Damage identification in bridges combining deep learning and computational mechanic

xiv, 112 p.[EN]Civil infrastructures, such as bridges, are critical assets for society and the economy. Many of them have already reached their expected life and withstand loadings that exceed the design specifications. Besides, bridges suffer from various degradation mechanisms, including aging, corrosion, earthquakes, and, nowadays, the undeniable effect of climate change. This context has motivated an increasing interest in early detecting damage to prevent costly actions and dangerous failures. Structural Health Monitoring (SHM) consists of implementing effective strategies to continuously assess the health condition of structures using monitoring data collected by sensors. This dissertation focuses on the SHM problem of damage detection and identification. It is an ill-posed inverse problem that aims at inferring the health state of a structure from measurements of its response. The measurements include large amounts of noisy data affected by environmental and operational conditions, acquired with sensors of different nature. Solving such a multidisciplinary problem encompasses the use of applied mathematics, computational mechanics, and data science. In this dissertation, we exploit the potential of Deep Neural Networks in approximating complex inverse problems and employ computational parametrizations and the Finite Element Method to enrich the training phase by including damage scenarios. We explore two different approaches to the problem. In the first approach, we develop an outlier detection strategy to detect departures from the baseline condition. We only employ long-term monitoring data measured at the bridge during normal (healthy) operation. Starting from Principal Component Analysis (PCA) as a statistical data reconstruction technique, we design a specific Deep Autoencoder network that enhances PCA by adding residual connections to include nonlinear transformations. This architecture gains partial explainability by evaluating the contribution of nonlinearties over affine transformations in the reconstruction process. We also investigate the method performance when using local or global variables and evaluate the potential of combining both data sources in the damage detection task. In the second approach, we reach a higher level of damage identification by estimating its severity and location. The goal is to provide a suitable methodology for real full-scale applications that requires reasonable computational resources. We employ a calibrated computational parametrization to solve multiple Finite Element simulations under different damage scenarios. These synthetic scenarios enrich the training dataset of a Deep Neural Network that maps the response of the bridge with its health condition in terms of damage location and severity. Finally, we incorporate the effect of environmental and operational variability in the parametrization by applying a clustering algorithm to find representative samples among the entire dataset. We assume these samples cover most of the variability present in the data and consider them as starting points to generate synthetic training data. We apply the proposed methods to three main case study bridges with available monitoring data: the Beltran bridge in Mexico, and the Infante Dom Henrique bridge in Porto, and the Z24 bridge in Switzerland. Both structures resulted critical to validate and test the ability of the proposed methods and to demonstrate their applicability in the full-scale.[ES]Esta tesis investiga la aplicación de técnicas Deep Learning y Mecánica Computacional en el ámbito de identificación de daños estructurales en puentes. En primer lugar, abordamos técnicas basadas puramente en datos, que emplean únicamente la respuesta estructural adquirida mediante un sistema de instrumentación (sensores). Estas técncias proporcionan un diagnóstico de alerta (daño- no daño). Empleamos un tipo de red neuronal conocido como Autoencoder, al que dotamos de una arquitectura particular que pretende replicar transformaciones afines (como el Análisis de Componenetes Principales) e incorporar transormaciones no lineales de forma interpretable y comprensible. Con el objetivo de alcanzar un nivel más elevado en el diagnóstico, estudiamos una metodología híbrida que incorpora la mecánica computacional como fuente adicional de datos. Mediante el uso de una parametrización de elementos finitos, obtenemos la respuesta estructural sintética ante diferentes escenarios de daño, clasificados por su localización y su grado de severidad. Esta metodología require una calibración previa de la parametrización de acuerdo a un estado de referencia, y los escenarios generados se emplean para entrenar una red neuronal profunda capaz de estimar la localización y severidad de un daño cuando se obtienen nuevas mediciones en el sistema de instrumentación.This disseration has been possible thanks to the support received from: the European Union’s Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project) and the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Constru¸c˜oes - funded by national funds through the FCT/MCTES (PIDDAC); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation with references PID2019-108111RB-I00 (FEDER/AEI) and the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the four Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), MATHEO (KK-2019-00085), and SIGZE (KK-2021/00095); the grant “Artificial Intelligence in BCAM number EXP. 2019/00432”, and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

