Probabilistic Approach for Design Optimization of Prestressed Girder Bridges Using Multi-Purpose Computer-Aided Model

ii 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