A hybrid computational intelligence approach to predict spectral acceleration. (May 2019)

• Record Type: Journal Article
• Authors: Akhani, Mohsen; Kashani, Ali R.; Mousavi, Mehdi; Gandomi, Amir H.
• Is Part Of: Measurement. Volume 138(2019)
• Page Start: 578
• Page End: 589
• Publication Date: 2019-05
• Subjects: Artificial neural network -- Genetic algorithm -- Regression analysis -- Spectral acceleration -- Ground motion models; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.02.054
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An AI-based approach is developed to predict spectral acceleration. Genetic algorithm, artificial neural network, and regression analysis are hybridized. Comprehensive experiments confirm a satisfying conformity to the real observations. The proposed approach outperforms other neural network and regression analysis. Abstract: In this study, a new explicit method has been suggested to predict the spectral acceleration characteristic of strong ground-motions based on hybridizing genetic algorithm (GA), multilayer perceptron neural network (MLPNN), and regression analysis (RA), called GA-NN-RA. The predictor variables encompass a period of vibration, magnitude, closest distance co-seismic rupture, shear wave velocity averaged over the top 30 m and flag for reverse faulting earthquakes. To develop the model, a data set of strong ground-motion records gathered by Pacific Earthquake Engineering Research Center has been employed. For confirmation and efficiency of proposed model, an additional set of test that is not involved in the modeling has been applied. The obtained results using GA-NN-RA show good accuracy in comparison with other ground motion models. Also, the proposed model is capable of evaluating the spectral acceleration for any records without restriction in a period of vibration.