Signal Processing and Alert Logic Evaluation for IoT–Based Work Zone Proximity Safety System
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 2
Abstract
Construction projects are dynamic by nature because of continuously moving resources such as heavy equipment and workers. This nature necessarily results in proximity hazards, especially on a large-scale construction site. Especially, struck-by accidents still account for about 38% of the total injuries in the US construction industry. Although there have been several efforts to mitigate the hazards, the statistics show that the hazards still persist. To provide a practical solution to this problem, this study proposes an Internet of Things (IoT)-based proximity warning system that provides an alert to workers whenever they are close to heavy equipment. The system includes equipment protection units (EPUs), personal protection units (PPUs), and Bluetooth low energy (BLE) beacons. A framework of signal processing and alert logic was developed to estimate the distance between PPUs and EPUs and to activate alerting modules timely. By calculating the distance based on the signal strength using a particle filtering method, EPUs and PPUs provide auditory and vibration alerts to equipment operators and workers when they are in an alert range. Also, practical alert logic was developed based on the site worker’s feedback. This study validates the system performance with different signal processing methods and alert logic through five real-world field tests. The system achieved a precision, recall, and F1-score of 89.21%, 97.45%, and 0.931 from the field tests, respectively. Also, positive feedback was obtained from the participating workers. The proposed IoT-based proximity warning system has a high potential for a practical solution to proximity hazards in actual construction sites.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based upon work supported by the Georgia Department of Transportation (GDOT) (RP18-17) and the National Cooperative Highway Research Program (NCHRP) (IDEA 226). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of GDOT and NCHRP.
References
Awolusi, I., C. Nnaji, E. Marks, and M. Hallowell. 2019. “Enhancing construction safety monitoring through the application of internet of things and wearable sensing devices: A review.” In Proc., ASCE Int. Conf. on Computing in Civil Engineering 2019, 530–538. Reston, VA: ASCE.
Brilakis, I., M. W. Park, and G. Jog. 2011. “Automated vision tracking of project related entities.” Adv. Eng. Inf. 25 (4): 713–724. https://doi.org/10.1016/j.aei.2011.01.003.
Chi, S., and C. H. Caldas. 2012. “Image-based safety assessment: Automated spatial safety risk identification of earthmoving and surface mining activities.” J. Constr. Eng. Manage. 138 (3): 341–351. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000438.
Cho, C., K. Kim, J. Park, and Y. K. Cho. 2018. “Data-driven monitoring system for preventing the collapse of scaffolding structures.” J. Constr. Eng. Manage. 144 (8): 04018077. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001535.
Cho, Y. K., J. H. Youn, and D. Martinez. 2010. “Error modeling for an untethered ultra-wideband system for construction indoor asset tracking.” Autom. Constr. 19 (1): 43–54. https://doi.org/10.1016/j.autcon.2009.08.001.
Choe, S., F. Leite, D. Seedah, and C. Caldas. 2013. “Application of sensing technology in the prevention of backing accidents in construction work zones.” In Computing in civil engineering, 557–564. Reston, VA: ASCE.
Choe, S., F. Leite, D. P. K. Seedah, and C. H. Caldas. 2014. “Evaluation of sensing technology for the prevention of backover accidents in construction work zones.” J. Inf. Technol. Construct. 19 (Jan): 1–19.
Fang, W., L. Ding, B. Zhong, P. E. D. Love, and H. Luo. 2018. “Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach.” Adv. Eng. Inf. 37 (5): 139–149. https://doi.org/10.1016/j.aei.2018.05.003.
Fang, Y., Y. K. Cho, S. Zhang, and E. Perez. 2016. “Case study of BIM and cloud–enabled real-time RFID indoor localization for construction management applications.” J. Constr. Eng. Manage. 142 (7): 05016003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001125.
Haeberlen, A., A. Rudys, E. Flannery, D. S. Wallach, A. M. Ladd, and L. E. Kavraki. 2004. “Practical robust localization over large-scale 802.11 wireless networks.” In Proc., Annual Int. Conf. on Mobile Computing and Networking, 70–84. New York: Association for Computing Machinery.
Haghi, M., K. Thurow, and R. Stoll. 2017. “Wearable devices in medical internet of things: Scientific research and commercially available devices.” Healthcare Inf. Res. 23 (1): 4–15. https://doi.org/10.4258/hir.2017.23.1.4.
Han, W., E. White, M. Mollenhauer, and N. Roofigari-Esfahan. 2019. “A connected work zone hazard detection system for roadway construction workers.” In Proc., ASCE Int. Conf. on Computing in Civil Engineering 2019, 242–250. Reston, VA: ASCE.
Hinze, J., S. Thurman, and A. Wehle. 2013. “Leading indicators of construction safety performance.” Saf. Sci. 51 (1): 23–28. https://doi.org/10.1016/j.ssci.2012.05.016.
Huang, Y., A. Hammad, and Z. Zhu. 2021. “Providing proximity alerts to workers on construction sites using bluetooth low energy RTLS.” Autom. Constr. 132 (Dec): 103928. https://doi.org/10.1016/j.autcon.2021.103928.
Ju, Y., C. Kim, and H. Kim. 2012. “RFID and CCTV-based material delivery monitoring for cable-stayed bridge construction.” J. Comput. Civ. Eng. 26 (2): 183–190. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000134.
Kanan, R., O. Elhassan, and R. Bensalem. 2018. “An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies.” Autom. Constr. 88 (Apr): 73–86. https://doi.org/10.1016/j.autcon.2017.12.033.
