Modeling peak ground acceleration for earthquake hazard safety evaluation – ScienceOpen
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      Modeling peak ground acceleration for earthquake hazard safety evaluation

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          Abstract

          This paper presents a ground motion prediction (GMP) model using an artificial neural network (ANN) for shallow earthquakes, aimed at improving earthquake hazard safety evaluation. The proposed model leverages essential input variables such as moment magnitude, fault type, epicentral distance, and soil type, with the output variable being peak ground acceleration (PGA) at 5% damping. To develop this model, 885 data pairs were obtained from the Pacific Engineering Research Center, providing a robust dataset for training and validation. The ANN architecture comprises 4 nodes in the input layer, two hidden layers each containing 25 nodes, and a single-node output layer, resulting in 750 unknown weight and bias values that the model must optimize. Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. This integration aimed to forecast 20 potential earthquake scenarios, a crucial step in validating the model’s effectiveness. The results were promising, as the ANN-GA successfully predicted earthquake occurrences in 15 out of 20 scenarios. These findings underscore the model’s potential in accurately forecasting seismic events, thereby contributing to the development of more resilient infrastructure and better-informed urban planning strategies.

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          Most cited references14

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          Genetic Algorithms

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            NGA-West2 Ground Motion Model for the Average Horizontal Components of PGA, PGV, and 5% Damped Linear Acceleration Response Spectra

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              Ground motion prediction equations derived from the Italian strong motion database

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                Author and article information

                Contributors
                fatimak@neduet.edu.pk
                Milad.razbin@aut.ac.ir
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 December 2024
                28 December 2024
                2024
                : 14
                : 31032
                Affiliations
                [1 ]Department of Civil Engineering, NED University of Engineering and Technology, ( https://ror.org/05db8zr24) Karachi, Pakistan
                [2 ]Department of Textile Engineering, Amirkabir University of Technology, ( https://ror.org/04gzbav43) Tehran, Iran
                [3 ]School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, ( https://ror.org/0384j8v12) Sydney, NSW 2006 Australia
                Article
                82171
                10.1038/s41598-024-82171-7
                11680787
                39730692
                871c1e34-9d33-47c1-a595-5839a4acf3f0
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 June 2024
                : 3 December 2024
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                © Springer Nature Limited 2024

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                earthquake,peak ground acceleration,artificial neural network,genetic algorithm,natural hazards,civil engineering,computational science

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