Classification of Buildings' Potential for Seismic Damage by Means of Artificial Intelligence Techniques

• URL: http://arxiv.org/abs/2205.01076v1
• Date: Wed, 6 Apr 2022 15:32:15 GMT
• Title: Classification of Buildings' Potential for Seismic Damage by Means of Artificial Intelligence Techniques
• Authors: Konstantinos Kostinakis, Konstantinos Morfidis, Konstantinos Demertzis, Lazaros Iliadis
• Abstract summary: A large training dataset is used for the implementation of the classification algorithms. The level of the seismic damage is expressed in terms of the Maximum Interstory Drift Ratio. The most significant conclusion which is extracted is that the Machine Learning methods can be used to solve some of the most sophisticated real-world problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Developing a rapid, but also reliable and efficient, method for classifying the seismic damage potential of buildings constructed in countries with regions of high seismicity is always at the forefront of modern scientific research. Such a technique would be essential for estimating the pre-seismic vulnerability of the buildings, so that the authorities will be able to develop earthquake safety plans for seismic rehabilitation of the highly earthquake-susceptible structures. In the last decades, several researchers have proposed such procedures, some of which were adopted by seismic code guidelines. These procedures usually utilize methods based either on simple calculations or on the application of statistics theory. Recently, the increase of the computers' power has led to the development of modern statistical methods based on the adoption of Machine Learning algorithms. These methods have been shown to be useful for predicting seismic performance and classifying structural damage level by means of extracting patterns from data collected via various sources. A large training dataset is used for the implementation of the classification algorithms. To this end, 90 3D R/C buildings with three different masonry infills' distributions are analysed utilizing Nonlinear Time History Analysis method for 65 real seismic records. The level of the seismic damage is expressed in terms of the Maximum Interstory Drift Ratio. A large number of Machine Learning algorithms is utilized in order to estimate the buildings' damage response. The most significant conclusion which is extracted is that the Machine Learning methods that are mathematically well-established and their operations that are clearly interpretable step by step can be used to solve some of the most sophisticated real-world problems in consideration with high accuracy.

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