Data-driven analysis of crustal and subduction seismic environments using interpretation of deep learning-based generalized ground motion models

Studies on understanding the regional seismological differences based on the variations in the characteristics of the ground motion waves recorded during seismic events have provided independent insights into the different seismic environments of the world. This study aims to showcase the differences between three of the major seismic environments of the world including Japanese subduction, Chilean subduction, and Californian crustal. The study is based on developing deep learning (DL)-based surrogate generalized ground motion models (GGMMs) and analyzing them to understand the patterns between the earthquake source parameters and the resulting ground motion waveforms’ engineering characteristics. The GGMMs are developed using long short-term memory (LSTM) based recurrent neural networks (RNNs), which are trained using six earthquake source and site parameters as the inputs and a 25 × 1 vector of amplitude-, duration-, and energy-based ground motion intensity measures (IMs). The GGMMs are trained and evaluated using carefully selected large datasets of ground motion records from the Japanese subduction, Chilean subduction, and Californian crustal sources (∼2000 records from each source). The models are developed in two settings: i) three independent GGMMs using the three datasets of each source, ii) one combined GGMM using the combined dataset. While the former provides individual surrogate models of the regional seismic environments and allows relative comparison among the three environments, the latter acts as a global seismic surrogate model and allows comparison in absolute terms. The seismic environments are investigated by analyzing the two types of GGMMs using explainable artificial intelligence (XAI) and game theory based Shapley explanations (SHAP). As the direct physical study of the seismic environments is not generally feasible/practical, the proposed GGMMs surrogating the process becomes a source of knowledge. By interpreting them, inferences about the seismic environments are derived. Results indicate the peculiar nature of the earthquakes arising from the three seismic backgrounds, further emphasizing the importance of conducting independent regional seismic hazard and risk analysis. In particular, the role of magnitude and rupture distance is observed to have a significantly different impact on the different IMs of the three different environments. The study further sets a novel basis to utilize advanced DL and XAI methods in understanding convoluted physics and engineering phenomena.</p

Estudio exploratorio acerca de las creencias del profesorado de ciencias naturales y ciencias sociales sobre la consulta en línea en diferentes dimensiones

Este trabajo se hace parte de una investigación más compleja derivada del Proyecto AKA EDU 03 que involucra a tres universidades finlandesas y dos universidades chilenas. Se trata de un proyecto de investigación ambicioso tanto para aumentar nuestra comprensión de cómo los estudiantes preguntan cuándo consultan en línea en el contexto de la información dominada por la Web y el ambiente de los medios de comunicación, y para desarrollar un nuevo modelo pedagógico de la práctica de consultas en línea en diferentes dominios de las ciencias sociales y naturales. Los desafíos a los que nos enfrentamos como profesores de promover sujetos competentes en ciencias (SCC) requieren un enfoque multidisciplinario que se base en la experiencia académica en lectura y comprensión en línea, educación de maestros y estudios de información. El proyecto conjunto entre socios finlandeses y chilenos ofrece una excelente oportunidad para estudiar este fenómeno y desarrollar modelos pedagógicos para consultas en línea en diferentes contextos y culturas educativas. Aquí compartimos la primera etapa de este proyecto de la contraparte chilena

Vibration-Based Health Monitoring and Mechanics-Based Nonlinear Finite Element Model Updating of Civil Structures

