Abstract:A fundamental issue that arises in the framework of probabilistic seismic risk analysis is the choice of ground motion intensity measures (IMs). A new structure-specific IM, namely, relative average spectral acceleration (ASA R ), is being proposed herein, and a comparison with current IMs is performed based on (1) a large data set of recorded earthquake signals;(2) numerical analyses conducted with state-of-the-art finite element (FE) models, representing actual load-bearing walls and frame structures, and va… Show more
“…In conclusion, taking into account multiple IM leads to a better description of the complexity of the seismic signals; furthermore, since the structural response evolves as a function of plastic hinge formation, a single IM is not always able to follow this evolution describing all the complexity of the structural demand. That is why the results presented in this paper can be improved using more specific IM defined to better relate the input ground motion and the structural response (De Biasio et al 2014;Lestuzzi et al 2004). In this sense, the naïve Bayesian classifier is conceived as a tool able to collect and fully exploit the published results on the best IMs as a function of the structural response.…”
Section: Discussionmentioning
confidence: 91%
“…This is not surprising, since these parameters describe the spectral velocity or acceleration content averaged over two frequencies, taking into account the fundamental frequency degradation related to the appearance of plastic hinges or cracks in the structures. Furthermore, several authors investigated the best way of defining the frequency integration range for pseudo-displacement/velocity and acceleration spectra, in order to customize the IM parameter as a function of the investigated structure properties (De Biasio et al 2014;Lestuzzi et al 2004).…”
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 123 Bull Earthquake Eng 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.
“…In conclusion, taking into account multiple IM leads to a better description of the complexity of the seismic signals; furthermore, since the structural response evolves as a function of plastic hinge formation, a single IM is not always able to follow this evolution describing all the complexity of the structural demand. That is why the results presented in this paper can be improved using more specific IM defined to better relate the input ground motion and the structural response (De Biasio et al 2014;Lestuzzi et al 2004). In this sense, the naïve Bayesian classifier is conceived as a tool able to collect and fully exploit the published results on the best IMs as a function of the structural response.…”
Section: Discussionmentioning
confidence: 91%
“…This is not surprising, since these parameters describe the spectral velocity or acceleration content averaged over two frequencies, taking into account the fundamental frequency degradation related to the appearance of plastic hinges or cracks in the structures. Furthermore, several authors investigated the best way of defining the frequency integration range for pseudo-displacement/velocity and acceleration spectra, in order to customize the IM parameter as a function of the investigated structure properties (De Biasio et al 2014;Lestuzzi et al 2004).…”
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 123 Bull Earthquake Eng 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.
“…The relative average-spectral-acceleration (ASA r ) is a new intensity measure presented by De Biasio et al (2014). Its main advantage over SA is its efficiency as an intensity measure appropriate for the structures that behave non-linearly.…”
Section: Introductionmentioning
confidence: 99%
“…It takes into account the lengthening of the fundamental period due to progressive loss of stiffness caused by irreversible damage processes. The optimum value of frequency drop, R, of the structure was chosen as 40 % by De Biasio et al (2014). The prediction of the new intensity measure ASA 40 using probabilistic seismic hazard analysis (PSHA) would enforce its sufficiency as a robust intensity measure for the analysis of non-linearly behaving structures.…”
Section: Introductionmentioning
confidence: 99%
“…However, in the case of inelastic structural behaviour, SA does not take into account the contribution of higher modes to the overall dynamic response or the lengthening of the fundamental period due to progressive loss of stiffness caused by irreversible damage processes. A structure-specific intensity measure has been developed by De Biasio et al (2014) in order to consider the lengthening of the fundamental period of the structure. The new intensity measure is named relative average spectral pseudo-acceleration (ASA r ).…”
27Relative Average-Spectral-Acceleration (ASA40), a recently developed intensity 28 measure, is defined as the average spectral pseudo-acceleration on the probable 29 interval of evolution of the fundamental frequency of a structure. This article presents 30 two ground motion prediction equations (GMPEs) appropriate for the prediction of
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.