Performance-based design and seismic reliability analysis using designed experiments and neural networks

Seismic design involves many uncertainties that arise from the earthquake motions, structural geometries, material properties, and analytical models. Taking into account all major uncertainties, reliability analysis is applied to estimate probability of failure in each of a set of performance requirements. The probability estimation is best conducted through Monte Carlo simulations with variance reduction techniques. However, this may involve many performance function evaluations, each requiring a non-linear dynamic analysis, which may be very computationally demanding. In order to improve computational efficiency, this paper explores Design of Computer Experiments and Neural Networks for representation of structural behavior. The neural networks are directly employed for reliability assessment and design optimization. Performance-based seismic design is formulated as an optimization problem, with design parameters optimally calculated. Two case studies are presented to demonstrate efficiency and applicability of the methodology: a bridge bent with or without seismic isolation and a steel pipe pile foundation.