R-3 Repository :: Browsing by Author "Duenas-Osorio, Leonardo"

Browsing by Author "Duenas-Osorio, Leonardo"

Now showing 1 - 19 of 19

Constrained Counting and Sampling: Bridging the Gap Between Theory and Practice
(2017-09-29) Meel, Kuldeep Singh; Chakraborty, Supratik; Chaudhuri, Swarat; Duenas-Osorio, Leonardo; Seshia, Sanjit A.; Vardi, Moshe Y.
Constrained counting and sampling are two fundamental problems in Computer Science with numerous applications, including network reliability, privacy, probabilistic reasoning, and constrained-random verification. In constrained counting, the task is to compute the total weight, subject to a given weighting function, of the set of solutions of the given constraints. In constrained sampling, the task is to sample randomly, subject to a given weighting function, from the set of solutions to a set of given constraints. Consequently, Constrained counting and sampling have been subject to intense theoretical and empirical investigations over the years. Prior work, however, offered either heuristic techniques with poor guarantees of accuracy or approaches with proven guarantees but poor performance in practice. In this thesis, we introduce a novel hashing-based algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress made in Boolean reasoning solving, in particular, {\SAT} and {\SMT}, over the past two decades. By exploiting the connection between definability of formulas and variance of the distribution of solutions in a cell defined by 3-universal hash functions, we introduced an algorithmic technique, {\MIS}, that reduced the size of XOR constraints employed in the underlying universal hash functions by as much as two orders of magnitude. The resulting frameworks for counting ( {\ScalApproxMC}) and sampling ({\UniGen}) can handle formulas with up to million variables representing a significant boost up from the prior state of the art tools' capability to handle few hundreds of variables. If the initial set of constraints is expressed as Disjunctive Normal Form (DNF), {\ScalApproxMC} is the only known Fully Polynomial Randomized Approximation Scheme (FPRAS) that does not involve Monte Carlo steps. We demonstrate the utility of the above techniques on various real applications including probabilistic inference, design verification and estimating the reliability of critical infrastructure networks during natural disasters. The high parallelizability of our approach opens up new directions for development of artificial intelligence tools that can effectively leverage high-performance computing resources.
Distributed Hydrologic Modeling of Large Storm Events in the Houston-Galveston Region
(2013-03-04) Deitz, Roni; Bedient, Philip B; Duenas-Osorio, Leonardo; Raun, Loren
In conjunction with the SSPEED Center, large rainfall events in the upper Gulf of Mexico are being studied in an effort to help design a surge gate to protect the Houston Ship Channel during hurricane events. When hurricanes hit Galveston Bay, there is a funneling effect and, depending on the track of the hurricane, the storm surge can vary by as much as 5 to 10 feet. For instance, Hurricane Ike produced a surge of about 13 feet in the bay; however, other tracks and higher winds could bring a worst case scenario of 20 to 25 feet of storm surge. Since the Houston Ship Channel is only protected from flooding up to 14-15 feet, and is currently the world’s second largest petrochemical complex, it is critical to understand the linkage between rainfall and storm surge to better protect the region. In this effort, rainfall events in the Houston-Galveston area are being examined. Given the large size of the watersheds flowing from the north and west, statistical methodologies, such as the Probable Maximum Precipitation (PMP) and Precipitation Depth Duration Frequency (PDDF), were employed to better design and predict the shape, pattern, size, and intensity of large rainfall events. Using Hydrometeorological Report (HMR) 52, as well as local hydrologic reports, the 24 hour PMP storm event was created for the upper Gulf of Mexico. In addition, large historic storms, such as Hurricane Ike, and simulated rainfalls from Hurricanes Katrina and Rita, were modeled over the Houston-Galveston region in a hydrologic/hydraulic model with the use of radar and rain gauge data. VfloTM, a distributed hydrologic model was used to model the aforementioned storms. The region was first calibrated to USGS stream gauge data from Greens Bayou Brays Bayou and Peach Creek, and the modeled results accurately depict key features of observed hydrographs, including time to peak, discharge, and the double peak discharge phenomenon caused by double rain bursts. Once calibrated, VfloTM, is used to quantify the effect that storm size, intensity, and location has on timing and peak flows in the upper drainage area. Results indicate that there is a double peak phenomenon with flows from the west draining earlier than flows from the north. With storm surge typically lasting 36-48 hours, this indicates the flows from the west and north are interacting with storm surge, with flows from the west arriving before flows from the north downstream. Gate operations were optimized in the model to account for the relative timing of upland runoff and hurricane surge, as well as the capability of the gate structure to protect the Ship Channel industry was quantified.
