Technical Papers
Oct 11, 2023

Smart Safety: Big Data–Enabled System for Analysis and Management of Unsafe Behavior by Construction Workers

Publication: Journal of Management in Engineering
Volume 40, Issue 1

Abstract

In the era of big data, the extraction of valuable knowledge and insights becomes feasible through efficient data processing methods. In this research, we establish an analytical framework enabled by big data to aid in the management of construction workers’ unsafe behaviors. To evaluate workers’ behavioral patterns, a multistage data processing model is employed, uncovering previously unknown connections between unsafe act records and situational data. Leveraging the high-level knowledge derived from this analysis, personalized safety management strategies are formulated based on individual workers’ behavioral patterns. The effectiveness of the proposed framework is evaluated by comparing it to conventional management strategies across three construction sites. Results demonstrate that behavioral patterns discovered by the big data framework provide an important decision basis and achieve smart construction unsafe behavior management.

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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

We are thankful for the financial support of the National Natural Science Foundation of China (Grant No. 72101275, No. 72201095) and the Natural Science Foundation of Hunan Province (Grant No. 2022JJ40645).

References

Ahn, S., T. Kim, Y.-J. Park, and J.-M. Kim. 2020. “Improving effectiveness of safety training at construction worksite using 3D BIM simulation.” Adv. Civ. Eng. 2020 (Feb): 2473138. https://doi.org/10.1155/2020/2473138.
Bilal, M., L. O. Oyedele, O. O. Akinade, S. O. Ajayi, H. A. Alaka, H. A. Owolabi, J. Qadir, M. Pasha, and S. A. Bello. 2016a. “Big data architecture for construction waste analytics (CWA): A conceptual framework.” J. Build. Eng. 6 (Jun): 144–156. https://doi.org/10.1016/j.jobe.2016.03.002.
Bilal, M., L. O. Oyedele, J. Qadir, K. Munir, S. O. Ajayi, O. O. Akinade, H. A. Owolabi, H. A. Alaka, and M. Pasha. 2016b. “Big Data in the construction industry: A review of present status, opportunities, and future trends.” Adv. Eng. Inf. 30 (3): 500–521. https://doi.org/10.1016/j.aei.2016.07.001.
Broström. 2022. “Real-time multi-object tracker using YOLOv5 and deep sort with OSNet.” Accessed November 27, 2022. https://github.com/mikel-brostrom/Yolov5_DeepSort_OSNet.
Bügler, M., G. Ogunmakin, J. Teizer, P. A. Vela, and A. Borrmann. 2014. “A comprehensive methodology for vision-based progress and activity estimation of excavation processes for productivity assessment.” In Proc., EG-ICE Workshop on Intelligent Computing in Engineering. Munich, Germany: European Group For Intelligent Computing in Engineering. https://doi.org/10.13140/RG.2.1.4630.2561.
Chen, Q., D. Long, C. Yang, and H. Xu. 2023. “Knowledge graph improved dynamic risk analysis method for behavior-based safety management on a construction site.” J. Manage. Eng. 39 (4): 04023023. https://doi.org/10.1061/JMENEA.MEENG-5306.
Choudhry, R. M. 2014. “Behavior-based safety on construction sites: A case study.” Accid. Anal. Prev. 70 (Sep): 14–23. https://doi.org/10.1016/j.aap.2014.03.007.
Clarke, S., and K. Ward. 2006. “The role of leader influence tactics and safety climate in engaging employees’ safety participation.” Risk Anal. 26 (5): 1175–1185. https://doi.org/10.1111/j.1539-6924.2006.00824.x.
DeJoy, D. M. 2005. “Behavior change versus culture change: Divergent approaches to managing workplace safety.” Saf. Sci. 43 (2): 105–129. https://doi.org/10.1016/j.ssci.2005.02.001.
Ding, L., W. Fang, H. Luo, P. E. D. Love, B. Zhong, and X. Ouyang. 2018. “A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory.” Autom. Constr. 86 (Feb): 118–124. https://doi.org/10.1016/j.autcon.2017.11.002.
Ding, L., Y. Zhou, and B. Akinci. 2014. “Building information modeling (BIM) application framework: The process of expanding from 3D to computable nD.” Autom. Constr. 46 (Oct): 82–93. https://doi.org/10.1016/j.autcon.2014.04.009.
