Browsing by Author "Padgett, Jamie"
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Item Computational framework for the analysis of hybrid masonry systems using an improved non-local technique(2014-12-05) Gao, Zhenjia; Stanciulescu, Ilinca; Padgett, Jamie; Lou, Jun; Willam, KasparHybrid masonry structures combine the ductility of steel components with the shear strength of reinforced masonry panels. The goal of this research is to provide a sound basis for the design of an optimal type of hybrid structure that can be implemented as a new lateral-force-resisting system in high seismic regions. The most challenging part in the hybrid structure simulation is to capture the behaviour of concrete under different loading scenarios. This thesis sets up a computational framework for the analysis of hybrid masonry systems using an improved non-local technique, including the contributions such as: adopting the consistent linearisation technique to improve the computational efficiency of the non-local one-scalar damage model; presenting a new way to calibrate parameters in the tension damage law in the two-scalar damage model by correlating them to the ones in the one-scalar damage model; designing a data structure to save the domain information for each material point in order to apply the non-local technique; proposing an automatic parameter calibration procedure based on the Nelder-Mead simplex method for the two-scalar damage model utilizing the global system testing data; proposing and identifying the internal variable to be non-localized to enhance a new damage model to obtain the mesh regularization solution. Finally, this thesis performs a system-level numerical study of the energy dissipation mechanisms of hybrid masonry structures under cyclic loading. The numerical studies extrapolate test data to a wider range of structural configurations in terms of various connector strengths and different masonry panels to maximize seismic energy dissipation. This work also investigates the influence of the load transfer mechanism on the lateral strength, stiffness, energy dissipation capacity and deformation pattern of the hybrid system. Findings from the numerical studies performed in this work confirm the feasibility of using hybrid structures in high seismic areas.Item Disaster Risk Management Through the DesignSafe Cyberinfrastructure(Springer Nature, 2020) Pinelli, Jean-Paul; Esteva, Maria; Rathje, Ellen M.; Roueche, David; Brandenberg, Scott J.; Mosqueda, Gilberto; Padgett, Jamie; Haan, FrederickDesignSafe addresses the challenges of supporting integrative data-driven research in natural hazards engineering. It is an end-to-end data management, communications, and analysis platform where users collect, generate, analyze, curate, and publish large data sets from a variety of sources, including experiments, simulations, field research, and post-disaster reconnaissance. DesignSafe achieves key objectives through: (1) integration with high performance and cloud-computing resources to support the computational needs of the regional risk assessment community; (2) the possibility to curate and publish diverse data structures emphasizing relationships and understandability; and (3) facilitation of real time communications during natural hazards events and disasters for data and information sharing. The resultant services and tools shorten data cycles for resiliency evaluation, risk modeling validation, and forensic studies. This article illustrates salient features of the cyberinfrastructure. It summarizes its design principles, architecture, and functionalities. The focus is on case studies to show the impact of DesignSafe on the disaster risk community. The Next Generation Liquefaction project collects and standardizes case histories of earthquake-induced soil liquefaction into a relational database—DesignSafe—to permit users to interact with the data. Researchers can correlate in DesignSafe building dynamic characteristics based on data from building sensors, with observed damage based on ground motion measurements. Reconnaissance groups upload, curate, and publish wind, seismic, and coastal damage data they gather during field reconnaissance missions, so these datasets are available shortly after a disaster. As a part of the education and community outreach efforts of DesignSafe, training materials and collaboration space are also offered to the disaster risk management community.Item Expected seismic performance of gravity dams using machine learning techniques(New Zealand Society for Earthquake Engineering, 2021) Segura, Rocio; Padgett, Jamie; Paultre, PatrickMethods for the seismic analysis of dams have improved extensively in the last several decades. Advanced numerical models have become more feasible and constitute the basis of improved procedures for design and assessment. A probabilistic framework is required to manage the various sources of uncertainty that may impact system performance and fragility analysis is a promising approach for depicting conditional probabilities of limit state exceedance under such uncertainties. However, the effect of model parameter variation on the seismic fragility analysis of structures with complex numerical models, such as dams, is frequently overlooked due to the costly and time-consuming revaluation of the numerical model. To improve the seismic assessment of such structures by jointly reducing the computational burden, this study proposes the implementation