Étude de la réhabilitation sismique d'un pont avec des isolateurs en caoutchouc à basse température par le biais de surfaces de fragilité

Abstract : In Quebec, Canada, due to aging and deficient seismic detailing, bridges are susceptible to important damage in the occurrence of a strong earthquake. To enhance the seismic performance of the provincial bridge inventory, the replacement of typical bearings with natural rubber isolators has shown to be a potentially efficient retrofitting measure. However, variations in the mechanical properties of the isolators due to environmental conditions can affect the seismic performance. For instance, rubber undergoes substantial stiffening when exposed to low temperatures, as those typically observed during winter in eastern Canada. In bridge-type structures, the thermal stiffening of isolators increases the forces transmitted to the substructure, which in turn becomes more prone to damage. A more detailed consideration of the thermal effects on the seismic performance of typical provincial bridges is thus necessary. In this study, fragility surfaces are used to assess the vulnerability of a typical bridge in Quebec when retrofitted with natural rubber isolators under the concomitant actions of earthquakes and low temperatures. Bridges are composed of several different components with distinguished behaviors and complex interactions under seismic excitation. Owing to the importance of the contribution of different components to the bridge fragility, the first part of this study focuses on the construction of multivariate probabilistic seismic demand models (PSDM). The validity of the commonly adopted assumptions has been criticized and their impact on fragility estimates is not fully understood. A multivariate PSDM approach is thus developed coupling the multiple-stripe analysis and Gaussian mixture models. The novel approach concomitantly captures the complexity of the dynamic response of multicomponent structures and models their uncertainties and correlation. The proposed approach is then used to assess the potential bias introduced by poor modeling on fragility and risk estimates of a real as-built case-study bridge. This PSDM strategy then is adopted to translate the uncertainty and the correlation of the response of the case-study bridge components when retrofitted. Fragility surfaces based on logistic regression depict the effects of thermal stiffening of isolators on the performance of the bridge in both component- and system-level. A beneficial combination is revealed between the decoupling effect provided by isolators and the lateral restraining action of the abutment wing walls depending on the provide clearances. The derivation of fragility surfaces for isolated bridges in cold regions sheds new light on the challenges of retrofitting structures exposed to multiple extreme environments (e.g., seismic and thermal). Overall the presented results can facilitate seismic vulnerability modeling and retrofit assessment of these complex systems and afford valuable practical impacts. The insights and methodological advances can prompt research and applications well beyond the case study structures considered in the thesis, and have broad impacts.Au Québec, Canada, en raison du vieillissement et de détails insuffisants de dimensionnement sismique, les ponts sont susceptibles de subir des dommages importants en cas de fort séisme. Pour améliorer la performance sismique de l'inventaire des ponts de la province, le remplacement des appareils d'appui classiques par des isolateurs en caoutchouc naturel s'est avéré une mesure de réhabilitation potentiellement efficace. Cependant, les variations des propriétés mécaniques des isolateurs dues aux conditions environnementales peuvent affecter la performance sismique. Par exemple, le caoutchouc subit un raidissement important lorsqu'il est exposé aux basses températures, comme celles typiquement observées pendant les hivers dans l'est du Canada. Dans les ponts, le raidissement thermique des isolateurs augmente les forces transmises à la sous-structure, qui devient alors plus susceptible d'être endommagée. Une étude plus détaillée des effets thermiques sur la performance sismique des ponts provinciaux typiques est donc nécessaire. Des surfaces de fragilité sont donc utilisées pour évaluer la vulnérabilité d'un pont typique au Québec réhabilité avec des isolateurs en caoutchouc naturel sous les actions concomitantes des séismes et des basses températures. Les ponts sont composés de plusieurs éléments différents ayant des comportements distincts et des interactions complexes sous une excitation sismique. En raison de l'importance de la contribution de plusieurs composants à la fragilité du pont, la première partie de cette étude se concentre sur la construction de modèles probabilistes multivariés de demande sismique (PSDM). On a critiqué la validité des hypothèses couramment adoptées et leur impact sur les estimations de fragilité n'est pas entièrement compris. Une approche PSDM multivariée est donc développée en couplant l'analyse de bandes multiples et les modèles de mélange gaussien. La nouvelle approche capture de manière concomitante la complexité de la réponse dynamique et modélise les incertitudes et la corrélation. On évalue ensuite le biais potentiel introduit par une mauvaise modélisation sur les estimations de fragilité et de risque d'un pont réel tel que construit. Cette stratégie PSDM est ensuite adoptée pour traduire la réponse des composants du pont de l'étude de cas lorsqu'il est réhabilité. Les surfaces de fragilité basées sur la régression logistique décrivent les effets du raidissement thermique des isolateurs sur les performances du pont, tant au niveau des composants que du système. Une combinaison bénéfique est révélée entre l'effet de découplage des isolateurs et l'action de retenue latérale des murs en fonction des écarts fournis. La dérivation des surfaces de fragilité pour les ponts isolés dans les régions froides jette un nouvel éclairage sur les défis de la réhabilitation des structures exposées à de multiples environnements extrêmes (sismiques et thermiques). Dans l'ensemble, les résultats présentés peuvent faciliter la modélisation de la vulnérabilité sismique et l'évaluation de la réhabilitation de ces systèmes complexes et avoir des répercussions pratiques importantes. Les idées et les avancées méthodologiques peuvent susciter des recherches et des applications bien au-delà des structures étudiées dans la thèse, et en avoir un large impact