Efficient seismic fragility assessment of highway bridges on liquefiable soils
(2009) Aygun, Bayram; Duenas-Osorio, Leonardo
The increasing failure potential of U.S. highway bridges from aging and susceptibility to damage in extreme events, such as earthquakes, necessitates the development of efficient assessment tools to help infrastructure owners prioritize maintenance and rehabilitation interventions. Particularly, seismic fragility curves are useful tools because they state the failure probability of structures conditioned on earthquake intensity levels. This study focuses on computationally efficient coupled bridge-soil-foundation (CBSF) analyses and develops fragility curves for multi-span continuous steel bridges (MSCS) typical of the central and eastern U.S. (CEUS) when exposed to earthquake-induced soil liquefaction. The resulting fragility curves show an increase in the vulnerability of rocker bearings and bent piles. Moreover, depending on the type of soil layer overlying the liquefiable sand, liquefaction either decreases or increases the fragility of columns, whereas the probability of unseating at the abutments increases when liquefaction is explicitly modeled. Lastly, bridge system fragilities are increased for extensive and complete damage.
Improved Seismic Risk Assessment of Non-ductile Reinforced Concrete Buildings
(2014-02-28) Fuselier, Blaine; Padgett, Jamie E.; Duenas-Osorio, Leonardo; Nagarajaiah, Satish
Existing reinforced concrete (RC) buildings built to non-ductile specifications are highly susceptible to damage given lateral loads induced from earthquake ground motions. To explore the effects of these ground motions, non-linear finite element analyses are being used in research and practice to model representations of non-ductile RC buildings as well as conduct probabilistic analyses of their seismic fragility in as-built and retrofitted conditions. This study examines the influence of modeling fidelity on the response and fragility of non-ductile RC buildings, testing the role of explicitly capturing local failure in the finite element model as well as providing new insight into the probability of component damage levels given system level failure. Also, a survey is presented to assess the tagging decisions made during post-earthquake rapid evaluations of reinforced concrete buildings and compare these results to empirical data from past earthquake reconnaissance reports. The results of this study will provide insight into several key issues in seismic performance assessment for RC buildings.
Interdependent Restoration of Infrastructure Networks with Humans in the Loop: decentralized and strategic decision processes
(2021-08-13) Talebiyan, Hesam; Duenas-Osorio, Leonardo
This dissertation brings essential elements of real-world decision-making environments into restoration strategies for interdependent networks after contingencies. Existing strategies have been predominantly developed from a centralized perspective. In reality, however, several autonomous decision-makers or agents take actions within and across these networks. Agents usually need to communicate and coordinate to make informed decisions. In practice, however, communications are typically noisy, delayed, or simply nonexistent. Therefore, agents cannot solicit enough information (pertinent to their decisions) from other agents when they need it. In this study, I focus on modeling such a decentralized decision-making environment with imperfect information flow, particularly when facing contingencies from natural hazards---a context in which modeling is known to appeal to community stakeholders. My proposed algorithms presuppose that decision-makers iteratively go through stages seen in the field to devise restoration plans, particularly resource allocation and decision-making stages. As for the first stage, I employ auction theory---given its theoretical and practical appeal---to propose a rigorous decision tool to address the problem of efficient, decentralized resource allocation. As for the second sage, I employ a decentralized approach to solve a family of restoration optimization models subject to budget and operational constraints. The novelty of my approach is the recognition that insufficient information forces agents to use their judgment and domain expertise to speculate other agents' potential decisions. Introducing the concept of Judgment Call (JC), I cover a spectrum of agents' potential mindsets toward judgment with ubiquitous optimism and pessimism at the extremes. Also, I model the agents' interaction during the decision-making stage using Bayesian games. This novel approach models decision-makers' strategic behavior while accounting for their incomplete information and bounded rationality. In addition, it allows me to study the effect of cooperation and competition in the decision processes. The proposed models are demonstrated, first, with ideal yet controllable synthetic networks, and then with the realistic infrastructure of Shelby County, TN, subject to hazard-consistent seismic scenarios. The results show that allocation mechanisms based on combinatorial auctions significantly improve upon equality-driven uniform allocations in terms of resilience. Furthermore, I show that not only are cooperation and optimism the key to gain near-optimal decisions, but also, agents could be offered realistic incentives to act cooperatively.
Mesoscale Modeling of Distributed Water Systems Enables Policy Search
(Wiley, 2023) Zhou, Xiangnan; Duenas-Osorio, Leonardo; Doss-Gollin, James; Liu, Lu; Stadler, Lauren; Li, Qilin; Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment
It is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, it is unclear which are beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a mesoscale representation model that approximates DWSs with reduced backbone networks to enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multiobjective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston's water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The mesoscale modeling approach and the multiobjective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.