of a polynomial response surface metamodel to emulate the response of the system. The latter will be computationally and visually validated and used to predict the continuous relative maximum base sliding of the dam in order to build fragility functions and show the effect of modelling parameter variation. The resulting fragility functions are used to assess the seismic performance of the dam and formulate recommendations with respect to the model parameters. To establish admissible ranges of the model parameters in line with the current guidelines for seismic safety, load cases corresponding to return periods for the dam classification are used to attain target performance limit states.Item Fragility and Risk Assessment of Aboveground Storage Tanks during Storm Events(2019-06-10) Bernier, Carl; Padgett, JamieAboveground storage tanks (ASTs) have suffered severe damage during past storm events, resulting in the release of hazardous chemicals in the environment. For instance, more than 30 million liters of oil products were spilled during Hurricane Katrina and Rita in 2005. Despite the evident vulnerability of ASTs, the literature is currently lacking comprehensive studies evaluating the performance of ASTs during multi-hazard storm events. To address this gap, this thesis offers new methods, tools, models, and frameworks to assess the structural behavior and structural vulnerability of ASTs subjected to multi-hazard storm conditions, as well as to support risk assessment and mitigation of ASTs located in coastal regions. First, a series of finite element models are developed to estimate storm loads on ASTs and investigate the structural behavior of ASTs under storm surge, wave, wind, debris impacts, and rainfall loads. Opportunities to reduce the computational complexity and cost of the derived numerical models are also explored using surrogate modeling techniques and assessing the validity of static analyses for dynamic phenomena. A general methodology is then posed to perform fragility assessments of ASTs under concurrent or multi-hazard storm loads using the derived numerical models, a statistical sampling method, and logistic regression. With this methodology, the first comprehensive fragility assessments are performed for (i) ASTs subjected to concurrent surge, wave, and wind loads; (ii) ASTs subjected to waterborne debris impacts; and (iii) floating roof ASTs subjected to rainwater accumulations. This study also proposes frameworks to perform risk assessments of ASTs located in coastal regions. A first framework is developed for large-scale regional assessments of ASTs subjected to surge, wave, and wind loads. As a proof of concept, a scenario-based assessment and, for the first time, a probabilistic risk assessment are performed for a case study region, the Houston Ship Channel. Useful metrics, such as expected spill volumes and annual probabilities of failure, are obtained from the risk assessments. A second risk assessment framework is also developed to estimate the likelihood of debris impacts and damage due to such impacts for small-scale AST terminals located near known debris sources; this framework is illustrated again for a case study terminal along the Houston Ship Channel. Moving from a purely engineering perspective, the results of the risk assessments are also coupled with social vulnerability modeling to explore community impacts. Building from the risk assessment frameworks, an integrated model of built-human-natural systems is also posed to perform a comprehensive assessment of procedural, structural, and protective mitigation strategies. Since no single mitigation strategy appears optimal, a tool is also developed to optimally select and combine mitigation strategies to achieve a given performance target and propose cost-effective solutions, while considering social impacts. Finally, forensic investigations of AST failures during Hurricane Harvey are performed to highlight the viability of the derived fragility models to understand the causes and mechanisms behind AST failures and further evaluate the effectiveness of mitigation strategies. Results obtained throughout this thesis demonstrate that the derived fragility models are efficient tools to perform rapid screening of vulnerable ASTs in industrial regions, and evaluate the viability of mitigation strategies to reduce this vulnerability. Insights obtained from the fragility and risk assessments reveal that neglecting the multi-hazard nature of storms, as existing studies have done, can lead to a significant underestimation of vulnerability and risks. Results of the assessments also indicate that small size ASTs are generally more vulnerable to loads such as wave, wind, debris impacts, and rainfall, and that floating roof ASTs do not appear to be vulnerable during rainfall events unless they are already damaged or their drain system is inefficient. Moreover, results show that simple mitigation strategies such as anchoring ASTs to the ground or filling them with liquid could greatly reduce the likelihood of AST failures and spills during storms. Lastly, this thesis illustrates how using surrogate model and statistical learning techniques can facilitate and reduce the computational complexity of fragility and risk assessments, particularly in multi-hazard settings. Overall, this thesis provides methods, tools, and insights essential to understand, evaluate and mitigate the vulnerability of a key component of energy