Fan, Y., X. Lu, D. Li, and Y. Liu. 2016. “Video-based emotion recognition using CNN-RNN and C3D hybrid networks.” In Proc., 18th ACM Int. Conf. on Multimodal Interaction, 445–450. New York: Association for Computing Machinery. https://doi.org/10.1145/2993148.2997632.
Fang, Q., H. Li, X. Luo, L. Ding, H. Luo, and C. Li. 2018a. “Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment.” Autom. Constr. 93 (Aug): 148–164. https://doi.org/10.1016/j.autcon.2018.05.022.
Fang, Q., H. Li, X. Luo, L. Ding, H. Luo, T. M. Rose, and W. An. 2018b. “Detecting non-hardhat-use by a deep learning method from far-field surveillance videos.” Autom. Constr. 85 (Jan): 1–9. https://doi.org/10.1016/j.autcon.2017.09.018.
Fang, Q., H. Li, X. Luo, L. Ding, T. M. Rose, W. An, and Y. Yu. 2018c. “A deep learning-based method for detecting non-certified work on construction sites.” Adv. Eng. Inf. 35 (Jan): 56–68. https://doi.org/10.1016/j.aei.2018.01.001.
Fang, Q., H. Li, X. Luo, C. Li, and W. An. 2020a. “A sematic and prior-knowledge-aided monocular localization method for construction-related entities.” Comput.-Aided Civ. Infrastruct. Eng. 35 (9): 979–996. https://doi.org/10.1111/mice.12541.
Fang, W., P. E. Love, H. Luo, and L. Ding. 2020b. “Computer vision for behaviour-based safety in construction: A review and future directions.” Adv. Eng. Inf. 43 (Jun): 100980. https://doi.org/10.1016/j.aei.2019.100980.
Fang, W., B. Zhong, N. Zhao, P. E. D. Love, H. Luo, J. Xue, and S. Xu. 2019. “A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network.” Adv. Eng. Inf. 39 (Jan): 170–177. https://doi.org/10.1016/j.aei.2018.12.005.
Gao, H., A. Yüce, and J. Thiran. 2014. “Detecting emotional stress from facial expressions for driving safety.” In Proc., IEEE Int. Conf. on Image Processing (ICIP), 5961–5965. New York: IEEE. https://doi.org/10.1109/ICIP.2014.7026203.
Geller, E. S. 2005. “Behavior-based safety and occupational risk management.” [In English.] Behav. Modification 29 (3): 539–561. https://doi.org/10.1177/0145445504273287.
Getuli, V., P. Capone, A. Bruttini, and T. Sorbi. 2022. “A smart objects library for BIM-based construction site and emergency management to support mobile VR safety training experiences.” Constr. Innovation 22 (3): 504–530. https://doi.org/10.1108/CI-04-2021-0062.
Guo, S., L. Ding, H. Luo, and X. Jiang. 2016. “A big-data-based platform of workers’ behavior: Observations from the field.” Accid. Anal. Prev. 93 (Jun): 299–309. https://doi.org/10.1016/j.aap.2015.09.024.
Hasan, A., and K. N. Jha. 2013. “Safety incentive and penalty provisions in Indian construction projects and their impact on safety performance.” Int. J. Inj. Control Saf. Promot. 20 (1): 3–12. https://doi.org/10.1080/17457300.2011.648676.
Hinze, J., and J. Gambatese. 2003. “Factors that influence safety performance of specialty contractors.” J. Constr. Eng. Manage. 129 (2): 159–164. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:2(159).
Hopkins, A. 2006. “What are we to make of safe behaviour programs?” Saf. Sci. 44 (7): 583–597. https://doi.org/10.1016/j.ssci.2006.01.001.
Hwang, B.-G., J. Ngo, and J. Z. K. Teo. 2022. “Challenges and strategies for the adoption of smart technologies in the construction industry: The case of Singapore.” J. Manage. Eng. 38 (1): 05021014. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000986.
Jannadi, M. O. 1996. “Factors affecting the safety of the construction industry.” Build. Res. Inf. 24 (2): 108–112. https://doi.org/10.1080/09613219608727510.
Johari, S., and K. N. Jha. 2020. “Interrelationship among belief, intention, attitude, behavior, and performance of construction workers.” J. Manage. Eng. 36 (6): 04020081. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000851.
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.
Li, C., and L. Ding. 2019. “Falling objects detection for near miss incidents identification on construction site.” In Proc., Computing in Civil Engineering, 138–145. Reston, VA: ASCE. https://doi.org/10.1061/9780784482438.018.
Li, R. 2023. “Revisiting person–situation interactionism.” Nat. Rev. Psychol. 2 (1): 6. https://doi.org/10.1038/s44159-022-00139-8.
Lu, R., H. Zhu, X. Liu, J. K. Liu, and J. Shao. 2014. “Toward efficient and privacy-preserving computing in big data era.” IEEE Netw. 28 (4): 46–50. https://doi.org/10.1109/MNET.2014.6863131.
