An application of the naïve Bayesian classifier for selecting strong
motion data in terms of the deformation probably induced on a given
structural system is presented. The main differences between the
proposed method and the “standard” procedure based on the inference of a
polynomial relationship between a single intensity measure and the
engineering demand parameter are: the discrete description of the
engineering demand parameter; the use of an array of intensity measures;
the combination of the information issued from the training phase via a
Bayesian formulation. Six non-linear structural systems with initial
fundamental frequency of 1, 2 and 5 Hz and with different strength
reduction factors are modelled. Their behaviour is described using the
Takeda hysteretic model and the engineering demand parameter is
expressed as the relative drift. A database of 6,373 strong motion
records is 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 system response is
reduced to a description of three possible classes: elastic, if the
induced drift is lower than the yield displacement; plastic, if the
drift ranges between the yield and the ultimate drift values; fragile if
the drift reaches the ultimate drift. The goal is to evaluate the
conditional probability of observing a given status of the system as a
function of the intensity measure 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 of
7,000 waveforms recorded in Japan, is performed. The Japanese data are
classed using the probability distribution functions derived on the
first data set. It is shown that, by combining several 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 intensity
measure and the engineering demand parameter controls the reliability of
the probability curves associated to each intensity measure.
