Distributed Simulation–Based Analytics Approach for Enhancing Safety Management Systems in Industrial Construction
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 1
Abstract
Although methods for assessing and simulating the influence of safety-related measures on safety performance have been proposed, practical applications remain limited. Data required by these methods are dispersed across departments, necessitating the development or redesign of data warehouses. This research proposes a simulation-based analytics approach to enhance safety management system (SMS) decision making using distributed simulation to overcome limitations associated with previous approaches. This distributed simulation approach is used to (1) integrate historical data without modifying data-warehouse structures (i.e., data fusion component), (2) link data to an artificial neural network–based analysis component for determining the influence of safety-related measures on incident levels, (3) connect data and analysis components to existing simulation components, and (4) combine the outputs, resulting in a comprehensive safety performance evaluation system to examine incident levels. Results demonstrate that this approach successfully fuses and integrates data from several sources with analysis and simulation components in a cost-, labor-, and time-efficient manner. A distributed simulation–based analytics approach represents a considerable opportunity for industrial construction companies to more effectively use historical data, analysis tools, and simulation models.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the acknowledgments.
Acknowledgments
The authors are thankful for the support of PCL Industrial Management Inc. This research was made possible by the financial support of a Collaborative Research and Development Grant (CRDPJ 492657) from the Natural Sciences and Engineering Research Council (NSERC) of Canada. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSERC.
References
Alavi, A. H., and A. H. Gandomi. 2017. “Big data in civil engineering.” Autom. Constr. 79 (Jul): 1–2. https://doi.org/10.1016/j.autcon.2016.12.008.
ANSI (American National Standards Institute). 2012. American national standard for occupational health and safety management systems. ANSI Z10. Washington, DC: ANSI.
Ayhan, B. U., and O. B. Tokdemir. 2019. “Predicting the outcome of construction incidents.” Saf. Sci. 113 (Mar): 91–104. https://doi.org/10.1016/j.ssci.2018.11.001.
Azimi, R., S. Lee, S. M. AbouRizk, and A. Alvanchi. 2011. “A framework for an automated and integrated project monitoring and control system for steel fabrication projects.” Autom. Constr. 20 (1): 88–97. https://doi.org/10.1016/j.autcon.2010.07.001.
Bande, R., and E. López-Mourelo. 2015. “The impact of worker’s age on the consequences of occupational accidents: Empirical evidence using Spanish data.” J. Labor Res. 36 (2): 129–174. https://doi.org/10.1007/s12122-015-9199-7.
Bilal, M., L. O. Oyedele, J. Qadir, K. Munir, S. O. Ajayi, O. O. Akinade, H. A. Owolabi, H. A. Alaka, and M. Pasha. 2016. “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.
Dao, B., S. Kermanshachi, J. Shane, S. Anderson, and E. Hare. 2017. “Exploring and assessing project complexity.” J. Constr. Eng. Manage. 143 (3): 04016126 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001275.
Dean, J. 2014. Big data, data mining, and machine learning: Value reaction for business leaders and practitioners. Hoboken, NJ: Wiley.
De Vaus, D. A. 2002. Analyzing social data science. London: SAGE.
El-Abbasy, M. S., A. Senouci, T. Zayed, F. Mirahadi, and L. Parvizsedghy. 2014. “Artificial neural network models for predicting condition of offshore oil and gas pipelines.” Autom. Constr. 45 (Sep): 50–65. https://doi.org/10.1016/j.autcon.2014.05.003.
Esmaeili, B., and M. Hallowell. 2013. “Integration of safety risk data with highway construction schedules.” Constr. Manage. Econ. 31 (6): 528–541. https://doi.org/10.1080/01446193.2012.739288.
Goh, Y., and D. Chua. 2013. “Neural network analysis of construction safety management systems: A case study in Singapore.” Constr. Manage. Econ. 31 (5): 460–470. https://doi.org/10.1080/01446193.2013.797095.
Guo, B. H. W., and T. W. Yiu. 2017. “Developing leading indicators to monitor the safety conditions of construction projects.” J. Manage. Eng. 32 (1): 04015016. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000376.
Guo, B. H. W., T. W. Yiu, V. A. González, and Y. M. Goh. 2016. “Using a pressure-state-practice model to develop safety leading indicators for construction projects.” J. Constr. Eng. Manage. 143 (2): 04016092. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001218.
