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 R². 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 R² 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.