Abstract
The prediction of the nonlinear seismic demand for a given hazard level is still a challenging task for seismic risk assessment. This paper presents a Ground Motion Prediction Model (GMPE) for efficient estimation of the inelastic response spectra of 5% damped Single Degree of Freedom (SDOF) systems, with Elastic-Perfectly-Plastic hysteretic behavior in terms of seismological parameters and structural properties. The model was developed using an Artificial Neural Network (ANN) with Back-Propagation (BP) learning algorithm, by means of 200 records collected from KiK-Net database. The proposed model outputs an inelastic response spectra expressed by a 21 values of displacement amplitudes for an input set composed of three earthquake parameters; moment magnitude, depth and source-to-site distance; one site parameter, the shear wave velocity; and one structural parameter, the strength-reduction factor. The performance of the neural network model shows a good agreement between the predicted and computed values of the inelastic response spectra. As revealed by a sensitivity analysis, the seismological parameters have almost the same influence on the inelastic response spectra, only the depth which shows a reduced impact. The advantage of the proposed model is that it does not require an auxiliary elastic GMPE, which makes it easy to be implemented in Probabilistic Seismic Hazard Analysis (PSHA) methodology to generate probabilistic hazard for the inelastic response.
Highlights
Predicting the seismic demand for a given hazard level is a critical part in both seismic design and seismic evaluation of existing structures
Several researchers have proposed the peak inelastic displacement (Sdi) as an adequate option to assess the seismic demand. This led to simplified seismic analysis procedures that are based on the peak inelastic displacement of a Single Degree of Freedom (SDOF) system with a bilinear hysteresis behavior, having a period equal to the fundamental period of the structure, and a lateral strength determined via a pushover analysis [1]
Most of the ground motion prediction models of inelastic response spectra are based on regression analysis, whereas, the objective of this work is to predict the inelastic response spectrum and analyze the effects of seismological parameters using feed forward artificial neural network (ANN) with a gradient back-propagation rule for the training
Summary
Predicting the seismic demand for a given hazard level is a critical part in both seismic design and seismic evaluation of existing structures. Several researchers have proposed the peak inelastic displacement (Sdi) as an adequate option to assess the seismic demand. This led to simplified seismic analysis procedures that are based on the peak inelastic displacement of a Single Degree of Freedom (SDOF) system with a bilinear hysteresis behavior, having a period equal to the fundamental period of the structure, and a lateral strength determined via a pushover analysis [1]. Well-known methods such as the Capacity Spectrum Method (CSM) are based on superimposing the seismic capacity over the corresponding seismic demand for a given hazard level to determine the expected response of the structure. The capacity curve relies on the use of nonlinear static analysis (pushover method) while the seismic demand is a representation of the earthquake ground motion, generally it is obtained directly by time-history analyses of inelastic SDOF systems, or indirectly from elastic spectra [2].
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