Hale, A. 2009. “Why safety performance indicators?” Saf. Sci. 47 (4): 479–480. https://doi.org/10.1016/j.ssci.2008.07.018.
Hallowell, M. R., D. Alexander, and J. A. Gambatese. 2017. “Energy-based safety risk assessment: Does magnitude and intensity of energy predict injury severity?” Constr. Manage. Econ. 35 (1–2): 64–77. https://doi.org/10.1080/01446193.2016.1274418.
Han, S., F. Saba, S. Lee, Y. Mohamed, and F. Pena-Mora. 2014. “Toward an understanding of the impact of production pressure on safety performance in construction operations.” Accid. Anal. Prev. 68 (7): 106–116. https://doi.org/10.1016/j.aap.2013.10.007.
Hinton, G., S. Osindero, and Y. W. Teh. 2006. “A fast learning algorithm for deep belief nets.” Neural Comput. 18 (7): 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527.
Hinze, J., M. Hallowell, and K. Baud. 2013a. “Construction-safety best practices and relationships to safety performance.” J. Constr. Eng. Manage. 139 (10): 04013006. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000751.
Hinze, J., S. Thurman, and A. Wehle. 2013b. “Leading indicators of construction safety performance.” Saf. Sci. 51 (1): 23–28. https://doi.org/10.1016/j.ssci.2012.05.016.
Husted, K., and M. Snejina. 2002. “Diagnosing and fighting knowledge-sharing hostility.” Organizational Dyn. 31 (1): 60–73. https://doi.org/10.1016/S0090-2616(02)00072-4.
IEEE Computer Society. 2010. IEEE standard for modeling and simulation high level architecture (HLA): Framework and rules. IEEE 1516. Piscataway, NJ: IEEE.
Jannadi, O. A., and M. S. Bu-Khamsin. 2002. “Safety factors considered by industrial contractors in Saudi Arabia.” Build. Environ. 37 (5): 539–547. https://doi.org/10.1016/S0360-1323(01)00056-7.
Jiang, Z., D. Fang, and M. Zhang. 2015. “Understanding the causation of construction workers’ unsafe behaviors based on system dynamics modeling.” J. Manage. Eng. 31 (6): 04014099. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000350.
jOOL. 2018. “jOOλ.” Accessed October 26, 2018. https://github.com/jOOQ/jOOL.
Khanzode, V. V., J. Maiti, and P. K. Ray. 2012. “Occupational injury and accident research: A comprehensive review.” Saf. Sci. 50 (5): 1355–1367. https://doi.org/10.1016/j.ssci.2011.12.015.
Kulkarni, P.S., S. N. Londhe, and M. C. Deo. 2012. “Artificial neural networks for construction management: A review.” J. Soft Comput. Civ. Eng. 1 (2): 70–88. https://doi.org/10.22115/scce.2017.49580.
Lee, H., H. Kim, M. Park, E. A. L. Teo, and K. Lee. 2012. “Construction risk assessment using site influence factors.” J. Comput. Civ. Eng. 26 (3): 319–330. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000146.
Lingard, H., M. Hallowell, R. Salas, and P. Pirzadeh. 2017. “Leading or lagging? Temporal analysis of safety indicators on a large infrastructure construction project.” Saf. Sci. 91 (Jan): 206–220. https://doi.org/10.1016/j.ssci.2016.08.020.
Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. 2011. Big data: The next frontier for innovation, competition, and productivity. New York: McKinsey & Company.
Marle, F., and L.-A. Vidal. 2016. Managing complex, high risk projects: A guide to basic and advanced project management. London: Springer.
Mitropoulos, P., T. S. Abdelhamid, and G. A. Howell. 2005. “Systems model of construction accident causation.” J. Constr. Eng. Manage. 131 (7): 816–825. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(816).
Mohammadi, A., M. Tavakolan, and Y. Khosravi. 2018. “Factors influencing safety performance on construction projects: A review.” Saf. Sci. 109 (2018): 382–397. https://doi.org/10.1016/j.ssci.2018.06.017.
Neuroph. 2018. “Neuroph: Java neural network framework.” Accessed October 26, 2018. http://neuroph.sourceforge.net/index.html.
Patel, D. A., and K. N. Jha. 2014. “Neural network approach for safety climate prediction.” J. Manage. Eng. 31 (6): 05014027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000348.