Nonlinear Stochastic Analysis of Motorcycle Dynamics
(2013-09-16) Robledo Ricardo, Luis; Spanos, Pol D.; Barrera, Enrique V.; Dick, Andrew J.; Duenas-Osorio, Leonardo
Off-road and racing motorcycles require a particular setup of the suspension to improve the comfort and the safety of the rider. Further, due to ground unevenness, off-road motorcycle suspensions usually experience extreme and erratic excursions in performing their function. In this regard, the adoption of nonlinear devices, such as progressive springs and hydro pneumatic shock absorbers, can help limiting both the acceleration experienced by the sprung mass and the excursions of the suspensions. For dynamic analysis purposes, this option involves the solution of the nonlinear differential equations that govern the motion of the motorcycle, which is excited by the stochastic road ground profile. In this study a 4 degrees-of-freedom (4-DOF) nonlinear motorcycle model is considered. The model involves suspension elements with asymmetric behaviour. Further, it is assumed that the motorcycle is exposed to loading of a stochastic nature as it moves with a specified speed over a road profile defined by a particular power spectrum. It is shown that a meaningful analysis of the motorcycle response can be conducted by using the technique of statistical linearization. The validity of the proposed approach is established by comparison with results from pertinent Monte Carlo studies. In this context the applicability of auto-regressive (AR) filters for efficient implementation of the Monte Carlo simulation is pointed out. The advantages of these methods for the synthesis of excitation signals from a given power spectrum, are shown by comparison with other methods. It is shown that the statistical linearization method allows the analysis of multi-degree-of-freedom (M-DOF) systems that present strong nonlinearities, exceeding other nonlinear analysis methods in both accuracy and applicability. It is expected that the proposed approaches, can be used for a variety of parameter/ride quality studies and as preliminary design tool by the motorcycle industry.
Parameterized Seismic Reliability Assessment and Life-Cycle Analysis of Aging Highway Bridges
(2013-09-16) Ghosh, Jayadipta; Padgett, Jamie E.; Duenas-Osorio, Leonardo; Nagarajaiah, Satish; Allen, Genevera
The highway bridge infrastructure system within the United States is rapidly deteriorating and a significant percentage of these bridges are approaching the end of their useful service life. Deterioration mechanisms affect the load resisting capacity of critical structural components and render aging highway bridges more vulnerable to earthquakes compared to pristine structures. While past literature has traditionally neglected the simultaneous consideration of seismic and aging threats to highway bridges, a joint fragility assessment framework is needed to evaluate the impact of deterioration mechanisms on bridge vulnerability during earthquakes. This research aims to offer an efficient methodology for accurate estimation of the seismic fragility of aging highway bridges. In addition to aging, which is a predominant threat that affects lifetime seismic reliability, other stressors such as repeated seismic events or simultaneous presence of truck traffic are also incorporated in the seismic fragility analysis. The impact of deterioration mechanisms on bridge component responses are assessed for a range of exposure conditions following the nonlinear dynamic analysis of three-dimensional high-fidelity finite element aging bridge models. Subsequently, time-dependent fragility curves are developed at the bridge component and system level to assess the probability of structural damage given the earthquake intensity. In addition to highlighting the importance of accounting for deterioration mechanisms, these time-evolving fragility curves are used within an improved seismic loss estimation methodology to aid in efficient channeling of monetary resources for structural retrofit or seismic upgrade. Further, statistical learning methods are employed to derive flexible parameterized fragility models conditioned on earthquake hazard intensity, bridge design parameters, and deterioration affected structural parameters to provide significant improvements over traditional fragility models and aid in efficient estimation of aging bridge vulnerabilities. In order to facilitate bridge management decision making, a methodology is presented to demonstrate the applicability of the proposed multi-dimensional fragility models to estimate the in-situ aging bridge reliabilities with field-measurement data across a transportation network. Finally, this research proposes frameworks to offer guidance to risk analysts regarding the importance of accounting for supplementary threats stemming from multiple seismic shocks along the service life of the bridge structures and the presence of truck traffic atop the bridge deck during earthquake events.