Lu, W., C. C. Lai, and T. Tse. 2018. BIM and big data for construction cost management. New York: Routledge.
Munawar, H. S., F. Ullah, S. Qayyum, and D. Shahzad. 2022. “Big data in construction: Current applications and future opportunities.” Big Data Cognit. Comput. 6 (1): 18. https://doi.org/10.3390/bdcc6010018.
Naderpajouh, N., J. Choi, and M. Hastak. 2016. “Exploratory framework for application of analytics in the construction industry.” J. Manage. Eng. 32 (2): 04015047. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000409.
Nath, N. D., and A. H. Behzadan. 2020. “Deep convolutional networks for construction object detection under different visual conditions.” Front. Built Environ. 6 (Jun): 97. https://doi.org/10.3389/fbuil.2020.00097.
Omran, A., A. Omran, and A. H. P. Kadir. 2010. “Critical success factors that influencing safety program performance in Malaysian construction projects: Case studies.” J. Acad. Res. Econ. 2 (1): 125–134.
Patel, D. A., and K. N. Jha. 2016. “Structural equation modeling for relationship-based determinants of safety performance in construction projects.” J. Manage. Eng. 32 (6): 05016017. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000457.
Ramírez-Gallego, S., A. Fernández, S. García, M. Chen, and F. Herrera. 2018. “Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce.” Inf. Fusion 42 (Jun): 51–61. https://doi.org/10.1016/j.inffus.2017.10.001.
Rowley, J. 2007. “The wisdom hierarchy: Representations of the DIKW hierarchy.” J. Inf. Sci. 33 (2): 163–180. https://doi.org/10.1177/0165551506070706.
Sawacha, E., S. Naoum, and D. Fong. 1999. “Factors affecting safety performance on construction sites.” Int. J. Project Manage. 17 (5): 309–315. https://doi.org/10.1016/S0263-7863(98)00042-8.
Snyder, M. 2013. “B = f (P, S): Perspectives on persons and situations, from Lewin to Bond and beyond.” Asian J. Social Psychol. 16 (1): 16–18. https://doi.org/10.1111/ajsp.12013.
Song, Y., J. Wang, Y. Ge, and C. Xu. 2020. “An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data.” GIScience Rem. Sens. 57 (5): 593–610. https://doi.org/10.1080/15481603.2020.1760434.
Vinodkumar, M. N., and M. Bhasi. 2010. “Safety management practices and safety behaviour: Assessing the mediating role of safety knowledge and motivation.” Accid. Anal. Prev. 42 (6): 2082–2093. https://doi.org/10.1016/j.aap.2010.06.021.
Wang, X., C. Liu, X. Song, and X. Cui. 2022. “Development of an internet-of-things-based technology system for construction safety hazard prevention.” J. Manage. Eng. 38 (3): 04022009. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001035.
Wu, H., B. Zhong, H. Li, J. Guo, and Y. Wang. 2021a. “On-site construction quality inspection using blockchain and smart contracts.” J. Manage. Eng. 37 (6): 04021065 https://doi.org/10.1061/(ASCE)ME.1943-5479.0000967.
Wu, H., B. Zhong, H. Li, P. Love, X. Pan, and N. Zhao. 2021b. “Combining computer vision with semantic reasoning for on-site safety management in construction.” J. Build. Eng. 42 (Jun): 103036. https://doi.org/10.1016/j.jobe.2021.103036.
Yu, Y., H. Li, X. Yang, L. Kong, X. Luo, and A. Y. L. Wong. 2019. “An automatic and non-invasive physical fatigue assessment method for construction workers.” Autom. Constr. 103 (Jul): 1–12. https://doi.org/10.1016/j.autcon.2019.02.020.

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Journal of Management in Engineering
Volume 40Issue 1January 2024

History

Received: Feb 3, 2023
Accepted: Aug 10, 2023
Published online: Oct 11, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 11, 2024

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Assistant Professor, School of Civil Engineering, Central South Univ., Changsha 410004, China. ORCID: https://orcid.org/0000-0002-9819-4055. Email: [email protected]
Daniel Castro-Lacouture, Ph.D., M.ASCE https://orcid.org/0000-0003-1549-9097 [email protected]
Professor, Purdue Polytechnic Institute, Purdue Univ., West Lafayette, IN 47907 (corresponding author). ORCID: https://orcid.org/0000-0003-1549-9097. Email: [email protected]
Chengqian Li, Ph.D. [email protected]
Assistant Professor, School of Civil Engineering, Hunan Univ., Changsha 410082, China. Email: [email protected]

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