infrastructure and support stakeholders in doing so. Furthermore, this thesis offers as strong foundation for future vulnerability and risk assessment of other coastal structures and systems.Item Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure(2014-08-22) Yang, Yongchao; Nagarajaiah, Satish; Padgett, Jamie; Meade, AndrewCivil structures are subjected to ambient loads, natural hazards, and man-made extreme events, which can cause deterioration, damage, and even catastrophic failure of structures. Dense networks of sensors embedded in structures, which continuously record structural data, make possible real-time health monitoring and diagnosis of structures. Effectively and efficiently sensing and processing the massive sensor data, potentially from hundreds of channels, is required to identify (update) structural information and detect damage as early as possible to inform immediate decisionmaking. Different from traditional model-based and parametric methods that usually require intensive computation and expert attendance, this thesis explores a new data-driven methodology towards rapid, unsupervised, and automated system identification and damage detection of structures as well as data management by harnessing the data structure itself. Specifically, the sparse representation and low-rank structure inherent but implicit in the multi-channel structural response data are exploited for efficient data sensing, processing, and management in real-time health monitoring and non-destructive assessment of structures. Numerical simulations, laboratory experiments on bench-scale structures, and real-world structures examples, including seismically excited buildings and a super high-rise TV tower, are investigated.Item Multi-hazard Fragility, Risk, and Resilience Assessment of Select Coastal Infrastructure(2017-04-20) Kameshwar, Sabarethinam; Padgett, JamieThe performance of coastal infrastructure is threatened by natural hazards such as hurricanes and floods. The intensity and frequency of many of these climate related hazards are expected to be influenced by climate change, which will add uncertainty to the performance of coastal infrastructure. Furthermore, coastal regions are experiencing rapid population growth, which is expected to continue in the future as well. Therefore, in view of multiple hazards, uncertainty due to climate change, and increasing coastal population, comprehensive performance assessment of regional portfolio of coastal infrastructure is essential for managing the existing infrastructure and ensuring adequate performance after extreme events such as hurricanes and earthquakes. Therefore, this study focuses on the development of a methodology and supporting tools that can be used to facilitate comprehensive multi-hazard performance assessment of regional portfolios of costal infrastructure. Particularly, this study focuses on above ground storage tanks (ASTs) and bridges since they are crucial components of the energy and transportation infrastructure, respectively, and they have been observed to fail in past events with severe economic and environmental impact. First, in order to describe and effectively communicate the effects of multiple hazards on the performance and design decisions of infrastructure systems, taxonomy for multi-hazard combination is developed. For understanding the effects of different hazards on the performance and design of structures and classifying hazards according to the taxonomy this study develops a dual layer metamodel based fragility assessment methodology. The proposed method harnesses statistical learning techniques to enable efficient response and reliability assessment of structures with a broad range of design details when subjected to hazardous conditions. Using the dual layer fragility assessment methodology, fragility functions are developed for most common failure modes of ASTs: due to strong winds and storm surge. For bridges, the fragility assessment framework is used to develop fragility function for most common causes of bridge failure such as: scour, scour and vehicular loads, barge impact, barge impact and scour, hurricanes, earthquakes, and vehicle loads and earthquakes. This study also develops frameworks for ASTs and bridges to facilitate resilience assessment, i.e. their post event functionality and recovery time, which is essential for comprehensive performance assessment of ASTs and bridges. For ASTs subjected to storm surge and strong winds, the estimates of repair costs, repair time, and estimates of potential spill volumes due to tank failures are developed. Similarly, for bridges, a methodology is developed to assess the entire distribution of repair costs considering uncertainties in damage to bridge components, repair costs, and repair actions. Additionally, empirical seismic damage data is used to develop decision trees that can determine the traffic restrictions and their duration for given the damage state of bridge components such as columns, bearings, and abutments. Finally, in order to develop structure specific performance targets from regional level performance targets this study develops a simple heuristic methodology which determines the performance targets for individual structures commensurate to their performance. All the fragility and resilience assessment frameworks are applied to individual structures and regional portfolio of structures which has provided several valuable insights in to the performance