Date of Award
Master of Engineering (ME)
Pang , Weichiang
Structural systems are subject to inherent uncertainties due to the variability in many hard-to-control `noise factors' that include but are not limited to external loads, material properties, and construction workmanship. Two design methodologies have been widely accepted in the practicing engineering realm to manage the variability associated with operational structures: Allowable Stress Design (ASD) and Load and Resistance Factor Design (LRFD). These traditional approaches explicitly recognize the presence of uncertainty; however, they do not take robustness against this uncertainty into consideration. Overlooking this robustness against uncertainty in the structural design process has two drawbacks. First, the design may not satisfy the safety requirements if the actual uncertainties in the noise factors are underestimated. Thus, the safety requirements can easily be violated because of the high variation of the system response due to noise factors. Second, to guarantee safety in the presence of this high variability of the system response, the structural designer may be forced to choose an overly conservative, inefficient and thus costly design. When the robustness against uncertainty is not treated as one of the design objectives, this trade-off between the over-design for safety and the under-design for cost-savings is exacerbated. The second chapter of this thesis demonstrates that safe and cost-effective designs can be achieved by implementing Robust Design concepts originally developed in manufacturing engineering to consider the robustness against uncertainty. Robust Design concepts can be used to formulate structural designs, which are insensitive to inherent variability in the design process, thus saving cost, and exceeding the main objectives of safety and serviceability. The second chapter of this thesis presents two methodologies for the application of Robust Design principles to structural design utilizing two optimization schemes: one-at-a-time optimization method and Particle Swarm Optimization (PSO) method.
Next, this multi-disciplinary research project introduces a methodology to build a new framework, Structural Life-Cycle Assessment (S-LCA), for quantifying the structural sustainability and resiliency of built systems. This project brings together techniques and concepts from two distinct disciplines: Structural Health Monitoring (SHM) of Civil Engineering and Life Cycle Assessment (LCA) of Environmental Engineering to construct the aforementioned S-LCA charts. The intellectual innovations of this project lie in the advancement in infrastructure management techniques through the development of S-LCA charts, which can be useful as an infrastructure monitoring and decision-making tool, for quantifying the structural sustainability and resiliency of built systems. Such a tool would be of great use in aiding infrastructure managers when prescribing maintenance and repair schemes, and emergency managers and first responders in allocating disaster relief effort resources. Moreover, a quantitative, real-time evaluation of structural damage after a disaster will support emergency managers in resource allocation. The project integrates science based modeling and simulation techniques with advanced monitoring and sensing tools, resulting in scientifically defendable, objective and quantitative metrics of sustainability and resiliency to be used in infrastructure management.
Dalton, Sarah, "Robust Design and Monitoring Tools for Sustainable and Resilient Structural Design and Infrastructure Management" (2011). All Theses. 1260.