Application of artificial intelligence for determining the efficient performance of technological characteristics of structures using BIM

In recent years, artificial intelligence (AI) has been extensively deployed in different fields, especially in engineering. The ability of AI algorithms has been indicated in many research papers by providing accurate results compared to numerical and experimental approaches. Integrating Artificial Intelligence (AI) with Building Information Modeling (BIM) has opened new possibilities for enhancing structural design processes. BIM provides rich parametric data, while AI enables intelligent interpretation and prediction. This study develops a hybrid Artificial Neural Network–Genetic Algorithm (ANN-GA) model to predict the structural load performance of Reinforced Concrete (RC) buildings using parameters extracted from BIM models. Primary inputs include geometric properties, material strengths, reinforcement ratios, and layout configurations; outputs include structural indicators such as ultimate load capacity and deflection under standard loads. The model is trained and validated using empirical data from literature. The Artificial Neural Network (ANN) captures complex nonlinear input-output relationships, while the Genetic Algorithm optimizes network parameters like hidden layers, neurons, learning rate, and weights. Performance is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R^2. This study offers a fast, simulation-free structural assessment method leveraging BIM and AI for early-stage decision-making. The ANN-GA performed strongly: RMSE 2.11 ± 0.36 kN and R² 0.9804 ± 0.0052. Compared to baselines, it outperformed linear regression (RMSE: 23.13 kN, R^2: –1.13) and a fixed-architecture ANN (RMSE: 43.64 kN, R^2: –8.01). Sensitivity analysis showed span length and reinforcement ratio were most influential (32% and 27%). In case-based validations, prediction errors stayed within ±5%, confirming practical applicability. The model was integrated into a BIM environment for real-time structural feedback using normalized inputs via Revit APIs or IFC-based workflows.