Integrating IoT and Machine Learning for Enhanced Construction Safety Management

Authors

  • 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

Keywords:

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

Abstract

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.

Downloads

Download data is not yet available.

References

P. V. Sáez, M. Del Río Merino, C. Porras-Amores, and A. S. A. González, “Assessing the accumulation of construction waste generation during residential building construction works,” Resources, Conservation and Recycling, vol. 93, no. 2014, pp. 67–74, 2014, doi: 10.1016/j.resconrec.2014.10.004.

S. H. Yang, J. U. Kim, Y. J. Kim, and H. Ok, “Measures for the improvement of construction work accident information service contents in CPMS: Focused on analysis of construction work accidents big data,” Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015, pp. 340–343, 2016, doi: 10.1109/CSCI.2015.51.

Y. He, Z. Liu, X. Zhou, and B. Zheng, “Analysis of Urban traffic accidents features and correlation with traffic congestion in large-scale construction district,” Proceedings - 2017 International Conference on Smart Grid and Electrical Automation, ICSGEA 2017, vol. 2017-Janua, pp. 641–644, 2017, doi: 10.1109/ICSGEA.2017.110.

W. Lu, J. Lou, C. Webster, F. Xue, Z. Bao, and B. Chi, “Estimating construction waste generation in the Greater Bay Area, China using machine learning,” Waste Management, vol. 134, no. August, pp. 78–88, 2021, doi: 10.1016/j.wasman.2021.08.012.

L. Zheng et al., “Characterizing the generation and flows of construction and demolition waste in China,” Construction and Building Materials, vol. 136, pp. 405–413, 2017, doi: 10.1016/j.conbuildmat.2017.01.055.

Y. Su and C. Zhao, “Analysis of the momentum for construction enterprises to improve safety producing behavior,” 2010 International Conference on Logistics Systems and Intelligent Management, ICLSIM 2010, vol. 3, pp. 1835–1838, 2010, doi: 10.1109/ICLSIM.2010.5461328.

R. Mao, H. Duan, H. Gao, and H. Wu, “Characterizing the Generation and Management of a New Construction Waste in China: Glass Curtain Wall,” Procedia Environmental Sciences, vol. 31, pp. 204–210, 2016, doi: 10.1016/j.proenv.2016.02.027.

A. Bakshan, I. Srour, G. Chehab, and M. El-Fadel, “A field based methodology for estimating waste generation rates at various stages of construction projects,” Resources, Conservation and Recycling, vol. 100, pp. 70–80, 2015, doi: 10.1016/j.resconrec.2015.04.002.

D. C. Ning, J. P. Wang, and W. S. Wang, “Forecasting model of mine construction project accidents based on SOM Neural Network,” Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010, vol. 3, no. Icnc, pp. 1252–1255, 2010, doi: 10.1109/ICNC.2010.5583625.

W. Ferdous et al., “Recycling of landfill wastes (tyres, plastics and glass) in construction – A review on global waste generation, performance, application and future opportunities,” Resources, Conservation and Recycling, vol. 173, no. May, p. 105745, 2021, doi: 10.1016/j.resconrec.2021.105745.

Z. Chen and Y. Wu, “Explaining the causes of construction accidents and recommended solutions,” 2010 International Conference on Management and Service Science, MASS 2010, 2010, doi: 10.1109/ICMSS.2010.5576704.

Z. Yang, F. Xue, and W. Lu, “Handling missing data for construction waste management: machine learning based on aggregated waste generation behaviors,” Resources, Conservation and Recycling, vol. 175, no. August, p. 105809, 2021, doi: 10.1016/j.resconrec.2021.105809.

P. V. Sáez, C. Porras-Amores, and M. Del Río Merino, “New quantification proposal for construction waste generation in new residential constructions,” Journal of Cleaner Production, vol. 102, pp. 58–65, 2015, doi: 10.1016/j.jclepro.2015.04.029.

H. Zhang, “Research on Construction Safety Risk of Prefabricated Utility Tunnel,” Proceedings - 2021 International Conference on Management Science and Software Engineering, ICMSSE 2021, pp. 192–195, 2021, doi: 10.1109/ICMSSE53595.2021.00048.

