Python Implemented AI based Machine Learning Approach for the Prediction of Optimum Location of Building using Structural Response with Self Learning using ANN

Abstract: The construction industry faces growing demands for faster, safer, and cost-effective building designs. Traditional methods are no longer sufficient, prompting the use of AI and ML for data-driven optimization. This study analyzes 10 multistory building cases with varying soil stiffness (K) using structural analysis software. A Python-based ML model focuses on predicting optimal configurations using column axial force data. An ANN is developed to minimize axial forces, with outputs visualized using Matplotlib. Data preprocessing is done using Pandas and NumPy, and models are built with scikit-learn and TensorFlow. Both Linear Regression and ANN are applied, with an 80:20 train-test split. The ANN outperforms with an MSE of 0 and R² of 1 after 150 training epochs. The model identifies optimal designs, improving cost efficiency and structural stability. This approach enhances design accuracy and reduces manual effort in structural engineering tasks.