Embargo
Principled Approximate Models for Reliability and Resilience Assessment of Electric Power Infrastructure
(2024-08-09) Patil, Jayant; Duenas-Osorio, Leonardo
Reliable infrastructure systems are essential for community resilience against natural hazards and extreme events. Recent events such as Hurricane Beryl in 2024, Turkey-Syria earthquakes in 2023, Winter Storm Uri in 2021, Hurricane Florence in 2018, and Hurricane Matthew in 2016, have highlighted the vulnerability of electric power networks (EPNs) to such disasters. This vulnerability underscores the need for robust performance assessments, which play a pivotal role in guiding decisions related to the design, operation, maintenance, upgrade, and repair of EPNs. Reliability and resilience are key performance indicators for critical infrastructure systems such as electric power grids. Electric power systems are considered critical due to their crucial role in maintaining the normal function of communities, interdependence on other infrastructure systems, and broader national stability. The computational methods used to estimate these performance metrics require realistic and accurate data, appropriate digital models for the computational problems, and high computational power along with suitable approximation techniques. While prior work has focused on the use of synthetic data and surrogate models to overcome these challenges, there is significant room for improvement, particularly for applications in community-level analyses. This dissertation aims to address these challenges through methodological advancements and algorithmic improvements for assessing the reliability of EPNs, especially for small communities with limited access to real-world data. By focusing on methodological innovations informed by practical constraints, this research seeks to provide more accurate and efficient tools for infrastructure analysis for the upkeep of real world infrastructure systems. This dissertation introduces significant advancements in the assessment and enhancement of electric power networks (EPNs). First, it proposes an integrated modeling technique that accounts for both the power transmission system and power distribution system in building-level power availability analyses, and thus better assess impacts on community members. We aim to address the limited availability of real power grid data by creating equivalenced synthetic grid models. Power transmission and distribution performance assessments remain largely siloed today, but require integration to accurately assess community-level impacts. To integrate analysis of the power transmission network and the power distribution network in community-level reliability and resilience assessment, we use power availability from the power transmission network analysis as tractable boundary conditions for the power distribution network analysis. Secondly, this dissertation proposes a novel recursive method designed to compute the expected functional reliability of radial infrastructure systems. Our technique is computationally efficient, enabling accurate reliability assessments under various operational scenarios and providing crucial insights into system behavior both during routine operations and following disruptive events. As our technique uses an application agnostic formulation, it can be applied to any infrastructure system with radial topology such as power distribution systems, water distribution systems, renewable energy farms, etc. Finally, this work addresses the high computational demands of traditional reliability analysis by developing graph neural network (GNN) models within an augmented artificial intelligence framework. Our proposed GNN model efficiently approximates all-terminal reliability, drastically reducing computational costs while maintaining accuracy in connectivity reliability assessments. Together, these methodologies offer a comprehensive suite of tools for assessing and improving the reliability of critical infrastructure in the face of natural hazards and other extreme conditions. Our methods and models are validated using several case studies with real communities such as Lumberton, North Carolina, and Galveston Island, Texas, alongside numerous synthetic benchmarks. The adoption of these integrated modeling, recursive reliability assessment, and GNN based all terminal reliability approximation techniques holds promise for improving decision support for community-level infrastructure resilience planning and intervention. By addressing data scarcity, reducing computational cost, and integrating practical constraints, these methods will contribute to more effective infrastructure reliability assessments and facilitate integration of socio-techno-economic models for improving the performance of systems during and after extreme events. Ultimately, this research aims to empower small communities to plan for, withstand, and recover from extreme events.
Resilience Planning for Water Distribution Systems
(2023-11-30) Zhou, Xiangnan; Duenas-Osorio, Leonardo
Urban water distribution systems are lifeline infrastructure, playing a crucial role on community resilience, safety, and governance. They are vulnerable to various threats, including extreme hazard events, inevitable infrastructure deterioration, disruptions from upstream dependencies, and climate change. Facing these evolving challenges, it is urgent for water utilities to incorporate resilience management into their daily operations and long-term strategic planning. In the past two decades, existing research on the resilience management of water distribution systems primarily focuses on resilience assessment. Besides quantifying resilience, an important question for water utilities is what proactive measures can be taken before disruptions to improve system resilience. This dissertation aims to develop algorithms and tools to guide water utilities’ long-term resilience planning. First, we identify and establish useful performance measures for the resilience planning of water utilities. Second, we develop novel approaches for guaranteed deterministic and probabilistic vulnerability analysis of water distribution to inform mitigation strategy development. For deterministic vulnerability diagnosis, we develop a guaranteed N-k contingency analysis method, which builds on a novel search algorithm that integrates enumeration and a guaranteed-error sampling scheme. For probabilistic vulnerability quantification, we establish a probabilistic functionality analysis scheme that incorporates the guaranteed-error sampling scheme for principled estimations. Third, we introduce advanced measures and tools to facilitate heuristic and optimal mitigation strategy development. Regarding decision modeling, we suggest using a superquantile measure as a probabilistic decision criterion to account for risk averseness and deep uncertainty. We propose network augmentation as an alternative mitigation strategy that goes beyond traditional component hardening. For optimal mitigation, we introduce the Cross Entropy method, a promising optimization analysis tool for combinatorial stochastic optimization problems. Finally, we develop generic system modeling and policy search methodologies for water distribution reconfiguration, exploring opportunities to alter the layout of water distribution systems. Specifically, we develop a meso-scale water distribution system representation model that approximates large-scale water systems using reduced backbone networks to enable policy search. We establish a multi-objective optimization model that explores trade-offs across alternative distributed water system configurations. We demonstrate and test our approaches and tools on hypothetical and practical water systems. We find that the internal vulnerabilities within a water distribution system are determined by the complex interactions among its network topology, physical characteristics, and demand patterns. There are usually a few critical components whose failure can cause significant performance losses to water distribution systems, while the rest of the system components have a uniformly small impact on system functioning. Informed hardening and network augmentation strategies can yield efficient vulnerability reduction, improving distribution resilience. Additionally, reconfiguring centralized water systems to distributed alternatives presents an opportunity to enhance supply resilience. This dissertation aims to provide water utilities with the knowledge and tools they need to navigate alternative resilience-improving strategies, ranging from component hardening to network augmentation to system reconfiguration. The algorithms and tools we develop can be implemented on resilience-oriented planning platforms, such as the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) and the Computational Modeling and Simulation Center (SimCenter) to guide practical resilience planning for water distribution systems.
