PERFORMANCE BASED DESIGN OF CIRCULAR SCRAP TYRE PAD ISOLATOR BY MACHINE LEARNING

Abstract

Base isolation systems have conventionally been used to mitigate the major impacts of earthquakes on the structures and attenuate their seismic responses. The scrap tyre pads are proven to be a material that resists vibrations. The optimal design of the base isolator has a vital role in the performance of a structure in response to an earthquake. Machine learning (ML) methods have been widely applied to predict the outputs of various problems in the structural engineering field. This study focuses on the development of a Machine Learning (ML)-based approach to predict the design of a base isolation system. The base isolator used in the present work is the Scrap Tyre Pad (STP) in a circular configuration. Conventionally, alternate layers of rubber bonded with steel reinforcement are used as isolators. As scrap tires consist of steel reinforcement inside the rubber itself, it can be considered as a cost-effective method. The presence of steel provides substantial vertical stiffness and rubber imparts horizontal flexibility. The eco-friendly Scrap Tyre Pads (STPs) provide several advantages such as low cost, ease of handling, and shear stiffness adjustments. In the present study, experimental evaluation of Circular Scrap Tyre Pads (CSTPs) under compression and cyclic loading is done in different configurations to analyse the structural behaviour of CSTPs as a base isolator. The damper properties obtained from the experiment are numerically analysed using non-linear time history analysis in ETABS to assess the isolator’s performance subjected to seismic loading on masonry structures. The data from the numerical evaluation is stored in Machine Learning (ML) database and the ML algorithm is trained to predict the design characteristics of the base isolator for a given structure. The performance of ML algorithms is validated using statistical metrics.