes in the saturated and vadose zone. However, in the field of hydrology none of the previous studies at Moreppen used spatially continuous geophysical data to estimate the flow parameters at the field site. In this study, cross well GPR travel time tomography for the first time was used at Moreppen to map the spatial and temporal distribution of the electromagnetic (EM) wave velocity at the field site. The EM wave velocities were converted to the soil water content using a petrophysical relationship. Then using an inverse flow modeling conditioned on volumetric soil water content, we estimated hydrological parameters in the field site. Since snowmelt is the main groundwater recharge at Gardermoen, we focused our study to the water flow through the vadose zone during the snowmelt

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3 and reports on five research projects.U.S. Department of Transportation Contract DTRS-57-88-C-00078TTD13U.S. Department of Transportation Contract DTRS-57-88-C-00078TTD30Defense Advanced Research Projects Agency Contract MDA972-90-C-0021Digital Equipment CorporationIBM CorporationJoint Services Electronics Program Contract DAAL03-89-C-0001Joint Services Electronics Program Contract DAAL03-92-C-0001Schlumberger-Doll ResearchU.S. Navy - Office of Naval Research Grant N00014-90-J-1002U.S. Navy - Office of Naval Research Grant N00014-89-J-1019National Aeronautics and Space Administration Grant NAGW-1617National Aeronautics and Space Administration Grant 958461National Aeronautics and Space Administration Grant NAGW-1272U.S. Army Corp of Engineers Contract DACA39-87-K-0022U.S. Navy - Office of Naval Research Grant N00014-89-J-110

Implementation of a simulation inversion method into estimating the damping coefficient in blasting

Damping is a mechanism of energy dissipation in shock and vibration. It is difficult to obtain the damping coefficient by theoretical method accurately because of varying material properties, vibration velocity and frequency, especially for the millisecond delay blasting in tunnel excavation. Therefore, the most effective method is simulation inversion by employing large-scale monitoring data, accurate blast loading model and detailed mechanical parameters. In this paper, in-situ monitoring data was acquired by Blasting Vibration Recorder. The accurate blast loading was calculated on the basis of neural network method, so the contribution rate coefficient of every sequence blasting in total millisecond delay blasting could be confirmed. Mechanical parameter of the host rock was acquired by Split Hopkinson Pressure Bar (SHPB) test. In order to predict the simulated velocity, the numerical model in physical dimensions was built by FLAC3D, alongside the constitutive parameters from laboratory tests and different damping coefficients. Compared with the monitoring attenuation law, the damping coefficient of host rock could be finally confirmed

Artificial Neural Network and Finite Element Modeling of Nanoindentation Tests on Silica

Two major forms of Silica include the crystalline form named Quartz which consist of the sand grains in nature, and amorphous form named Silica Glass or Fused Silica which is commonly known as glass. Fused Silica is an amorphous crystal that can show plastic behavior at micro-scale despite its brittle behavior in large scales. Due to the amorphous and ductile nature of Fused Silica, this behavior may not be explained well using the traditional dislocation-based mechanism of plasticity for crystalline solids. The crystal plasticity happens due to shear stress and stored energy in the material as dislocations which does not change the volume. In amorphous Fused Silica however, the permanent deformation is mainly caused by densification of the material under localized loading in addition to plastic flow caused by shear stress. This behavior is particularly true in the case of nanoindentation testing. Due to this densifying behavior, modeling the material using constitutive models such as Drucker-Prager/Cap can be quite helpful to further expand the model parameters to be used for geomaterials. Nanoindentation tests were performed on Fused Silica and Quartz samples and Finite Element Method (FEM) was used to further investigate the effect of different constitutive model parameters on material behavior. It was observed that, by implementing volumetric hardening in constitutive models, the FEM results were in better agreement with experimental results in case of both Fused Silica and sand grains. In the second part of the study Artificial Neural Network (ANN) models were used to predict nanoindentation test results for different material parameters as well as indenter shape and geometry. ANN models were trained using FEM results and experimental test results and verified using the reminder of the data. Trained models were then used to study of different scenarios that were not analyzed using FEM or experiments. Advisor: Chung R. Son

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

Investigation Progresses and Applications of Fractional Derivative Model in Geotechnical Engineering

Over the past couple of decades, as a new mathematical tool for addressing a number of tough problems, fractional calculus has been gaining a continually increasing interest in diverse scientific fields, including geotechnical engineering due primarily to geotechnical rheology phenomenon. Unlike the classical constitutive models in which simulation analysis gradually fails to meet the reasonable accuracy of requirement, the fractional derivative models have shown the merits of hereditary phenomena with long memory. Additionally, it is traced that the fractional derivative model is one of the most effective and accurate approaches to describe the rheology phenomenon. In relation to this, an overview aimed first at model structure and parameter determination in combination with application cases based on fractional calculus was provided. Furthermore, this review paper shed light on the practical application aspects of deformation analysis of circular tunnel, rheological settlement of subgrade, and relevant loess researches subjected to the achievements acquired in geotechnical engineering. Finally, concluding remarks and important future investigation directions were pointed out