RISK-BASED DESIGN OF BRIDGES AND ASSOCIATED TRANSPORTATION NETWORKS UNDER NATURAL HAZARDS
(2014-04-25) Wang, Zhenghua; Padgett, Jamie E.; Duenas-Osorio, Leonardo; Nagarajaiah, Satish; Stein, Robert M.; Tobias, Daniel
The highway infrastructure system in the United States is deteriorating and facing an increased number of threats from natural and man-made hazards, including earthquakes, scour, hurricanes, and vehicle collisions. At the same time, the reliable functioning of the highway system plays an important role in emergency response and recovery processes after disaster strikes. However, there are several inadequacies in current codes and associated practice for the design of bridges, as well as funding restrictions for their upkeep. Although recent changes in the seismic design of bridges have adopted displacement-based design approaches to promote adequate performance under seismic loads, the current design philosophy hinges upon a uniform hazard perspective without explicit consideration of a homogeneous risk of damage or collapse. In addition, this approach does not reflect the influence of individual bridges on the transportation network behaviour, which is desirable to estimate the performance of a transportation infrastructure system and enhance its overall post-event operation. Moreover, current bridge design specifications deal with various extreme hazards independent of one another. The reliable performance of transportation infrastructure systems under natural hazards requires a new life-cycle risk-based design method, along with effective resource assignment and prioritization strategies. This thesis will address these noted gaps by putting forward a risk consistent design approach for bridges and associated transportation networks. To enable the proposed shift toward risk-based design of bridges and transportation networks, this thesis develops a framework to evaluate the life-cycle risk (LCR) and life-cycle cost (LCC) of structures based on the time-dependent hazard function approach. The resulting LCR formulation provides a basis for inverse risk analyses to determine the design parameters required to achieve an acceptable risk level. A key advantage of this formulation relative to most existing methods is that it captures the change in structural vulnerability throughout a structure’s lifetime due to structural deterioration as well as changes in loading, while most previous studies neglect this feature assuming that the annual failure probability of a structure or an infrastructure system is constant during the design life. This methodology is also amenable to future extensions that include benefits and impacts to society. To incorporate the transportation infrastructure system level performance into the design and retrofit of bridges in practice, this thesis proposes a new bridge ranking method based on graph theoretic metrics that quantifies network topology features while also incorporating individual bridge characteristics, such as bridge vulnerability and construction cost. This methodology is flexible enough to include in the future socio-demographic factors (e.g., population density, median household income, and vote margin) that affect policy and the distribution of funds to top ranking bridges. Based on the bridge ranking results, an inverse reliability method is used to quantitatively determine the individual bridge reliability levels required to achieve a target performance for entire transportation networks—a new result bound to inform engineering practice. This top-down bridge design approach is superior to current structure-specific design methods because highway bridges are integral parts of entire transportation networks, which means that the design of new bridges or prioritization of existing bridges for upgrade based solely on their structural behavior is not appropriate. Bridge design must account for the topology of the transportation network and the desired system-level performance. Based on the required individual bridge reliability levels for the transportation network, a new method for displacement-based uniform risk design of bridges under seismic hazards considering the effect of soil-structure interaction is also put forward. The method is desirable in practice to reduce the uncertainty in the performance of bridges across regions. Furthermore, no risk-based combination of extreme events for the design of bridges is currently available. This thesis investigates the feasibility of establishing a risk-based design framework to address multiple extreme hazards, particularly scour and earthquake, because they are the most common reason of collapse of bridges in the United States. This risk-consistent multi-hazard bridge design framework provides a basis for combinations of earthquake and scour loads that is also consistent with the load and resistance factor design (LRFD) methodology that is widely used in practice. In addition to providing an LCR framework to the risk-based design of highway bridges and associated transportation networks, modeling complexities typically simplified or neglected in the risk assessment and design of bridges are explored in this thesis. For example, the influence of vertical ground motions as well as soil-structure interaction and liquefaction, which tend to be ignored in current bridge design approaches, are accounted for within the uniform risk framework and illustrated through the seismic risk assessment of a coupled bridge-soil-foundation system. The integrated, uniform, and risk-based framework proposed in this thesis has the potential to directly improve the safety and reliability of transportation infrastructure systems under deteriorating conditions and in the presence of natural hazards and limited funds in the United States. The findings of this thesis will benefit the department of transportation (DOT), AASHTO committee, and other agencies, such as offices of emergency management. The risk-consistent framework is desirable in practice to reduce the uncertainty in the performance of highway transportation system across regions under multiple natural hazards.