A Data-Driven Computing Framework for Structural Seismic Response Prediction

Accurate and rapid seismic response prediction of reinforced concrete (RC) structures in earthquake-prone regions is an important topic in structural and earthquake engineering. However, existing physics-based modeling approaches do not have a good compromise between predictive performance and computational efficiency. High-fidelity models have reasonable predictive performance but are computationally demanding, while more simplified models may be computationally efficient, but do not have as good of performance. The research presented herein aims to address this challenge by developing a novel data-driven computational paradigm via the coupling of machine learning (ML) methods and physics-based models. The ML methods can directly link the experimental data to nonlinear properties of target component, while the physical models meeting universal laws (e.g., Newton’s law of motion) can be used to perform the seismic analysis. Additionally, in real-world scenarios, the dataset is most likely corrupted by outliers, contains missing values, and has sample bias due to the potentially small size. The performance of existing ML methods will be negatively affected by these data-related problems. Thus, novel computational methods to deal with these data-related problems are also developed to make the proposed data-driven framework robust under these circumstances. In sum, the contributions of this dissertation are the following: 1) Two RC column databases, one for rectangular and another for circular columns, were developed. 2) A new ML-based backbone curve model (ML-BCV) was developed by integrating a multi-output least squares support vector machine for regression (MLS-SVMR) with a grid search algorithm for rapid prediction of the bi-linear cyclic backbone curve of RC columns. 3) A novel, locally-weighted ML model (LWLS-SVMR) was developed by combining LS-SVMR and a locally weighted learning algorithm for generalized drift capacity prediction of RC columns. 4) A new, component-level, data-driven framework was developed for generalized, accurate, and efficient seismic response history prediction of structural components subjected to both displacement-controlled cyclic loading and dynamic ground motions. The framework was illustrated for RC columns. 5) The component-level data-driven framework was extended to the system level by coupling it with the simplified, physics-based shear building model. The proposed system-level framework was illustrated for RC frames. 6) A novel, robust, locally-weighted ML model (RLWLS-SVMR) was developed by introducing a weight function into the reformulation of LWLS-SVMR to eliminate the negative effect induced by outliers. 7) A new multiple imputation (MI) method (SRB-PMM) was developed by using sequential regression and predictive mean matching to generate several candidates for imputing (filling in) each missing value while considering the uncertainty associated with the missing data. 8) A novel, regression-based, transfer learning model (DW-SVTR) was developed by coupling two weight functions with LS-SVMR to reduce the negative effect of sample bias due to small datasets

Seismic performance evaluation of reinforced concrete bridge piers considering postearthquake capacity degradation