TRAINING AN AGENT TO MOVE TOWARDS A TARGET INTERACTING WITH A COMPLEX ENVIRONMENT

Στη σημερινή εποχή, το πρόβλημα της αυτόνομης πλοήγησης στα σύγχρονα κινητά ρομπότ αποτελεί σημείο ενδιαφέροντος για την πλειοψηφία της έρευνας που γίνεται γύρω από τη ρομποτική. Αυτό το θέμα γίνεται ακόμα πιο απαιτητικό, καθώς οι απαιτήσεις στα δυναμικά περιβάλλοντα περιλαμβάνουν αυτονομία υψηλού επιπέδου και ευέλικτες δυνατότητες λήψης αποφάσεων για το ρομπότ, ώστε να επιτευχθεί αποφυγής συγκρούσεων. Το Deep learning κατάφερε να λύσει κάποια κοινά ζητήματα στη ρομποτική, όπως η λήψη αποφάσεων, η πλοήγηση και ο έλεγχος, όμως, με εποπτευόμενο τρόπο. Οι Reinforcement learning τεχνολογίες έχουν συνδυαστεί με το Deep learning, με αποτέλεσμα ένα νέο ερευνητικό θέμα γνωστό ως deep reinforcement learning (DRL). Με τη χρήση του DRL, η διαδικασία μπορεί να αυτοματοποιηθεί με τη μετάφραση δεδομένων αισθητήρων πολλών διαστάσεων σε εντολές κίνησης ρομπότ χωρίς τη χρήση κεντρικοποιημένων πληροφοριών, παρέχοντας έναν μη εποπτευόμενο τρόπο. Αυτό που χρειάζεται, για να ενθαρρυνθεί ο agent μάθησης και μέσω διαδικασίας δοκιμής και σφάλματος με το περιβάλλον, να βρει την καλύτερη δράση για κάθε κατάσταση, είναι μία βαθμωτή συνάρτηση ανταμοιβής. Στην εν λόγω διατριβή, δημιουργήθηκε ένα προσομοιωμένο περιβάλλον με ένα κινητό ρομπότ που αλληλεπιδρά με αυτό. Δύο αλγόριθμοι βασισμένοι στο DRL, οι Actor-Critic και PPO, χρησιμοποιήθηκαν για να εκπαιδεύσουν τον παράγοντα να κινείται με ασφάλεια στο περιβάλλον, αποφεύγοντας τα εμπόδια και στοχεύοντας στην επίτευξη ενός καθορισμένου στόχου. Τα αποτελέσματά τους παρουσιάζονται και συγκρίνονται.Nowadays, the problem of autonomous navigation in modern mobile robots is the point of interest for the majority of research in robotics. This topic becomes even more challenging as the requirements in dynamic environments include high-level autonomy and flexible decision-making capabilities for the robot, to achieve collision avoidance. Deep learning has succeeded in solving some common issues in robotics, such as decision making, navigation and control, in a supervised manner though. Reinforcement learning frameworks have been combined with deep learning, resulting in a new research topic known as deep reinforcement learning (DRL). With the use of DRL the procedure can become automated by mapping high-dimensional sensory data to robot motion commands without using ground-truth information, providing an unsupervised manner. It simply takes a scalar reward function to encourage the learning agent through trial-and-error interactions with the environment, with the goal of finding the best action for each state. In the project thesis in question, a simulated environment was created with a mobile robot interacting with it. Two DRL-based algorithms, Actor-Critic and PPO were used to train the agent to move safely in the environment, avoiding the obstacles and aiming to reach a specified goal. Their results are presented and compared