Screw and Edge Dislocations in Cement Phases: Atomic Modeling
(2013-10-09) Chen, Lu; Shahsavari, Rouzbeh; Nagarajaiah, Satish; Duenas-Osorio, Leonardo
Cement is the key strengthening and the most energy-intensive ingredient in concrete. With increasing pressure for reducing energy consumption in cement manufacturing, there is an urgent need to understand the basic deformation mechanisms of cement. In this thesis, we develop a computational framework based on molecular dynamics to study two common types of defects, namely screw and edge dislocations, in complex, anisotropic crystalline polymorphs of cement clinkers and cement hydration products. We found the likelihood of these defects in regions with higher Young moduli. We also found the preferred cement polymorphs that require less energy for grinding via analysis of Peierls stresses. Together, the results provide a detailed understanding of the role and type of defects in cement phases, which impact the rate of hydration, crystal growth and grinding energy. To our knowledge, this is the first study with atomic-resolution on deformation-based mechanisms in cement crystalline phases.
Seismic response control of structures using novel adaptive passive and semi-active variable stiffness and negative stiffness devices
(2013-09-16) Pasala, Dharma Theja; Nagarajaiah, Satish; Padgett, Jamie E.; Duenas-Osorio, Leonardo; Meade, Andrew J., Jr.
Current seismic design practice promotes inelastic response in order to reduce the design forces. By allowing the structure to yield while increasing the ductility of the structure, the global forces can be kept within the limited bounds dictated by the yield strength. However, during severe earthquakes, the structures undergo significant inelastic deformations leading to stiffness and strength degradation, increased interstory drifts, and damage with residual drift. The research presented in this thesis has three components that seek to address these challenges. To prevent the inelastic effects observed in yielding systems, a new concept “apparent weakening” is proposed and verified through shake table studies in this thesis. “Apparent weakening” is introduced in the structural system using a complementary “adaptive negative stiffness device” (NSD) that mimics "yielding” of the global system thus attracting it away from the main structural system. Unlike the concept of weakening and damping, where the main structural system strength is reduced, the new system does not alter the original structural system, but produces effects compatible with an early yielding. Response reduction using NSD is achieved in a two step sequence. First the NSD, which is capable of exhibiting nonlinear elastic stiffness, is developed based on the properties of the structure. This NSD is added to the structure resulting in reduction of the stiffness of the structure and NSD assembly or “apparent weakening”-thereby resulting in the reduction of the base shear of the assembly. Then a passive damper, designed for the assembly to reduce the displacements that are caused due to the “apparent weakening”, is added to the structure-thereby reducing the base shear, acceleration and displacement in a two step process. The primary focus of this thesis is to analyze and experimentally verify the response reduction attributes of NSD in (a) elastic structural systems (b) yielding systems and (3) multistory structures. Experimental studies on 1:3 scale three-story frame structure have confirmed that consistent reductions in displacements, accelerations and base shear can be achieved in an elastic structure and bilinear inelastic structure by adding the NSD and viscous fluid damper. It has also been demonstrated that the stiffening in NSD will prevent the structure from collapsing. Analogous to the inelastic design, the acceleration and base shear and deformation of the structure and NSD assembly can be reduced by more than 20% for moderate ground motions and the collapse of structure can be prevented for severe ground motions. Simulation studies have been carried on an inelastic multistoried shear building to demonstrate the effectiveness of placing NSDs and dampers at multiple locations along the height of the building; referred to as “distributed isolation”. The results reported in this study have demonstrated that by placing a NSD in a particular story the superstructure above that story can be isolated from the effects of ground motion. Since the NSDs in the bottom floors will undergo large deformations, a generalized scheme to incorporate NSDs with different force deformation behavior in each storey is proposed. The properties of NSD are varied to minimize the localized inter-story deformation and distribute it evenly along the height of the building. Additionally, two semi-active approaches have also been proposed to improve the performance of NSD in yielding structures and also adapt to varying structure properties in real time. The second component of this thesis deals with development of a novel device to control the response of structural system using adaptive length pendulum smart tuned mass damper (ALP-STMD). A mechanism to achieve the variable pendulum length is developed using shape memory alloy wire actuator. ALP-STMD acts as a vibration absorber and since the length is tuned to match the instantaneous frequency, using a STFT algorithm, all the vibrations pertaining to the dominant frequency are absorbed. ALP-STMD is capable of absorbing all the energy pertaining to the tuned-frequency of the system; the performance is experimentally verified for forced vibration (stationary and non-stationary) and free vibration. The third component of this thesis covers the development of an adaptive control algorithm to compensate hysteresis in hysteretic systems. Hysteretic system with variable stiffness hysteresis is represented as a quasi-linear parameter varying (LPV) system and a gain scheduled controller is designed for the quasi-LPV system using linear matrix inequalities approach. Designed controller is scheduled based on two parameters: linear time-varying stiffness (slow varying parameter) and the stiffness of friction hysteresis (fast varying parameter). The effectiveness of the proposed controller is demonstrated through numerical studies by comparing the proposed controller with fixed robust H∞ controller. Superior tracking performance of the LPV-GS over the robust H∞ controller in different displacement ranges and various stiffness switching cases is clearly evident from the results presented in this thesis. The LPV-GS controller is capable of adapting to the parameter changes and is effective over the entire range of parameter variations.