Bridges play a key role in the transportation sector while serving as lifelines for the economy and safety of communities. The need for resilient bridges is especially important following natural disasters, where they serve as evacuation, aid, and supply routes to an affected area. Much of the earthquake engineering community is interested in improving the resiliency of bridges, and many contributions to the field have been made in the past decades, where a shift towards performancebased design (PBD) practices is underway. While the Canadian Highway Bridge Design Code (CHBDC) has implemented PBD as a requirement for the seismic design of lifeline and major route bridges, the nature of PBD techniques translate to a design process that is not universally compatible for all scenarios and hazards. Therefore, there is great benefit to be realised in the development of PBD guidelines for mainshock-aftershock seismic sequences for scenarios in which the chance to assess and repair a bridge is not possible following a recent mainshock. This research analytically explored a parameterized set of 20 reinforced concrete bridge piers which share several geometrical and material properties with typical bridge bents that support many Canadian bridges. Of those piers, half are designed using current PBD guidelines provided in the 2019 edition of the CHBDC, whereas the remaining half are designed with insufficient transverse reinforcement commonly found in the bridges designed pre-2000. To support this study, a nonlinear fiber-based modelling approach with a proposed material strength degradation scheme is developed using the OpenSEES finite element analysis software. A multiple conditional mean spectra (CMS) approach is used to select a suite of 50 mainshock-aftershock ground motion records for the selected site in Vancouver, British Columbia, which consist of crustal, inslab, and interface earthquakes that commonly occur in areas near the Cascadia Subduction zone. Nonlinear time history analysis is performed for mainshock-only and mainshock-aftershock excitations, and static pushover analysis is also performed in lateral and axial directions for the intact columns, as well as in their respective post-MS and post-AS damaged states. Using the resulting data, a framework for post-earthquake seismic capacity estimation of the bridge piers is developed using machine learning regression methods, where several candidate models are tuned using an exhaustive grid search algorithm approach and k-fold crossvalidation. The tuned models are fitted and evaluated against a test set of data to determine a single best performing model using a multiple scorer performance index as the metric. The resulting performance index suggests that the decision tree model is the most suitable regressor for capacity estimation due to this model exhibiting the highest accuracy as well as lowest residual error. Moreover, this study also assessed the fragility of the bridge piers subjected to mainshock-only and mainshock-aftershock earthquakes. Probabilistic seismic demand models (PSDMs) are derived for the columns designed using current PBD guidelines (PBD-compliant) to evaluate whether the current PBD criteria is sufficient for resisting aftershock effects. Additional PSDMs are generated for the columns with inadequate transverse reinforcement (PBD-deficient) to assess aftershock vulnerability of older bridges. The developed fragility curves indicate an increased fragility of all bridge piers for all damage levels. The findings indicate that adequate aftershock performance is achieved for bridge piers designed to current (2019) CHBDC extensive damage level criteria. Furthermore, it is suggested that minimal damage performance criteria need to be developed for aftershock effects, and the repairable damage level be reintroduced for major route bridges

A computational framework for selecting the optimal combination of seismic retrofit and insurance coverage

Economic earthquake losses can be mitigated through either building retrofit strategies and/or, to some extent, risk-transfer to the (re)insurance market. This paper proposes a computational framework to select the optimal combination of seismic retrofit and insurance policy parameters for buildings. First, a designer selects a suitable retrofit strategy. This is implemented incrementally to define interventions with increasing retrofit performance levels. The cost of each intervention is calculated, along with the cost of property rental while the retrofit is implemented. Alternative insurance options are considered. For each retrofit-insurance combination, the insured and uninsured economic losses within a given time horizon are estimated. The optimal retrofit and insurance combination minimizes the tail value at risk of the life cycle cost. The selected confidence level for this metric depends on the homeowner's risk aversion. The proposed framework is illustrated for a case-study archetype Italian reinforced concrete frame building retrofitted with concrete jacketing, also considering the Italian retrofit tax incentives/rebates called “Sismabonus.

Utilização de Inteligência Artificial para Análise e Dimensionamento de Estruturas em Concreto Armado: uma prospecção tecnológica

Artificial Intelligence (AI) is a technology that makes use of machines that learn from experience and have the ability to perform complex tasks such as the design of reinforced concrete structures. The increase in the complexity of structures used in constructions brings the need for the development and implementation of new technologies in civil construction. Thus, the objective of this work was to carry out a scientific and technological prospection on the use of AI in the analysis and design of reinforced concrete structures. This work presents a qualitative-quantitative approach, of an exploratory nature, through scientific research in the bases of Capes and Web of Science, and patents in the bases of INPI and Orbit. Although the use of ICTs in civil construction is timid, the prospection pointed to a relevant growth in the use of AI in civil construction in the world, however, in Brazil the use of technology is still very incipient.A Inteligência Artificial (IA) é uma tecnologia que faz uso de máquinas que aprendem com a experiência e possuem a capacidade de executar tarefas complexas como o dimensionamento de estruturas de concreto armado. O aumento na complexibilidade das estruturas utilizadas nas construções traz a necessidade de desenvolvimento e de implantação de novas tecnologias na construção civil. Assim, o objetivo deste trabalho é realizar uma prospecção científica e tecnológica sobre a utilização de IA na análise e no dimensionamento de estruturas em concreto armado. Este trabalho apresenta uma abordagem quali-quantitativa, de natureza exploratória, por meio de pesquisas científicas nas bases da Capes e Web of Science, e patentárias nas bases do INPI e do Orbit. Apesar de o uso de TICs na construção civil ser tímido, a prospecção apontou um crescimento relevante da utilização da IA na construção civil no mundo, no entanto, no Brasil, a utilização dessa tecnologia ainda é muito incipiente

Performance Assessment of Masonry School Buildings to Seismic and Flood Hazards Using Bayesian Networks

