Application of Artificial Neural Network for Prediction of Story Drift in High-Rise Buildings

Abstract

The difficulties arising from growing population expansion and extensive urbanization are driving up the demand for high-rise structures. Comprehending the symmetry and composition of structural components correspondingly is becoming more and more important in light of current developments in computer technology, novel materials, and unique structures. The structural responses of multistory buildings predicted by Artificial Neural Networks (ANN) briefly outlined in a systematic manner. This review enhances the application of ANN for the prediction of story drift in multistory buildings with greater level accuracy. A multistory structure might fall because of the intense movement of the ground, resulting in fatalities and economic loss. As a result, it is imperative that the multistory structure be appropriately constructed to minimize the earthquake hazards. Based on a literature analysis, it can be concluded that artificial neural networks are widely used in simulating and predicting story drift in high-rise building systems. The objective of the current investigation is to present a comprehensive overview of the many approaches and uses of artificial neural network modeling research in the domain of structural engineering. Accordingly, the articles were categorized in this way based on author, year, algorithm used, software application and description of findings and conclusions. Here, the most influential parameter (input vector) is also rigorously reviewed. This information may be taken into account when creating a neural network model that operates more effectively. The review's conclusions might prove beneficial for structural and/or civil engineering problems. The approaches described here assist the structural practitioner in understanding the limitations and advantages of ANN in comparison to other traditional mathematical modelling. This review contributes a body of knowledge for the ANN modelling approach to forecast the story drift evaluation within the field of structural engineering.