Aging, man-made and natural hazards (e.g., earthquakes and hurricanes) may induce significant damage or even cause the collapse of civil structures. Such damage and failures imply life and economic losses, and function disruption of critical facilities; however, these devastating consequences can be reduced by means of accurate and timely risk mitigation decisions taken before and after the damage-inducing event. Structural health monitoring (SHM) has emerged as an attractive technology for the research and engineering communities to provide tools and protocols aiming to inform and prioritize the decision- making process and, therefore, has been accepted as a critical tool to achieve sustainable and resilient communities. Condition-based inspection and monitoring strategies to assess the residual life, detect any damage and safety threat at the earliest possible stage, and prioritize the repair or replacement of critical infrastructure are crucial preventive and proactive actions that can be facilitated by the use of advanced SHM methodologies. Because of recent advances in computational resources and cost reductions in sensor technologies, nowadays, dense and sophisticated sensor networks have been deployed and are collecting data for different types of civil structures throughout the world. However, current methods and practices in SHM are not achieving the goal of supporting the decision-making process. In this regard, two major hurdles are : (1) there is still a need to validate current state-of-the-art system identification (SID) and damage identification (DID) methods using data recorded from large and complex civil structures subjected to real or realistic damage-inducing events (e.g., man- made or natural hazards such as earthquakes), and (2) there is a disconnect between the advances made in the subfields of SHM and mechanics-based modeling and simulation of structures. This dissertation contributes to overcome these two hurdles by (1) analyzing vibration data recorded from a full-scale five-story reinforced concrete (RC) building fully outfitted with nonstructural components and systems, which was seismically tested and subjected to progressive damage on the NEES@UCSD shake table, and (2) developing and validating a novel and advanced SHM and DID framework that integrates high- fidelity mechanics-based nonlinear finite element (FE) structural modeling and analysis with state-of-the-art Bayesian inference methods. The first part of this dissertation focuses on SID and dynamic characterization of the full-scale five-story RC building specimen. Dynamic data for different sources of excitation, including ambient vibration as well as free and forced vibration tests, are used to investigate the evolution of the modal properties during construction of the building. Variations of the modal properties of the building, under both fixed- base and base-isolated configurations, due to the effects of nonstructural components, seismic-induced damage, and environmental conditions are explored comprehensively. In the second part of the dissertation, a novel framework is developed for system and damage identification of nonlinear structural systems subjected to known or unknown inputs. The proposed framework is validated using homogeneous and heterogeneous sensor data simulated from realistic nonlinear FE models of structures of increasing complexity, including 2D and 3D steel and RC frame structures, subjected to seismic excitation. Stochastic (Bayesian) filtering methods, including the Unscented Kalman filter and the Extended Kalman filter, are used to estimate unknown parameters of the FE model, unknown input base excitations, and their estimation uncertainties using spatially-sparse noisy measurements. By employing the estimated model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess the damage in the structure, and can also be used for damage prognosis purpose

Bayesian Methods for Nonlinear System Identification of Civil Structures

This paper presents a new framework for the identification of mechanics-based nonlinear finite element (FE) models of civil structures using Bayesian methods. In this approach, recursive Bayesian estimation methods are utilized to update an advanced nonlinear FE model of the structure using the input-output dynamic data recorded during an earthquake event. Capable of capturing the complex damage mechanisms and failure modes of the structural system, the updated nonlinear FE model can be used to evaluate the state of health of the structure after a damage-inducing event. To update the unknown time-invariant parameters of the FE model, three alternative stochastic filtering methods are used: the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the iterated extended Kalman filter (IEKF). For those estimation methods that require the computation of structural FE response sensitivities with respect to the unknown modeling parameters (EKF and IEKF), the accurate and computationally efficient direct differentiation method (DDM) is used. A three-dimensional five-story two-by-one bay reinforced concrete (RC) frame is used to illustrate the performance of the framework and compare the performance of the different filters in terms of convergence, accuracy, and robustness. Excellent estimation results are obtained with the UKF, EKF, and IEKF. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the modeling parameters are observed when using these filters. The UKF slightly outperforms the EKF and IEKF

Material parameter estimation in distributed plasticity FE models using the unscented Kalman filter

This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and a nonlinear stochastic filtering technique, the unscented Kalman filter (UKF), to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. The proposed framework updates the nonlinear FE model of the structure using input-output data recorded during earthquake events. The updated model can be directly used for damage identification. A two-dimensional 3-bay 3-story steel moment-resisting frame is used to verify the convergence, robustness, and accuracy of the proposed methodology. The steel frame is modeled using fiber-section beam-column elements with distributed plasticity and is subjected to a ground motion recorded during the 1989 Loma Prieta earthquake. The results indicate that the proposed framework provides accurate estimation of the unknown material parameters of the nonlinear FE model.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyGraduat

