首页> 中文期刊> 《工程与科学中的计算机建模(英文)》 >Probabilistic Performance-Based Optimum Seismic Design Framework: Illustration and Validation

Probabilistic Performance-Based Optimum Seismic Design Framework: Illustration and Validation

         

摘要

In the field of earthquake engineering,the advent of the performance-based design philosophy,together with the highly uncertain nature of earthquake ground excitations to structures,has brought probabilistic performance-based design to the forefront of seismic design.In order to design structures that explicitly satisfy probabilistic performance criteria,a probabilistic performance-based optimum seismic design(PPBOSD)framework is proposed in this paper by extending the state-of-the-art performance-based earthquake engineering(PBEE)methodology.PBEE is traditionally used for risk evaluation of existing or newly designed structural systems,thus referred to herein as forward PBEE analysis.In contrast,its use for design purposes is limited because design is essentially a more challenging inverse problem.To address this challenge,a decision-making layer is wrapped around the forward PBEE analysis procedure for computer-aided optimum structural design/retrofit accounting for various sources of uncertainty.In this paper,the framework is illustrated and validated using a proof-of-concept problem,namely tuning a simplified nonlinear inelastic single-degreeof-freedom(SDOF)model of a bridge to achieve a target probabilistic loss hazard curve.For this purpose,first the forward PBEE analysis is presented in conjunction with the multilayer Monte Carlo simulation method to estimate the total loss hazard curve efficiently,followed by a sensitivity study to investigate the effects of system(design)parameters on the probabilistic seismic performance of the bridge.The proposed PPBOSD framework is validated by successfully tuning the system parameters of the structure rated for a target probabilistic seismic loss hazard curve.The PPBOSD framework provides a tool that is essential to develop,calibrate and validate simplified probabilistic performance-based design procedures.

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