of ASTs and bridges. The application of the fragility functions for ASTs for tanks in the Houston Ship Channel shows that tanks are more vulnerable to storm surges than strong winds. Additionally, application of the fragility functions show that mitigation measure such as anchoring tanks can lead to contradictory effects on the performance of ASTs for different failure modes such as flotation and buckling. For bridges, application of the fragility functions highlights that earthquakes and hurricanes can have competing effects on selection of column height. Furthermore, estimation of seismic repair costs highlights the multi-modal nature of the distribution of repair costs. The use of decision trees to determine traffic restrictions and their duration highlights the influence of damage to different bridge components on traffic restrictions. Finally, the application of the heuristic methodology to determine structure specific performance targets for all the ASTs in the Ship Channel shows that anchoring of tanks and simple procedural measures can significantly reduce the spill volumes. Similarly, application of the heuristic methodology for a portfolio of bridges in the Charleston region shows the framework can be used to obtain bridge specific performance targets which can significantly improve the performance of the entire bridge network.Item Seismic Retrofitting of Low-Rise Reinforced Concrete (RC) Structures: a Multi-Faceted Evaluation(2024-04-23) Laguerre, Marc-Ansy; Desroches, Reginald; Padgett, Jamie; Duno-Gottberg, Luis; Duenas-Osorio, LeonardoThe threat of seismic activity is a major concern for countries worldwide, and many have invested significant resources into researching the seismic retrofit of reinforced concrete (RC) structures. As a result, building codes and retrofit strategies have been enhanced to strengthen vulnerable structures. However, Haiti remains a country with limited knowledge about the vulnerability of RC buildings to seismic events and retrofitting solutions. This study aims to address this knowledge gap by conducting a comprehensive analysis of Haitian RC structures and evaluating multiple retrofit methods to enhance their seismic performance. This study examines the retrofitting of RC buildings in Haiti using deterministic and probabilistic approaches, followed by a Life-Cycle Cost-Benefit (LCCB) analysis to determine the optimal techniques. The study first analyzes Haitian construction norms and practices before selecting building prototypes: R1 (residential 1-story), R2 (residential 2-story), NR2 (non-residential 2-story), and NR3 (non-residential 3-story). These prototypes' columns and beams are designed according to the BAEL (Beton Aux Etats Limites) guidelines, a French construction code widely used for engineered buildings in Haiti before 2010. For the deterministic analysis, a two-phase numerical modeling method is used. Initially, continuum-based finite element models on LS-DYNA are used to validate and derive hysteretic curves of the column joints. Following this, a macroscopic model, which is calibrated from the results from LS-DYNA, is used for non-linear time history analysis of the building's 2D frames using OpenSees. Five retrofit strategies are then added to the original frames: RC shear walls (used for non-residential models), steel braces (used for residential models), buckling-restrained braces (used for non-residential models), prestressed tendons (used for residential models), and RC jackets (used for all models). These retrofits were designed such that the frames do not reach the life safety (LS) objectives of FEMA for a hazard of the return period of 2475 years. A total of 10 ground motions, which include motion recorded in Haiti, are chosen to run the time history analysis and evaluate the retrofit methods' efficiency. It was observed that the using of RC jackets with each of the global retrofits is able to enhance the building's performance to meet chosen performance objectives. This research also assessed retrofitting solutions through probabilistic analysis, generating fragility curves. Initially, empirical fragility curves were derived using post-earthquake data and the shakemap from Haiti's 2021 earthquake, confirming the high vulnerability of Haitian RC buildings. Analytical fragility curves were subsequently developed for the four models representing these structures. Using continuum-based models on LS-DYNA, four damage states (minor, moderate, severe, and collapse) were used and investigated through pushover analyses. The results were then used for a multiple linear regression to predict the drift limit states. A probabilistic seismic demand regression was further derived via time history analysis on a 2D OpenSees model. The resulting analytical fragility curves revealed that incorporating RC jackets and a global retrofit substantially improved building resilience. Finally, a LCCB analysis was conducted to assess the financial implications of the retrofits. By integrating hazard and fragility data with the estimated costs for building repair, replacement, and retrofitting, the benefit of implementing the retrofits was evaluated. The analysis revealed that retrofitting with RC jackets offers significant benefits. However, these benefits are notably higher when RC jackets are combined with steel braces in residential buildings, and with shear walls in non-residential buildings, thus optimizing the structural resilience and financial