Prediction of ground motion parameters using randomized ANFIS (RANFIS) |
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| Affiliation: | 1. Department of Earthquake Engineering, Indian Institute of Technology, Roorkee, India;2. Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India;1. University Grenoble Alpes, ISTerre, CNRS, IRD, IFSTTAR, Grenoble, France;2. CEA, French Alternative Energies and Atomic Energy Commission, DEN, Saint Paul lez Durance, France;3. Faculty of Math., Physics and Informatics, Comenius University, Bratislava, Slovakia;4. Earth Science Institute, Slovak Academy of Sciences, Bratislava, Slovakia;5. Bureau de Recherches Géologiques et Minières, Orléans, France;6. ITSAK, Institute of Engineering Seismology and Earthquake Engineering, Thessaloniki, Greece;7. Aristotle University of Thessaloniki, Thessaloniki, Greece;8. Formerly CEA, Cadarache, France;1. Department of Structural Mechanics and Hydraulic Engineering, University of Granada, Spain;2. Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Italy;3. Department of Mechanical Engineering, ETSII, Polytechnic University of Madrid, Spain |
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| Abstract: | In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The three time domain ground motion parameters which are predicted by the model are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using the database released by PEER (Pacific Earthquake Engineering Research Center). Each ground motion parameter is related to mainly to four seismic parameters, namely earthquake magnitude, faulting mechanism, source to site distance and average soil shear wave velocity. The experimental results validate the improved performance of the machine, with lesser computation time compared to prior studies. |
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| Keywords: | ANFIS Random weight vector Prediction Ground motion parameter |
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