PREDICTION OF OPTIMUM LOCATION OF BUILDING WITH SELF LEARNING ANN: RECENT ADVANCEMENTS IN STRUCTURAL ENGINEERING - VOLUME V

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.