|dc.description.abstract||Prestressed girder bridges are a very common type of bridges constructed all over the world. The girder bridges are ideal as short to medium spans (15 m to 60 m) structures, due to their moderate self-weight, structural efficiency, ease of fabrication, fast construction, low initial cost, long life expectancy, low maintenance, simple deck removal, and replacement process. Thus, the vast applicability of prestressed girder bridges provides the motivation to develop optimization methodologies, techniques, and models to optimize the design of these widely-used types of bridges, in order to achieve cost-effective design solutions.
Most real-world structural engineering problems involve several elements of uncertainty (e.g. uncertainty in loading conditions, in material characteristics, in analysis/simulation model accuracy, in geometric properties, in manufacturing precision, etc). Such uncertainties need to be taken into consideration in the design process in order to achieve uniform levels of safety and consistent reliability in the structural systems. Consideration of uncertainties and variation of design parameters is made through probabilistic calibration of the design codes and specifications. For all current bridge design codes (e.g. AASHTO LRFD, CHBDC, or European code) no calibration is yet made to the Serviceability Limit State or Fatigue Limit State. Eventually, to date only Strength I limit state has been formally calibrated with reliability basis.
Optimum designs developed without consideration of uncertainty associated with the design parameters can lead to non-robust designs, ones for which even slight changes in design variables and uncertain parameters can result in substantial performance degradation and localized damages. The accumulated damage may result in serviceability limitations or even collapse, although the structural design meets all code requirements for ultimate flexural and shear capacity.
In order to search for the best optimization solution between cost reduction and satisfactory safety levels, probabilistic approaches of design optimization were applied to control the structural uncertainties throughout the design process, which cannot be achieved by deterministic optimization. To perform probabilistic design optimization, the basic design parameters were treated as random variables. For each random variable, the statistical distribution type was properly defined and the statistical parameters were accurately derived. After characterizing the random variables, in the current research, all the limit state functions were formulated and a comprehensive reliability analysis has been conducted to evaluate the bridge’s safely level (reliability index) with respect to every design limit state. For that purpose, a computer-aided model has been developed using Visual Basic Application (VBA). The probabilities of failure and corresponding reliability indexes determined by using the newly developed model, with respect to limit state functions considered, were obtained by the First-Order Reliability Method (FORM) and/or by Monte Carlo Simulation MCS technique. For the overall structural system reliability, a comprehensive Failure Mode Analysis (FMA) has been conducted to determine the failure probability with respect to each possible mode of failure. The Improved Reliability Bounds (IRB) method was applied to obtain the upper and lower bounds of the system reliability.
The proposed model also provides two methods of probabilistic design optimization. In the first method, a reliability-based design optimization of prestressed girder bridges has been formulated and developed, in which the calculated failure probabilities and corresponding reliability indexes have been treated as probabilistic constraints. The second method provides a quality-controlled optimization approach applied to the design of prestressed girder bridges where the Six Sigma quality concept has been utilized. For both methods, the proposed model conducts simulation-based optimization technique. The simulation engine performs Monte Carlo Simulation while the optimization engine performs metaheuristic scatter search with neural network accelerator.
The feasibility of any bridge design is very sensitive to the bridge superstructure type. Failing to choose the most suitable bridge type will never help achieving cost-effective design alternatives. In addition to the span length, many other factors (e.g. client’s requirements, design requirements, project’s conditions, etc.) affect the selection of bridge type. The current research focusses on prestressed girder bridge type. However, in order to verify whether selecting the prestressed girder bridge type, in a specific project, is the right choice, a tool for selecting the optimum bridge type was needed. Hence, the current research provides a new model for selecting the most suitable bridge type, by integrating the experts’ decision analysis, decision tree analysis and sensitivity analysis. Experts’ opinions and decisions form essential tool in developing decision-making models. However the uncertainties associated with expert’s decisions need to be properly incorporated and statistically modelled. This was uniquely addressed in the current study.|
|dc.publisher||Université d'Ottawa / University of Ottawa|
|dc.title||Probabilistic Approach for Design Optimization of Prestressed Girder Bridges Using Multi-Purpose Computer-Aided Model|
|thesis.degree.discipline||Génie / Engineering|
|uottawa.department||Génie civil / Civil Engineering|
|Collection||Thèses, 2011 - // Theses, 2011 -|