Batch and Recursive Bayesian Estimation Methods for Nonlinear Structural System Identification

This chapter presents a framework for the identification of nonlinear finite element (FE) structural models using Bayesian inference methods. Using the input-output dynamic data recorded during an earthquake event, batch and recursive Bayesian estimation methods are employed to update a mechanics-based nonlinear FE model of the structure of interest (building, bridge, dam, etc.). Unknown parameters of the nonlinear FE model characterizing material constitutive models, inertia, geometric, and/or constraint properties of the structure can be estimated using limited response data recorded through accelerometers or heterogeneous sensor arrays. The updated nonlinear FE model can be used to identify the damage in the structure following a damage-inducing event. This framework, therefore, can provide an advanced tool for post-disaster damage identification and structural health monitoring. The batch estimation method is based on a maximum a posteriori estimation (MAP) approach, where the time history of the input and output measurements are used as a single batch of data for estimating the FE model parameters. This method results in a nonlinear optimization problem that can be solved using gradient-based and non-gradient-based optimization algorithms. In contrast, the recursive Bayesian estimation method processes the information from the measured data recursively, and updates the estimation of the FE model parameters progressively over the time history of the event. The recursive Bayesian estimation method results in a nonlinear Kalman filtering approach. The Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) are employed as recursive Bayesian estimation methods herein. For those estimation methods that require the computation of structural FE response sensitivities (total partial derivatives) with respect to the unknown FE model parameters, the direct differentiation method (DDM) is used. Response data numerically simulated from a nonlinear FE model (with unknown material model parameters) of a five-story two-by-one bay reinforced concrete frame building subjected to bi-directional horizontal seismic excitation are used to illustrate the performance of the proposed framework

Bayesian Methods for Nonlinear System Identification of Civil Structures

This paper presents a new framework for the identification of mechanics-based nonlinear finite element (FE) models of civil structures using Bayesian methods. In this approach, recursive Bayesian estimation methods are utilized to update an advanced nonlinear FE model of the structure using the input-output dynamic data recorded during an earthquake event. Capable of capturing the complex damage mechanisms and failure modes of the structural system, the updated nonlinear FE model can be used to evaluate the state of health of the structure after a damage-inducing event. To update the unknown time-invariant parameters of the FE model, three alternative stochastic filtering methods are used: the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the iterated extended Kalman filter (IEKF). For those estimation methods that require the computation of structural FE response sensitivities with respect to the unknown modeling parameters (EKF and IEKF), the accurate and computationally efficient direct differentiation method (DDM) is used. A three-dimensional five-story two-by-one bay reinforced concrete (RC) frame is used to illustrate the performance of the framework and compare the performance of the different filters in terms of convergence, accuracy, and robustness. Excellent estimation results are obtained with the UKF, EKF, and IEKF. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the modeling parameters are observed when using these filters. The UKF slightly outperforms the EKF and IEKF

Time-variant modal parameters and response behavior of a base-isolated building tested on a shake table

This paper presents the identification of the instantaneous modal properties and the experimental response of a full-scale, five-story base-isolated RC building tested on a shake table. A suite of earthquake motions of various intensities was applied to the building to progressively increase the seismic demand. The deterministic-stochastic subspace identification method is employed to estimate the variations of the modal properties of the building by employing a short-time windowing approach. The changes of the modal parameters during the seismic motions are tracked and analyzed. Observed and measured responses of the structure are analyzed and correlated with the variation of the identified modal parameters. The nonlinear behavior of the isolators generates the variation of the identified natural frequencies and equivalent damping ratios of the building, which change in agreement with the input motion intensity. A high correlation between the effective stiffness of the isolators and the instantaneous frequency of the first mode is found. The effective damping ratio of the isolation system and the instantaneous damping ratio of the fundamental mode of the building are highly correlated