Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6\u27s states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6\u27s State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters

Uncertainty Quantification for Naval Ships and the Optimal Adaptation of Bridges to Climate Change

Repairing and adapting existing structures and infrastructure is essential for maintaining the functionality of a transportation network and the flow of people, goods, and ideas across a region. However, structures are vulnerable to extreme events, such as hurricanes and floods, and continuous deterioration, due to exposure to corrosive environments and cyclic loading. The occurrence of extreme events may be nonstationary over the service life of the structures, leading to uncertain future loading conditions on the structure. Continuous deterioration, due to corrosion or fatigue, changes the capacity of the structure to resist loads over time. Repair and adaptation measures may be applied to a structure in order to improve the capacity to resist loads. However, limited economic resources prohibit the immediate repair and adaptation of all structures, thus requiring a systematic methodology be established prioritizing actions. It is because of this need that the field of life-cycle management has emerged. The focus of the research in this dissertation is on enhancing this field and the ability of engineers to (1) quantify uncertainty in the life-cycle management problem, (2) assess the performance of structures and develop effective management strategies, and (3) integrate the uncertainties of climate changes and future loading conditions into the management of structures.Uncertainty quantification typically involves describing the variability in the loads acting on a structure, the capacity of the structure, and the deterioration over time of the structure. In the design phase, uncertainty quantification is based on observing loads in the area (traffic, wind, hydraulic loads, etc) and testing materials and connections to characterize their properties. In the operational phase, Structural Health Monitoring (SHM) data can be integrated into the uncertainty quantification process. This research specifically enhances the ability to integrate SHM data into the fatigue life prediction of ship structures and improve uncertainty quantification for naval ships.Life-cycle management integrates the quantifiable uncertainties into the performance assessment of a structure. For civil structures, hydraulic hazards like hurricanes, floods, and tsunamis may cause extensive damage; and failure may have major economic, societal, and environmental consequences. This research focuses on enhancing the performance assessment methodologies for evaluating the risk associated with the failure of riverine and coastal bridges once the uncertainties are known. The considerations for the multiple failure modes, as well as the multiple hazards, included in this research are shown to be essential when determining the risk level of bridges. Furthermore, this work includes proposed methodologies for determining optimal management strategies that are driven by both performance and cost in order to aid decision makers.The final thrust area of this research emanates from the uncertainties associated with anticipated climate changes. Natural and anthropogenic changes result in changes to sea level, the intensity of storms, and the intensity of precipitation which leave riverine and coastal bridges increasingly vulnerable. The uncertainties that govern the future variability in climate are currently reported as unquantifiable. This type of uncertainty is referred to as a deep uncertainty and stems from the multiple feasible projections for gas concentrations and the multiple available climate models with which to evaluate them. This research introduces a systematic decision support framework for determining adaptation strategies in the presence of both the deep uncertainties of climate change and the quantifiable uncertainties of structural performanc

Seismic fragility curves for a concrete bridge using structural health monitoring and digital twins

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategie

Post-earthquake Serviceability Assessment of RC Bridge Columns Using Computer Vision

Modern seismic design codes ensure a large displacement capacity and prevent total collapse for bridges. However, this performance objective is usually attained at the cost of damage to target ductile members. For reinforced concrete (RC) bridges, the columns are usually the main source of ductility during an earthquake in which concrete cover, core, and reinforcement may damage, and the column may experience a large permanent lateral deformation. A significant number of the US bridges will experience large earthquakes in the next 50 years that may result in the bridge closure due to excessive damage. A quick assessment of bridges immediately after severe events is needed to maximize serviceability and access to the affected sites, and to minimize casualties and costs. The main goal of this project was to accelerate post-earthquake RC bridge column assessment using \u201ccomputer vision\u201d. When sending trained personnel to the affect sites is limited or will take time, local personnel equipped with an assessment software (on various platforms such as mobile applications, cloud-based tools, or built-in with drones) can be deployed to evaluate the bridge condition. The project in this phase was focused on the damage assessment of modern RC bridge columns after earthquakes. Substandard columns, other bridge components, and other hazards were not included

Resilient Housing Design for Tsunami Prone Andaman and Nicobar Islands in India

Human settlement along coastal areas has grown dramatically over the last two decades. Unfortunately, coastal areas are prone to natural disasters caused by climate change and tectonic shifts underwater in the adjacent water bodies, resulting in loss of life and property. Tsunami is a form of natural disaster which occurs because of earthquakes under oceans and seas creating large waves which flood or wash out any coastal cities or islands located in the area impacted. In 2004 the Indian Ocean Tsunami had an enormous impact on the coastal cities in Southeast Asia causing loss of thousands of lives and making millions homeless. One region that bore the worst impacts of this tsunami was the Andaman and Nicobar Islands in India, located in the Bay of Bengal. Rehousing for those who lost their houses was the biggest post disaster issue as these islands were completely washed out. People built temporary housing until they moved to permanent houses, which took about two years to develop. The temporary houses built by the disaster stuck population were unsustainable. Being an island town, natural disasters such as tsunami due to tectonic shifts or rising water levels globally could reoccur bringing even more damage. This thesis addresses the development of a resilient house as a solution for preventing homelessness caused by coastal natural disasters and act as an informative guide for houses that are to be built to make them resilient to similar natural disasters. Resilient housing is a viable solution to reduce the loss of housing post a disaster and to protect human lives through the disaster. This proposed prototype design of the core and shell of the house which is resilient and based on the characteristics of the region is based on studying and analyzing existing research in the field of tsunami\u27s and their impact. This thesis takes into account the climate of the region and has features that ensure better comfort levels. The development of this thesis involved a study of Tsunami and its impacts, social aspects of the study area, relationship between structures and their resilience to Tsunami, the regulatory requirements of the government regarding incorporation of resilience in building design, a climate characterization of the region. This was followed by an analysis of the information gathered, based on which a prototype design of a house resilient to impacts of Tsunami has been proposed

