• Asmita Sahu
• ANURAG WAHANE

The seismic performance of reinforced concrete (RCC) buildings is highly influenced by the configuration and properties of shear walls, as well as key structural indices that govern lateral strength, stiffness, and ductility. Conventional seismic response assessment methods often rely on computationally expensive nonlinear analyses, making them less practical for rapid classification of building performance. In this study, a machine learning (ML)-based framework is developed to classify the seismic response of RCC buildings by integrating shear wall characteristics (location, aspect ratio, and wall density). A comprehensive dataset is generated through nonlinear time-history analyses of prototype RCC building models subjected to varying ground motions. The extracted response parameters are used to train and validate using machine learning classifier. The findings highlight the potential of combining structural engineering principles with data-driven techniques for rapid seismic performance assessment, aiding in resilient design and risk-informed decision-making for RCC buildings.

Shear wall, Machine Learning, Regression, Displacement

"SEISMIC PERFORMANCE PREDICTION OF RCC BUILDINGS HAVING SHEAR WALL USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.f611-f615, August-2025, Available :http://www.jetir.org/papers/JETIR2508585.pdf

"SEISMIC PERFORMANCE PREDICTION OF RCC BUILDINGS HAVING SHEAR WALL USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppf611-f615, August-2025, Available at : http://www.jetir.org/papers/JETIR2508585.pdf