Performance assessment of schools is an integral part of disaster risk reduction of communities from natural hazards such as earthquakes and floods. In regions of high exposure, these hazards may often act concurrently, whereby yearly flood events weaken masonry school buildings, rendering them more vulnerable to frequent earthquake shaking. This recurring damage, combined with other functional losses, ultimately result in disruption to education delivery, affecting vulnerable schoolchildren. This project examines behaviour of school buildings to seismic and flood loading, and associated disruption to education from a structural and functional perspective. The study is based on a case study of school buildings in Guwahati, India, where the majority of the buildings can be classified into confined masonry (CM) typology. This project presents three stages of analyses to study the performance of these CM school buildings and the system of schools, as summarised in the following. The first stage of the study involves refinement of the World Bank’s Global Library of School Infrastructure taxonomy to widen its scope and to fit the CM school typology. This leads to the identification of index buildings, which are single-story buildings with flexible diaphragms differing mainly in the level of seismic design. In the second stage, a novel numerical modelling platform based on Applied Element Method is used to analyse the index buildings for simplified lateral loads from both the aforementioned hazards. Seismic loading is applied in the form of ground acceleration, while flood loading is applied as hydrostatic pressure. Sequential scenarios are simulated by subjecting the building to varying flood depths followed by lateral ground acceleration, after accounting for the material degradation due to past flooding. Analytical fragility curves are derived for each case of analysis to quantify their physical performance, using a non-linear static procedure (N2 method) and least square error regression. The third stage of the study employs a Bayesian network (BN) based methodology to model the education disruption at the school system level, from exposure of schools to flood and seismic hazards. The methodology integrates the qualitative and quantitative nature of system variables, such as the physical fragility of school buildings (derived in the second stage), accessibility loss, change of use as shelters and socio-economic condition of the users-community. The performance of the education system impacted by the sequential hazards is quantified through the probability of the various states of disruption duration. The BN also explores the effectiveness of non-structural mitigating measures, such as the transfer of students between schools in the system. The framework proves to be a useful tool to assist decision-making, with regard to disaster preparedness and recovery, hence, contributing to the development of resilient education systems

A consensus-based approach for structural resilience to earthquakes using machine learning techniques

Seismic hazards represent a constant threat for both the built environment but mainly for human lives. Past approaches to seismic engineering considered the building deformability as limited to the elastic behaviour. Following to the introduction of performance-based approaches a whole new methodology for seismic design and assessment was proposed, relying on the ability of a building to extend its deformability in the plastic domain. This links to the ability of the building to undergo large deformations but still withstand it and therefore safeguard human lives. This allowed to distinguish between transient and permanent deformations when undergoing dynamic (e.g., seismic) stresses. In parallel, a whole new discipline is flourishing, which sees traditional structural analysis methods coupled to Artificial Intelligence (AI) strategies. In parallel, the emerging discipline of resilience has been widely implemented in the domain of disaster management and also in structural engineering. However, grounding on an extensive literature review, current approaches to disaster management at the building and district level exhibit a significant fragmentation in terms of strategies of objectives, highlighting the urge for a more holistic conceptualization. The proposed methodology therefore aims at addressing both the building and district levels, by the adoption of scale-specific methodologies suitable for the scale of analysis. At the building level, an analytical three-stage methodology is proposed to enhance traditional investigation and structural optimization strategies by the utilization of object-oriented programming, evolutionary computing and deep learning techniques. This is validated throughout the application of the proposed methodology on a real building in Old Beichuan, which underwent seismically-triggered damages as a result of the 2008 Wenchuan Earthquake. At the district scale, a so-called qualitative methodology is proposed to attain a resilience evaluation in face of geo-environmental hazards and specifically targeting the built environment. A Delphi expert consultation is adopted and a framework is presented. To combine the two scales, a high-level strategy is ultimately proposed in order to interlace the building and district-scale simulations to make them organically interlinked. To this respect, a multi-dimensional mapping of the area of Old-Beichuan is presented to aid the identification of some key indicators of the district-level framework. The research has been conducted in the context of the REACH project, `vi investigating the built environment’s resilience in face of seismically-triggered geo-environmental hazards in the context of the 2008 Wenchuan Earthquake in China. Results show that an optimized performance-based approach would significantly enhance traditional analysis and investigation strategies, providing an approximate damage reduction of 25% with a cost increase of 20%. In addition, the utilization of deep learning techniques to replace traditional simulation engine proved to attain a result precision up to 98%, making it reliable to conduct investigation campaign in relation to specific building features when traditional methods fail due to the impossibility of either accessing the building or tracing pertinent documentation. It is therefore demonstrated how sometimes challenging regulatory frameworks is a necessary step to enhance the resilience of buildings in face of seismic hazards