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

Suming Chen , Zhonghong Li , Hamid Assilzadeh , Slobodan Bjelić , Abdullah Alnutayfat , Dalia H. Elkamchouchi , Sultan Saleh Alnahdi , Belgacem Bouallegue , José Escorcia-Gutierrez

Abstract

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 R2. 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 R

Key Words

Artificial Intelligence (AI); Artificial Neural Network (ANN); Building Information Modeling (BIM); Genetic Algorithm (GA); reinforced concrete design; structural load prediction

Address

(1) Suming Chen, Zhonghong Li — School of Architecture and Engineering, Chongqing Chemical Industry Vocational College, Chongqing 401228, China
(2) Hamid Assilzadeh — Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietam
(3) Hamid Assilzadeh — School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam
(4) Hamid Assilzadeh — Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, India
(5) Slobodan Bjelić — Univerzitet u Prištini, — Kosovska Mitrovica, Fakultet tehničkih nauka, Kosovska Mitrovica, Republic of Serbia
(6) Abdullah Alnutayfat — Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
(7) Dalia H. Elkamchouchi — Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
(8) Sultan Saleh Alnahdi — Civil Engineering Department, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
(9) Belgacem Bouallegue — Department of Computer Engineering, College of Computer Science, King Khalid University, ABHA, 61421, Saudi Arabia
(10) José Escorcia-Gutierrez — Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.

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