A Hybrid AI-Based Risk Assessment Framework for Sustainable Construction: Integrating ANN, Fuzzy Logic, and IoT

The construction industry is central to the advancement of economic growth all over the world but it has various problems in risk management especially concerning sustainable construction projects. Standard risk management techniques like AHP and Monte Carlo simulation do not afford the flexibility and accuracy needed in construction sites. Based on the identified limitations, this study offers a new system of risk assessment that combines Artificial Neural Networks (ANN), Fuzzy Logic, and Internet of Things (IoT) technologies. Real-time IoT sensor data and historical project data are integrated into a real-time and adaptive system which can identify, suggest, and minimize potential risks for improved decision making. The ANN component is distinctive in pattern recognition and risk prediction while Fuzzy Logic brings ease of interpretation and reasoning in the uncertain environment. Raw IoT data are live data which may be processed and updated frequently relative to the devices and their environment. The effectiveness of this framework can be ascertained through experimental proof; the framework’s accuracy is 92.7%; project delay and cost have been minimized. The results reveal that the presented framework is highly resistant to noise, and its performance changes fairly slowly if the project requirements change. This integrative approach ensures the identification of the comprehensive solution for the sustainable construction risk management, which may help with the development of the safer, more efficient and non-harmful to the environment construction techniques.

@article{Góes2025,
  title     = {A Hybrid AI-Based Risk Assessment Framework for Sustainable Construction: Integrating ANN, Fuzzy Logic, and IoT},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {André Luís Barbosa Gomes Góes and Rafaqat Kazmi and Aqsa and Siddhartha Nuthakki},
  doi       = {10.14569/IJACSA.2025.0160305},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160305}
}