L. Yang, S. Li, and Y. Xiao, “A BDD-based method for the importance analysis of construction safety accident,” 2010 International Conference on Future Information Technology and Management Engineering, FITME 2010, vol. 1, pp. 313–316, 2010, doi: 10.1109/FITME.2010.5654921.

L. J. G. Estrada, J. Nakatani, T. Hayashi, and T. Fujita, “Life cycle assessment of construction and demolition waste management based on waste generation projections of residential buildings in Metro Manila, the Philippines,” Cleaner Waste Systems, vol. 4, no. December 2022, p. 100076, 2023, doi: 10.1016/j.clwas.2023.100076.

M. S. Jain, “A mini review on generation, handling, and initiatives to tackle construction and demolition waste in India,” Environmental Technology and Innovation, vol. 22, p. 101490, 2021, doi: 10.1016/j.eti.2021.101490.

A. Akhtar and A. K. Sarmah, “Construction and demolition waste generation and properties of recycled aggregate concrete: A global perspective,” Journal of Cleaner Production, vol. 186, pp. 262–281, 2018, doi: 10.1016/j.jclepro.2018.03.085.

G. Murugadoss et al., “Construction of novel quaternary nanocomposite and its synergistic effect towards superior photocatalytic and antibacterial application,” Journal of Environmental Chemical Engineering, vol. 10, no. 1, p. 106961, 2022, doi: 10.1016/j.jece.2021.106961.

R. Hu, K. Chen, W. Chen, Q. Wang, and H. Luo, “Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China,” Waste Management, vol. 126, pp. 791–799, 2021, doi: 10.1016/j.wasman.2021.04.012.

L. M. F. Maués, B. do M. O. do Nascimento, W. Lu, and F. Xue, “Estimating construction waste generation in residential buildings: A fuzzy set theory approach in the Brazilian Amazon,” Journal of Cleaner Production, vol. 265, 2020, doi: 10.1016/j.jclepro.2020.121779.

A. Bakchan and K. M. Faust, “Construction waste generation estimates of institutional building projects: Leveraging waste hauling tickets,” Waste Management, vol. 87, pp. 301–312, 2019, doi: 10.1016/j.wasman.2019.02.024.

A. B. De Melo, A. F. Gonalves, and I. M. Martins, “Construction and demolition waste generation and management in Lisbon (Portugal),” Resources, Conservation and Recycling, vol. 55, no. 12, pp. 1252–1264, 2011, doi: 10.1016/j.resconrec.2011.06.010.

K. Zhu and L. Shuquan, “Study on the long effective mechanism of construction safety supervision management,” Proceedings - 2008 International Conference on MultiMedia and Information Technology, MMIT 2008, pp. 455–458, 2008, doi: 10.1109/MMIT.2008.63.

A. P. Kern, L. V. Amor, S. C. Angulo, and A. Montelongo, “Factors influencing temporary wood waste generation in high-rise building construction,” Waste Management, vol. 78, pp. 446–455, 2018, doi: 10.1016/j.wasman.2018.05.057.

M. Duque-Acevedo, I. Lancellotti, F. Andreola, L. Barbieri, L. J. Belmonte-Ureña, and F. Camacho-Ferre, “Management of agricultural waste biomass as raw material for the construction sector: an analysis of sustainable and circular alternatives,” Environmental Sciences Europe, vol. 34, no. 1, 2022, doi: 10.1186/s12302-022-00655-7.

Y. Pan and L. Zhang, “Integrating BIM and AI for Smart Construction Management: Current Status and Future Directions,” Archives of Computational Methods in Engineering, no. 0123456789, 2022, doi: 10.1007/s11831-022-09830-8.

X. Zheng and Y. Li, “Engineering construction accident prediction and controlling technology based on grade of likelihood of accident occurrence,” 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, pp. 1–6, 2008, doi: 10.1109/WiCom.2008.1827.

H. Shi, “An improved unascertained approach to construction accident risk assessment and analysis,” 3rd International Symposium on Intelligent Information Technology Application, IITA 2009, vol. 1, pp. 610–613, 2009, doi: 10.1109/IITA.2009.74.

Downloads

Published

24.03.2024

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 https://ijisae.org/index.php/IJISAE/article/view/5183

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.