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, Leonardo
The 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.
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, Ali
Severe 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.
The Construction of Sustainability in the Cement Industry: Audit Culture, Materiality and Affective Processes
(2013-09-16) Resendez de Lozano, Laura; Faubion, James D.; Boyer, Dominic C.; Duenas-Osorio, Leonardo; Padgett, Jamie E.; Schuler, Douglas A.
Introducing sustainability policies in the cement industry involves changing not only production technologies, but the organizational culture of a mature industry that is characterized by huge CO₂ emissions and significant environmental impacts. This research attempts to understand the transition process of the industry and its employees as the process is taking place. The actors involved are strongly influenced by often contradictory forces: On one hand the naturalized market dynamics in the context of the automobile dependent society and widespread networks of highways and other concrete structures, and on the other, the growing concern of preserving resources for future generations as a shared responsibility that raises awareness of the negative environmental impacts of cement production. The fieldwork component of the project was comprised of two complementary parts: First, an ethnographic study of how the abstract goal of becoming sustainable is given meaning as it is implemented in Cemex, one of the largest companies in the cement industry at the global level. Second, an analysis of the audit culture mechanisms present in the production of knowledge among experts involved in designing sustainability assessment mechanisms for infrastructure projects. The latter component took place among experts in the academy and in the Texas Department of Transportation, which represents at the same time a regulating force and a key client of the cement industry. To present the findings, I approach the subject of sustainability as a construction project where cement and sustainability act as boundary objects between multiple communities (Star and Griesemer 1989) at the same time that sustainability is being constructed. I attempt to present the interactions as an institutional ecology with multiple actors and layers of meaning which are interdependent. The work first describes the prevailing landscape of the urban environment pointing to the influence of aesthetic discourses through the course of history from modernism to brutalism and place-making as well as to the prevailing regulatory, geographic and cultural conditions. Here, the landscape is taken as the point of departure where the construction project of sustainability is to take place given that its characteristics allow certain constructions of sustainability while thwarting others. I consider the built environment to be the response to the surrounding conditions that constitute the landscape and to the prevailing preferences of key players. To follow, I describe the main actors who participate in the construction of sustainability including internal and external stakeholders. I take these groups as members of the construction crew of sustainability presenting their interests as they relate to the triple bottom line and to their affiliation to multiple publics (Warner 2002). Next, I turn to the accreditation mechanisms and the dynamics followed by experts and their interlocutors defining the blueprints which the cement industry must follow while sustainability is being constructed within the company and in dialogue with stakeholders. These blueprints are the result of negotiations between experts in industry, government and academy and portray the influence of audit culture, the widespread trust in quantification and the importance of the efficiency paradigm as described by informants. Afterwards, I focus on the construction of sustainability project that takes place within the cement company where multiple avenues are followed to complete the building of sustainability as a material object, combining the blueprints defined by experts as they are translated into concrete demonstrations of sustainability with the subjective interpretations of actors within the material constraints set by concrete and the plasticity of sustainability. While this is the institutional response to comply with sustainability expectations, the final construction of sustainability needs to include the construction of the sustainable subject where individuals incorporate into their mindset sustainability considerations. As the last part of the work, I discuss the emergence of sustainable subjectivities among key participating members of the construction crew of sustainability taken as employees and other stakeholders, presenting the distinct logics followed by individuals while becoming committed to sustainability. Finally, I present the conclusions of this constructive analysis. Foucault’s (Burchell, Gordon, and Miller 1991) concept of governmentality and Strathern’s (Strathern 2000b) analysis of audit culture frame this study, offering a common thread that transforms the need of corporate legitimacy into a process of accountability and transparency that resembles Rose and Miller’s (Rose and Miller 2008) description of the neoliberal rationalities of government. Paradoxically, sustainability as an ideal is transformed into an established system that tends to be mechanical. For this to occur, experts shape the meaning of sustainability and determine the parameters that must be met, creating metrics and certification processes that define a set of procedures that track and evaluate sustainability performance, hence defining what practices are selected by cement companies to demonstrate their sustainability credentials, and how these are implemented. Furthermore, both sustainability and cement are vibrant matters (Bennett 2010) with an agency of their own which introduces further constraints into the construction of sustainability process and influences the pace of change. However, the process of becoming sustainable is far from homogenous since each individual relates to sustainability according to the gamut of personal ethical convictions, affective needs, aesthetic preferences and gender perceptions which vary among many factors, including social class, geographic region, educational level and gender. Hence, it is not suitable for a single definition even when subjected to seemingly objective standards. In addition, in the case of employees, the interaction with different groups of stakeholders raises awareness about particular interests also influencing the meaning making process for each of them. Hence, the making of sustainable subjects not only involves the creation of specific regulatory practices tied to the emergence of a greater concern for social and environmental challenges but also the particular context of the individual. Even in this highly structured environment, the affect/emotion dynamic strongly shapes the interpretation and the weight that sustainability eventually gains. The material expressions of sustainability mediate the process and materialize morality at the same time (Verbeek 2006) given the underlying ethical position that sustainability as an idea conveys. As sustainability is becoming widely adopted and introduced into the conscience of more people, it is also being transformed into a numerical parameter that makes possible the perpetuation of market efficiency parameters. Capitalism is thus legitimated through the meta-narrative of sustainability as the triple bottom line that promises to fulfill the desire of progress for all while not really transforming the life-style and consumption patterns of today. As the concept of the triple bottom line enables sustainability to be adopted by key economic, governmental and NGO actors, it also contributes to the naturalization of market forces and profit oriented priorities making it difficult to re-orient human activities towards more environmentally friendly and socially inclusive models of community organization.