Applications of MEMS Gyroscope for Human Gait Analysis

After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis

Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces

Heterogeneous catalysis is an area of active research, because many industrially relevant reactions involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the field has become more intense due to the development of surface science and simulation techniques that allow for acquiring deeper insight into these catalysts, with the goal of producing more active, cheaper and less toxic catalytic materials. One particularly crucial case study for heterogeneous catalysis is the synthesis of methanol from synthesis gas, composed of H2, CO and CO2. The reaction is catalyzed by a mixture of Cu and ZnO nanoparticles with Al2O3 as a support material. This process is important not only due to methanol’s many uses as a solvent, raw material for organic synthesis, and possible energy and carbon capture material, but also as an example for many other metal/metal oxide catalysts. A plethora of experimental studies are available for this catalyst, as well as for simpler model systems of Cu clusters supported on ZnO surfaces. Unfortunately, there is still a lack of theoretical studies that can support these experi- mental results by providing an atom-by-atom representation of the system. This scarcity of atomic level simulations is due to the absence of fast but ab-initio level accurate potentials that would allow for reaching larger systems and longer simulated time scales. A promising possibility to bridge this gap in potentials is the rise of machine learning potentials, which utilize the tools of machine learning to reproduce the potential energy surface of a system under study, as sampled by an expensive electronic structure reference method of choice. One early and fruitful example of such machine learning force fields are neural network potentials, as initially developed by Behler and Parrinello. In this thesis, a neural network potential of the Behler-Parrinello type has been constructed for ternary Cu/Zn/O systems, focusing on supported Cu clusters on the ZnO(10-10) surface, as a model for the industrial catalyst. This potential was subsequently utilized to perform a number of simulations. Small supported Cu clusters between 4 and 10 atoms were optimized with a genetic algorithm, and a number of structural trends observed. These clusters revealed the first hints of the structure of the Cu/ZnO interface, where Cu prefers to interact with the support through configurations in the continuum between Cu(110) and Cu(111). Simulated annealing runs for Cu clusters between 200 and 500 atoms reinforced this observation, with these larger clusters also adopting this sort of interface with the support. Additionally, in these simulations the effect of strain induced by the support can be observed, with deviations from ideal lattice constants reaching the top of all of the clusters. To further investigate the influence of strain in this system, large coincident surfaces of Cu were deposited on ZnO supports. Due to the lattice mismatch present between the two materials, this requires straining the Cu overlayer. This analysis confirmed once again that Cu(110) and Cu(111) are the most stable surfaces when de- posited on ZnO(10-10). During this thesis a number of new algorithm and programs were developed. Of particular interest is the bin and hash algorithm, which was designed to aid in the construction and curating of reference sets for the neural network potential, and can also be used to evaluate the quality of atomic descriptor sets.2021-10-0

Quantum Biomimetics

136 p.En esta tesis proponemos el concepto de Biomimética Cuántica orientado hacia la reproducción de comportamientos propios de los seres vivos en protocolos de información cuántica. En concreto, las propiedades que aspiramos a imitar emergen como resultado de fenómenos de interacción en diferentes escalas, resultando inaccesibles para un tratamiento matemático acorde al ofrecido por las plataformas de tecnologías cuánticas. Por tanto, el objetivo de la tesis es el de diseñar modelos con cabida para las mencionadas características biológicas pero simplificados de forma que puedan ser adaptados en protocolos experimentales. La tesis se divide en tres partes, una por cada rasgo biológico diferente empleado como inspiración: selección natural, memoria e inteligencia. El estudio presentado en la primera parte culmina con la obtención de un modelo de vida artificial con una identidad exclusivamente cuántica, que no solo permite la escenificación del modelo de selección natural a escala microscópica si no que proporciona un posible marco para la implementación de algoritmos genéticos y problemas de optimización en plataformas cuánticas. En la segunda parte se muestran algoritmos asociados con la simulación de evolución temporal regida por ecuaciones con una dependencia explicita en términos deslocalizados temporalmente. Estos permiten la incorporación de la retroalimentación y posalimentación al conjunto de herramientas en información cuántica. La tercera y última parte versa acerca de la posible simbiosis entre los algoritmos de aprendizaje y los protocolos cuánticos. Mostramos como aplicar técnicas de optimización clásicas para tratar problemas cuánticos así como la codificación y resolución de problemas en dinámicas puramente cuánticas

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area