A non-parametric wave type based model for real-time prediction of strong ground motion accelerogram |
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| Institution: | 1. Institute of Construction Informatics, Technical University of Dresden, Dresden, Sachsen 01062, Germany;2. International Institute of Earthquake Engineering and Seismology, Tehran, Iran;1. Indian Institute of Technology Hyderabad 502285, India;2. IcfaiTech, IFHE, Hyderabad 502285, India;3. 2820 Prestwood Drive, Cumming, GA 30040, USA;4. Research Data Scientist, Mangalathu, Mylamkulam, Puthoor P O, Kollam, Kerala 691507, India;1. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China;2. Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China;1. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran;2. School of Civil Engineering, University of Tehran, Tehran, Iran;3. Department of Civil and Architectural Engineering, Qatar University, Doha, Qatar;4. Department of Civil Engineering and Energy Technology, Oslo Metropolitan University, Oslo, Norway;5. Faculty of Engineering & IT, University of Technology Sydney, Australia |
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| Abstract: | A wave type based method for real-time prediction of strong ground motion (SGM) accelerogram is developed. Real-time prediction of SGM is requested in predictive building control systems to trigger and control actuator systems achieving the goal of reduction of the structural deformations during an on-going earthquake. It is well known that SGM is a classic example of non-stationary stochastic process with temporal variation of both amplitude and frequency content. The developed non-parametric model considers the non-homogeneity of the seismic process which contains different wave types with the individual frequency contents and time-dependency amplitude distribution pattern. Therefore, an important part of the method is to detect dominant seismic wave phases. Prediction of seismic signal is undertaken by applying frequency adaptive windowing approach, which leads to predict the on-coming signal in time window t+Δt based on the measured data in the time window t. Besides use of the frequency adaptive windowing, constant windowing and semi-adaptive windowing approaches are deployed. The results show that use of the adaptive time windows relevant to dominant frequency of the signal will enable the model to catch and predict the most dominant frequencies. Performance of the proposed model is verified by the use of 97 free-field accelerograms, which were applied to train and validate the prediction model. The selected accelerograms were measured above the soil type C and D according Eurocode 8 and their Moment magnitude are ranging between 6.2 and 7.7. The learning capability of the radial basis function Artificial Neural Network is used to reconstruct the SGM accelerogram. The most significant advantage of the proposed model is the concept of wave type based modeling which has the advantage of a conceptual physical modeling of the seismic process. Comparison of the real-time predicted and the observed accelerograms shows a high correlation when the frequency adaptive approach is applied. This paper lays a foundation for more effective use of real-time predictive control systems and potential for future extension in active structural control as well as in real-time seismology. |
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