Prediction of Optimum Location of Building with Self Learning Ann

Rapid urbanization and complex designs drive the building industry to adopt AI and ML for faster, safer, and more cost-efficient solutions. In this research book, soil investigation reports were used to define site-specific parameters and 10 distinct building cases were analyzed using building analysis software, each with individual spring stiffness (K). A Python-based ML approach was developed to predict optimum multistory structural configurations focusing on column axial force. The AI-ML code, comprising two stages, identifies inputs, generates plots using Matplotlib v3.10.3 and compares predicted versus actual values to evaluate MSE and R . Data preprocessing utilized Pandas v2.0.3 and NumPy v1.26.4, while Linear Regression and ANN models (TensorFlow v2.16.1, sklearn v1.3.0) were trained on an 80:20 split. The ANN achieved an MSE of 0 and R of 1, marking superior accuracy and efficiency for structural design optimization.Keywords - AI based Prediction, Machine Learning, Python Programming, Multistory Buildings, Optimization, Structural Design, Data Analysis, Model Training and Computational Efficiency.