viability of the retrofitting strategies.Item Situational Awareness Frameworks for Real-Time Sensing of Flood Impacts on Road Transportation Networks(2022-12-02) Panakkal, Pranavesh; Padgett, Jamie; Bedient, Philip; Subramanian, Devika; Duenas-Osorio, Leonardo; Mostafavi, AliSevere storms and associated flooding pose a significant risk to roadway mobility. Consequently, 40 to 60% of flood-related deaths are attributed to vehicle-related incidents in developed countries. A real-time situational awareness framework that can sense road conditions can facilitate safer mobility, reduce vehicle-related drownings, enhance flood response efficiency, and support emergency response decision-making. Existing situational awareness tools, which often depend on limited data sources and show acceptable performance in limited case studies, fall short of providing a comprehensive framework for sensing flood impacts on roads. Particularly, opportunities to significantly improve situational awareness by leveraging existing data sources in urban regions remain untapped. This thesis addresses this need by offering new tools, models, methodologies, and frameworks for detecting flood impacts on roads in real time and advances the current state-of-the-art for sensing roadway conditions during floods. First, this thesis reports results from semi-structured one-on-one needs assessment interviews with stakeholders responsible for managing flood response in Houston. Specifically, it reports situational awareness data needs for facilitating efficient and safe emergency response, most and least valuable information for situational awareness, communication and visualization strategies, and factors influencing stakeholder trust. These insights inform the methodological underpinning of the three situational awareness frameworks proposed in this thesis. The first situational awareness framework proposed in this thesis senses flood impacts on infrastructure using precompiled maps and real-time rainfall data. The framework offers basic situational awareness information accessible to most communities and is appropriate for areas with limited resources. Relying on precompiled maps to sense real-time flood impacts is often insufficient. This study proposes Open Source Situational Awareness Framework for Mobility (OpenSafe Mobility) to provide a more comprehensive sensing of flood impacts on roads. OpenSafe Mobility uses real-time rainfall data, a physics-based flood model, spatial and network analyses, and vehicle characteristics to sense real-time flood impact on the road transportation system. Case studies using three recent storms in Houston, Texas, demonstrate the framework's ability to provide vehicle-class specific roadway conditions for even minor roads and residential streets—a problem existing approaches struggle with. While OpenSafe Mobility case studies highlight its ability to model flood impacts, it also provides evidence that depending on only one source for sensing flood impacts is insufficient. An alternative is to leverage multiple sources in a data fusion framework to sense current flood conditions. This thesis proposes Open Source Situational Awareness Framework for Mobility using Data Fusion (OpenSafe Fusion) to take advantage of this opportunity. First, OpenSafe Fusion identifies different data sources that either directly or indirectly observe flooding in the study region. Next, source-specific data collection and processing workflows are developed, leveraging diverse techniques from spatial analysis to deep learning. The observations from the sources are then combined in real-time using data fusion techniques explicitly accounting for data source characteristics. Case studies using recent storms in Houston, Texas, demonstrate the framework's ability to significantly improve situational awareness data availability and provide reliable estimates of road conditions using existing public data sources. Finally, this thesis uses OpenSafe Fusion to develop a new prototype web tool for Houston that provide real-time road conditions data for enhancing mobility-centric situational awareness. The proposed tool addresses essential stakeholder needs identified during needs assessment interviews. Overall, this thesis provides new tools, models, methodologies, and frameworks to sense flood impacts on roads in real time and quantify network-level impacts of flooding. Applying the methodologies presented in this thesis will significantly improve situational awareness during flooding. Specifically, it will enable emergency responders and decision-makers to identify flooded roads and safer routes, locate isolated communities, reduce delays and detours, and aid equipment selection. In conclusion, the contributions of this thesis have societal importance in enhancing emergency response efficiency and road safety. These contributions are significant and timely considering the potential increase in flood risk to roadway mobility due to climate change and other factors.Item The interdependent networked community resilience modeling environment (IN-CORE)(Elsevier, 2023) van de Lindt, John W.; Kruse, Jamie; Cox, Daniel T.; Gardoni, Paolo; Lee, Jong Sung; Padgett, Jamie; McAllister, Therese P.; Barbosa, Andre; Cutler, Harvey; Van Zandt, Shannon; Rosenheim, Nathanael; Navarro, Christopher M.; Sutley, Elaina; Hamideh, SaraIn 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform. Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.