Pereira, E., S. Han, and S. Abourizk. 2018a. “Integrating case-based reasoning and simulation modeling for testing strategies to control safety performance.” J. Comput. Civ. Eng. 32 (6): 04018047. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000792.
Pereira, E., S. Han, S. Abourizk, and U. Hermann. 2017. “Empirical testing for use of safety related measures at the organizational level to assess and control the on-site risk level.” J. Constr. Eng. Manage. 143 (6): 05017004. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001303.
Pereira, E., U. Hermann, S. Han, and S. Abourizk. 2018b. “Case-based reasoning approach for assessing safety performance using safety-related measures.” J. Constr. Eng. Manage. 144 (9): 04018088. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001546.
Podgórski, D. 2015. “Measuring operational performance of OSH management system: A demonstration of AHP-based selection of leading key performance indicators.” Saf. Sci. 73 (Mar): 146–166. https://doi.org/10.1016/j.ssci.2014.11.018.
Poh, C. Q. X., C. U. Ubeynarayana, and Y. M. Goh. 2018. “Safety leading indicators for construction sites: A machine learning approach.” Autom. Constr. 93 (Sep): 375–386. https://doi.org/10.1016/j.autcon.2018.03.022.
Portico RTI. 2018. “The poRTIco project.” Accessed October 26, 2018. http://www.porticoproject.org/.
Salas, R., and M. Hallowell. 2016. “Predictive validity of safety leading indicators: Empirical assessment in the oil and gas sector.” J. Constr. Eng. Manage. 142 (10): 04016052. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001167.
Saleh, J. H., E. A. Saltmarsh, F. M. Favarò, and L. Brevault. 2013. “Accident precursors, near misses, and warning signs: Critical review and formal definitions within the framework of discrete event systems.” Reliab. Eng. Syst. Saf. 114 (1): 148–154. https://doi.org/10.1016/j.ress.2013.01.006.
Saunders, L. W., B. M. Kleiner, A. P. McCoy, K. P. Ellis, T. Smith-Jackson, and C. Wernz. 2017. “Developing an inter-organizational safety climate instrument for the construction industry.” Saf. Sci. 98 (Oct): 17–24. https://doi.org/10.1016/j.ssci.2017.04.003.
Shrestha, S., X. Fang, D. Wre, and W. C. Zech. 2014. “What should be the 95th percentile rainfall event depths?” J. Irrig. Drain. Eng. 140 (1): 06013002. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000658.
Soibelman, L., and H. Kim. 2002. “Data preparation process for construction knowledge generation through knowledge discovery in databases.” J. Comput. Civ. Eng. 16 (1): 39–48. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:1(39).
Soleimanifar, M., X. Shen, M. Lu, and I. Nikolaidis. 2014. “Applying received signal strength based methods for indoor positioning and tracking in construction applications.” Can. J. Civ. Eng. 41 (8): 703–716. https://doi.org/10.1139/cjce-2013-0433.
Sterman, J. D. 1988. “Deterministic chaos in models of human behavior: Methodological issues and experimental results.” Syst. Dyn. Rev. 4 (1–2): 148–178. https://doi.org/10.1002/sdr.4260040109.
Tixier, A. J.-P., M. R. Hallowell, B. Rajagopalan, and D. Bowman. 2016. “Application of machine learning to construction injury prediction.” Autom. Constr. 69 (Sep): 102–114. https://doi.org/10.1016/j.autcon.2016.05.016.
US Department of Labor. 2017. “Commonly used statistics.” Accessed October 30, 2018. https://www.osha.gov/oshstats/commonstats.html.
Wibowo, A. 2008. “Discussion of float consumption impact on cost and schedule in the construction industry.” J. Constr. Eng. Manage. 134 (10): 828–829. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:10(828).
Wu, X., Q. Liu, L. Zhang, M. J. Skibniewski, and Y. Wang. 2015. “Prospective safety performance evaluation on construction sites.” Accid. Anal. Prev. 78 (May): 58–72. https://doi.org/10.1016/j.aap.2015.02.003.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 1 • January 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Dec 3, 2018
Accepted: May 2, 2019
Published online: Oct 31, 2019
Published in print: Jan 1, 2020
Discussion open until: Mar 31, 2020
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.