Integrating IoT and Machine Learning for Enhanced Construction Safety Management


  • Rajendra Pujari Bharati Vidyapeeth (Deemed to be University), Institute of Management and Rural Development Administration, Sangli, Maharashtra, India
  • R. Suguna Department of Computer Science and Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  • M. S. Vinmathi Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
  • Jyothi A. P. Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India
  • Sathyakala S. Department of Management Studies, Sona College of Technology, Salem, Tamil Nadu, India
  • Dhanashri Vinay Sahasrabuddhe Department of Computer Applications, BV(DU), Institute of Management and Rural Development Adminstration, Sangli, Maharashtra, India


construction site safety, Internet of Things (IoT), machine learning, hazard prediction, real-time monitoring


This study presents a unique framework for enhancing construction site safety control with the integration of Internet of Things (IoT) technologies and machine learning to know models. A broad array of sensors, including as temperature, pressure, fireplace, vibration, and proximity sensors, is strategically installed to expose important protective characteristics in real-time. The examine incorporates machine learning, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT), to beautify danger prediction abilities. The findings indicate that ANN attains the highest accuracy of 94.5%, while SVM records 92.3%, NB records 88.7%, and DT records 84.4%. Confusion matrices give a thorough analysis of genuine high quality, true horrible, false fantastic, and false poor forecasts for every version, giving insights into their individual strengths and limits. Notably, ANN displays better overall performance, displaying its ability to change protection methods in building situations. The integrated approach provides a robust safety net, integrating actual-time sensor facts with superior machine learning strategies to proactively uncover and minimise threats, consequently enhancing average safety in construction locations. This study adds to the expanding frame of understanding at the junction of IoT, machine learning, and construction protection, delivering a scalable and adaptive solution for industry stakeholders searching for to prioritize and increase protection outcomes on construction sites.


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How to Cite

Pujari, R. ., Suguna, R. ., Vinmathi, M. S. ., A. P., J. ., S., S. ., & Sahasrabuddhe, D. V. . (2024). Integrating IoT and Machine Learning for Enhanced Construction Safety Management. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 540–548. Retrieved from



Research Article