Kim, D., S. Lee, V. R. Kamat, S. Lee, and V. R. Kamat. 2020. “Proximity prediction of mobile objects to prevent contact-driven accidents in co-robotic construction.” J. Comput. Civ. Eng. 34 (4): 04020022. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000899.
Kim, D., M. Liu, S. H. Lee, and V. R. Kamat. 2019. “Remote proximity monitoring between mobile construction resources using camera-mounted UAVs.” Autom. Constr. 99 (4): 168–182. https://doi.org/10.1016/j.autcon.2018.12.014.
Kim, H., H. Kim, Y. W. Hong, and H. Byun. 2017a. “Detecting construction equipment using a region-based fully convolutional network and transfer learning.” J. Comput. Civ. Eng. 32 (2): 04017082. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000731.
Kim, H. H., K. Kim, and H. H. Kim. 2015. “Vision-based object-centric safety assessment using fuzzy inference: Monitoring struck-by accidents with moving objects.” J. Comput. Civ. Eng. 30 (4): 04015075. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000562.
Kim, K., H. Kim, and H. Kim. 2017b. “Image-based construction hazard avoidance system using augmented reality in wearable device.” Autom. Constr. 83 (4): 390–403. https://doi.org/10.1016/j.autcon.2017.06.014.
Lee, H.-S., K.-P. Lee, M. Park, Y. Baek, S. Lee, Y. Baek, and S. Lee. 2012. “RFID-based real-time locating system for construction safety management.” J. Comput. Civ. Eng. 26 (3): 366–377. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000144.
Maalek, R., and F. Sadeghpour. 2016. “Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios.” Autom. Constr. 63 (4): 12–26. https://doi.org/10.1016/j.autcon.2015.11.009.
Park, J., J. Chen, and Y. K. Cho. 2017a. “Self-corrective knowledge-based hybrid tracking system using BIM and multimodal sensors.” In Advanced engineering informatics, 126–138. Amsterdam, Netherlands: Elsevier.
Park, J., and Y. K. Cho. 2017. “Development and evaluation of a probabilistic local search algorithm for complex dynamic indoor construction sites.” J. Comput. Civ. Eng. 31 (4): 04017015. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000658.
Park, J., K. Kim, and Y. K. Cho. 2016a. “Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors.” J. Constr. Eng. Manage. 143 (2): 05016019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001223.
Park, J., E. Marks, Y. K. Cho, and W. Suryanto. 2016b. “Performance test of wireless technologies for personnel and equipment proximity sensing in work zones.” J. Constr. Eng. Manage. 142 (1): 04015049. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001031.
Park, J., X. Yang, Y. K. Cho, and J. Seo. 2017b. “Improving dynamic proximity sensing and processing for smart work-zone safety.” Autom. Constr. 84 (Aug): 111–120. https://doi.org/10.1016/j.autcon.2017.08.025.
Pradhananga, N., and J. Teizer. 2013. “Automatic spatio-temporal analysis of construction site equipment operations using GPS data.” Autom. Constr. 29 (Sep): 107–122. https://doi.org/10.1016/j.autcon.2012.09.004.
Razavi, S. N., and C. T. Haas. 2010. “Multisensor data fusion for on-site materials tracking in construction.” Autom. Constr. 19 (8): 1037–1046. https://doi.org/10.1016/j.autcon.2010.07.017.
Song, J., T. Haas, C. H. Caldas, C. T. Haas, and C. H. Caldas. 2006. “Tracking the location of materials on construction job sites.” J. Constr. Eng. Manage. 132 (9): 911–918. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:9(911).
US Department of Labor. 2021. “Fatal occupational injuries by industry and event or exposure, all United States, 2020.” Accessed January 17, 2022. https://www.bls.gov/iif/oshwc/cfoi/cftb0340.htm.
Wang, J., and S. N. Razavi. 2016. “Low false alarm rate model for unsafe-proximity detection in construction.” J. Comput. Civ. Eng. 30 (2): 04015005. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000470.
Wu, H., J. Tao, X. Li, X. Chi, H. Li, X. Hua, R. Yang, S. Wang, and N. Chen. 2013. “A location based service approach for collision warning systems in concrete dam construction.” Saf. Sci. 51 (1): 338–346. https://doi.org/10.1016/j.ssci.2012.08.006.
Zhang, M., T. Cao, and X. Zhao. 2017. “Applying sensor-based technology to improve construction safety management.” Sensors 17 (8): 1841. https://doi.org/10.3390/s17081841.
Zhang, M., Z. Cao, Z. Yang, and X. Zhao. 2020. “Utilizing computer vision and fuzzy inference to evaluate level of collision safety for workers and equipment in a dynamic environment.” J. Constr. Eng. Manage. 146 (6): 04020051. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001802.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 1, 2022
Accepted: Sep 6, 2022
Published online: Nov 21, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 21, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Yao Huang, Dingli Liu, Fredric M. Bell, Jing Yu, Feniosky Pena-Mora, Influences of Intra- and Interorganizational IT Innovations on Knowledge Sharing and Team Creativity: Evidence from Construction Projects in China, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14007, 150, 5, (2024).
- Pinsheng Duan, Jianliang Zhou, Yang Miang Goh, Safety Risk Diagnosis Based on Motion Trajectory for Construction Workers: An Integrated Approach, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13673, 149, 11, (2023).