Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Seismic Resilience-based Design and Optimization: A Deep Learning and Cyber-Physical Approach(2018) Wu, Jingzhe; Phillips, Brian M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the growing acceptance and better understanding of the importance of uncertainties in seismic design, traditional design approaches with deterministic analysis are being replaced with more reliable approaches within a risk-based context. Recently, resilience has been increasingly studied as a comprehensive metric to assess the ability of a system to withstand and recover from disturbances with large uncertainties. For civil infrastructure systems susceptible to natural hazards, especially earthquakes as considered herein, seismic resilience could provide a measurement integrating both earthquake and post-earthquake performance. For structural engineers, improving infrastructure disaster resilience starts with the design of more resilient structures. This requires a quantitative approach to explicitly guild the design towards better resilience. However, when attempting to quantify the seismic resilience of a structure, large uncertainties lead to large computational costs associated with risk-based approaches. Additionally, the accuracy of numerical simulations under wide range of design scenarios is unknown. To address these challenges, this dissertation investigates the role of seismic resilience in structural design. This dissertation starts with a novel seismic protective device to improve structural resilience and follows with the development of a quantitative and efficient design, evaluation, and optimization framework for seismic resilience. This framework proposes metamodeling through deep neural networks for improved efficiency and cyber-physical systems for improved accuracy. Feedforward neural networks are adopted for fragility metamodeling, while online learning long-short term memory neural networks are developed for structural component metamodeling. Real-time hybrid simulation is used for the construction of cyber-physical systems. The proposed framework is demonstrated to have both improved accuracy and significantly reduced computational/experimental cost when compared to existing approaches. The applicability of the framework is illustrated through the optimization of structural systems for improved seismic resilience.Item Structural performance evaluation and optimization through cyber-physical systems using substructure real-time hybrid simulation(2017) Zhang, Ruiyang; Phillips, Brian M; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Natural hazards continue to demonstrate the vulnerability of civil infrastructure worldwide. Engineers are dedicated to improving structural performance against natural hazards with improved design codes and computational tools. These improvements are often driven by experiments. Experimental testing not only enables the prediction of structural responses under those dynamic loads but also provide a reliable way to investigate new solutions for hazard mitigation. Common experimental techniques in structural engineering include quasi-static testing, shake table testing, and hybrid simulation. In recent years, real-time hybrid simulation (RTHS) has emerged as a powerful alternative to drive improvements in civil infrastructure as the entire structure’s dynamic performance is captured with reduced experimental requirements. In addition, RTHS provides an attractive opportunity to investigate the optimal performance of complex structures or components against multi-hazards by embedding it in an optimization framework. RTHS stands to accelerate advancements in civil engineering, in particular for designing new structural systems or devices in a performance-based design environment. This dissertation focuses on the use of cyber-physical systems (CPS) to evaluate structural performance and achieve optimal designs for seismic protection. This dissertation presents systematic studies on the development and validation of the dynamic substructuring RTHS technique using shake tables, novel techniques in increasing RTHS stability by introducing artificial damping to an under-actuated physical specimen, and the optimal design of the structure or supplemental control devices for seismic protection through a cyber-physical substructure optimization (CPSO) framework using substructure RTHS.