Performance-Based Seismic Design and Assessment of Bridges

Current trends in the seismic design and assessment of bridges are discussed, with emphasis on two procedures that merit some particular attention, displacement-based procedures and deformation-based procedures. The available performance-based methods for bridges are critically reviewed and a number of critical issues are identified, which arise in all procedures. Then two recently proposed methods are presented in some detail, one based on the direct displacement-based design approach, using equivalent elastic analysis and properly reduced displacement spectra, and one based on the deformation-based approach, which involves a type of partially inelastic response-history analysis for a set of ground motions and wherein pier ductility is included as a design parameter, along with displacement criteria. The current trends in seismic assessment of bridges are then summarised and the more rigorous assessment procedure, i.e. nonlinear dynamic response-history analysis, is used to assess the performance of bridges designed to the previously described procedures. Finally some comments are offered on the feasibility of including such methods in the new generation of bridge codes

Strategy for the selection of input ground motion for inelastic structural response analysis based on naïve Bayesian classifier

International audienceAn application of the naïve Bayesian classifier for selecting strong motion datain terms of the deformation probably induced on a given structural system is presented. Themain differences between the proposed method and the “standard” procedure based on theinference of a polynomial relationship between a single intensity measure and the engineeringdemand parameter are: the discrete description of the engineering demand parameter; theuse of an array of intensity measures; the combination of the information issued from thetraining phase via a Bayesian formulation. Six non-linear structural systems with initialfundamental frequency of 1, 2 and 5 Hz and with different strength reduction factors aremodelled. Their behaviour is described using the Takeda hysteretic model and the engineeringdemand parameter is expressed as the relative drift. A database of 6,373 strong motion recordsis built from worldwide catalogues and is described by a set of “classical” intensity measures;it constitutes the “training dataset” used to feed the Bayesian classifier. The structural systemresponse is reduced to a description of three possible classes: elastic, if the induced driftis lower than the yield displacement; plastic, if the drift ranges between the yield and theultimate drift values; fragile if the drift reaches the ultimate drift. The goal is to evaluate theconditional probability of observing a given status of the system as a function of the intensitymeasure array. To validate the presented methodology and evaluate its prediction capability,a blind test on a second dataset, completely disjointed from the training one, composed of7,000 waveforms recorded in Japan, is performed. The Japanese data are classed using theprobability distribution functions derived on the first data set. It is shown that, by combiningseveral intensity measures through the likelihood product, a stable result is obtained whereby most of the data (>75 %) are well classed. The degree of correlation between the intensitymeasure and the engineering demand parameter controls the reliability of the probabilitycurves associated to each intensity measure

Generation of accelerograms compatible with design specifications using information theory

International audienceThis paper deals with the generation of seismic accelerograms which are compatible with a given response spectrum and other design specifications. The time sampling of the stochastic accelerogram yields a time series represented by a random vector in high dimension. The probability density function of this random vector is constructed using the Maximum Entropy (MaxEnt) principle under constraints defined by the available information (design specifications). An adapted algorithm is proposed to identify the Lagrange multipliers introduced in the MaxEnt principle to take into account the constraints. This algorithm is based on (1) the minimization of an appropriate convex functional and (2) the construction of the probability distribution defined as the invariant measure of an Itˆo Stochastic Differential Equation in order to estimate the integrals in high dimension of the problem. The constraints related to a seismic ccelerogram are developed explicitly