The efficacy of preparing for natural disasters
(Kinder Institute for Urban Research, 2013) Stein, Robert; Buzcu-Given, Birnur; Duenas-Osorio, Leonardo; Subramanian, Devika
Previous research has identified a host of actions individuals take in preparation for pending natural disasters. We do not know, however, how these preparations affect outcomes, including property damage, personal injury and evacuation behavior. In this study we argue that different modes/types of preparation produce different outcomes and are associated with different predictors. We test our hypotheses with data from a survey conducted with residents of Harris County, Texas, after Hurricane Ike in 2008. We find that preparations for hurricanes cluster around two distinct dimensions; preventative preparation (e.g., raising the level of residence, purchasing insurance) and mitigating preparation (e.g., buying water and food, filling gas tank). We tested the relationship between preparation and outcomes by defining preparation as a function of risk and other determinants of risk identified in the literature including prior hurricane experience, demographics, living closer to the coast, and information seeking. We find that those who prepare are most likely to confront greater risk from approaching hurricanes than those who do not prepare. We also find that preventative preparation has a significant and negative effect on bad outcomes, specifically in property damage. Mitigating preparation, however, has a significant and negative effect on the likelihood individuals evacuate, especially residents of non-evacuation areas. Our findings have strong implications on how emergency planners and local officials should prepare for and communicate with the public before severe weather episodes.
The Right to Party (Resources): Political Party Networks and Candidate Success
(2014-12-04) Kettler, Jaclyn J; Hamm, Keith E; Stein, Robert M; Duenas-Osorio, Leonardo
How does the structure of political party organizations impact candidates in elections and the legislature? How does the position of candidates within the party affect their success? To address these questions in my dissertation, I use social network analysis to study candidates’ relationships and the context around those relationships. I measure party networks with campaign finance transactions in seven states for the 2010 and 2012 state legislative elections. After a case study of Texas parties that establishes the validity of my approach, I compare the structure of party networks across states. Although I discover that these networks are relatively sparse in general, my results also reveal that parties in states with competitive legislative chambers tend to be more connected. Finally, I explore how the party structure influences candidates. By drawing upon Ronald S. Burt’s (1992, 2005) structural holes theory, I identify influential actors and examine how their network position impacts their success in legislatures. I find that influential candidates in the electoral party network are more likely to become a legislative leader in the following session, demonstrating an important link between electoral and legislative politics.
Time-dependent resilience assessment and improvement of urban infrastructure systems
(American Institute of Physics, 2012) Ouyang, Min; Duenas-Osorio, Leonardo
This paper introduces an approach to assess and improve the time-dependent resilience of urban infrastructure systems, where resilience is defined as the systemsﾒ ability to resist various possible hazards, absorb the initial damage from hazards, and recover to normal operation one or multiple times during a time period T. For different values of T and its position relative to current time, there are three forms of resilience: previous resilience, current potential resilience, and future potential resilience. This paper mainly discusses the third form that takes into account the systemsﾒ future evolving processes. Taking the power transmission grid in Harris County, Texas, USA as an example, the time-dependent features of resilience and the effectiveness of some resilience-inspired strategies, including enhancement of situational awareness, management of consumer demand, and integration of distributed generators, are all simulated and discussed. Results show a nonlinear nature of resilience as a function of T, which may exhibit a transition from an increasing function to a decreasing function at either a threshold of post-blackout improvement rate, a threshold of load profile with consumer demand management, or a threshold number of integrated distributed generators. These results are further confirmed by studying a typical benchmark system such as the IEEE RTS-96. Such common trends indicate that some resilience strategies may enhance infrastructure system resilience in the short term, but if not managed well, they may compromise practical utility system resilience in the long run.

Browsing by Author "Duenas-Osorio, Leonardo"

Results Per Page

Sort Options