Vulnerability Assessment of Coastal Bridges Subjected to Hurricane Events

Bridges are the most critical components of the transportation network. The functionality of bridges is important for hurricane aftermath recovery and emergency activities. However, past hurricane events revealed the potential susceptibility of these bridges under storm induced wave and surge loads. Coastal bridges traditionally were not designed to sustain hurricane induced wave and surge loads; and furthermore, no reliability assessment tool exists for bridges exposed to this hazard. However, such a tool is imperative for decision makers to evaluate the risk posed to the existing bridge inventory, and to decide on the retrofit measures and mitigation strategies. This dissertation offers a first attempt to quantify the structural vulnerability of bridges under coastal storms, offering a probabilistic framework, input tools, and application illustrations. To accomplish this goal, first an unbiased wave load model is developed based on the existing wave load models in the literature. The biased is removed from the load models through statistical analysis of the experimental test data. The developed wave load model is used to evaluate the response of coastal bridges employing single-physics domain Dynamic numerical models. Additionally, a high fidelity fluid-structure interaction model is developed to take into account the significant intricacies, such as turbulence, wave diffraction, and air entrapment, as well as material and geometric nonlinearities in structure. This numerical model provides insight on the influential parameters that affect the response of coastal bridges. Moreover, a Monte Carlo based Static Model methodology is developed to enable fast evaluation of the bridge deck unseating mode of failure. This methodology can be used for fast screening of vulnerable structures under hurricane induced wave and surge loads in a large bridge inventory. New statistical learning tools are used to develop fragility surfaces for coastal bridges vulnerable to storms. The performance of each of these tools is evaluated and compared. The statistical learning approaches are used to enable reliability assessment using the more rigorous finite element models such as the Dynamic and FSI Models which is important for improved confidence and retrofit assessment. Additionally, a new systematic method to evaluate the limit state capacity functions based on the post-event global performance of the bridge structure is developed. The application of the developed reliability models is illustrated by utilizing them for Houston/Galveston Bay area bridge inventory. The case study of Houston/Galveston Bay area reveals that more than 30% of bridges have a high probability of failure during an extreme hurricane scenario event. Two vulnerable bridge structures from the case study are selected to investigate the effect of different potential retrofit measures. Recommendations are made for the most appropriate retrofit measures that can prevent the deck unseating without significantly increasing the structural demands on other components

Multiscale Simulation and Assessment of the Seismic Resilience of Communities

Quantifying the seismic resilience of communities requires rigorous modeling of their behavior at disparate temporal (earthquakes – seconds vs. recovery – months) and spatial (component - meters vs. system - kilometers) scales. Hence, this dissertation has two main goals. The first one is to investigate the seismic behavior of components with heterogeneous scales in the community (i.e., member, building and community level studies) and further explore the effect of their behavior on the seismic resilience of communities over the relevant time scales. The second goal is to investigate the mutual interdependencies between the different systems of the community (i.e., engineering, social, etc.) during the disaster and the post-disaster recovery stages. On the member level, measurements obtained from a 3D noncontact laser scanning technique are used to quantify the initial geometric imperfections of steel W-shape members. Based on the measured imperfections, a spectral approach that models the imperfections in each plate of the W-shape member as a 2D field of random vibrations is proposed. It is shown that although geometric imperfections can, in certain situations, influence column buckling behavior, their effect on nonlinear cyclic behavior is generally small and inconsistent. The capabilities of different machine learning classification and regression methods in predicting the seismic collapse behavior of deep steel W-shape columns in SMFs are explored. A dataset of more than nine hundred experimental and numerical results of deep steel W-shape columns with different attributes is assembled. The results suggest that machine learning algorithms that are continually updated with new experimental and computational data could inform future generations of design specifications. The seismic collapse behavior of SMF hollow structural steel (HSS) columns under combined axial and drift loading is computationally studied through a validated finite element model. The simulation results are used to propose slenderness limits and design guidelines that incorporate key variables identified in the research to permit HSS columns to achieve highly ductile behavior. On the building level, the extent of debris generation around collapsed reinforced concrete moment frame (RCMRF) buildings is characterized using a validated computational approach. A set of RC moment resisting frame structures with different heights is modeled under different ground motion records scaled up until they induce collapse of the building to assess the seismic debris field under different ground motion histories and building heights. The effect of building code requirements on debris field extent is also investigated. On the community level, a scalable model that employs a simulation-based dynamic analysis, which models the behavior of the community at each time step as the seismic event occurs (time step in seconds) and as the community recovers after the event (time step in days) is developed. The developed model is employed to simulate the mutual interdependencies between the building portfolio, transportation network, and healthcare system in the community as well as to integrate post-earthquake household decision making when quantifying the seismic resilience of communities subjected to earthquake sequences. Incremental dynamic analysis (IDA) is used to develop fragility curves for mainshock-damaged structures, which are distinguished from the conventional fragility curves of intact structures. The capabilities of the developed models to support hazard mitigation planning are demonstrated through various case studies that highlight the effects of interdependencies between the different systems under consideration. Mitigation strategies to improve seismic resilience of the prototype communities are also proposed and assessed.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167910/1/osediek_1.pd