Application and development of Artificial Neural Networks to predict structural damage subjected to earthquake-A review

Abstract

Abstract The complexity of earthquake incidents supports efforts to use machine learning methods as an alternative to conventional methods because they can simply capture difficult relations between the input and output parameters without requiring a particular functional structure. Machine learning is a branch of artificial intelligence techniques that applies the capacity of machines to recreate intelligent human behavior and has been receiving increasing consideration as an effective solution for damage detection due to earthquakes. Artificial neural networks (ANNs) are one of the most important machine techniques, inspired by the human brain. There hasn’t been a lot of published research on the use of ANNs for structural damage identification to predict earthquakes on various structures up until now. This paper presents a comprehensive review of recent studies on the applications and development of ANNs to predict earthquakes and damage caused by earthquakes on different structures. ANNs can learn from their experience and have drawn significant consideration in damage detection due to their capability of pattern identification and error tolerance in forming a nonlinear modeling between the inputs and outputs. According to this research, the use of ANNs can prevent unpredictable failures and reduce maintenance costs, increasing the safety and functionality of structures. A brief introduction to the ANN algorithm is presented first. Following that, the benefits, and limitations of ANNs, as well as some new research trends, have been discussed.