MOCAP and AI-Based Automated Physical Demand Analysis for Workplace Safety
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 7
Abstract
Worker safety and productivity and the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application of ergonomics and the identification of the connections between workers and assigned tasks have led to a decrease in worker injuries and discomfort, beneficial effects on productivity, and a reduction in project costs. Nevertheless, workers in the construction area are often subjected to awkward body postures and repetitive motions that cause musculoskeletal disorders, in turn leading to delays in production. As a systematic and widely used procedure that generates a final document or form, physical demand analysis (PDA) assesses the health and safety of workers engaged in construction or manufacturing activities and proactively evaluates ergonomic risks. However, to gather the necessary information, traditional PDA methods require ergonomists to spend significant time observing and interviewing workers. To increase the speed and accuracy of PDA, this study focuses on developing a systematic PDA framework to automatically fill a posture-based PDA form and address the physiological aspects of task demands. In contrast to the traditional observation-based approach, the proposed framework uses a motion capture (MOCAP) system and a rule-based expert system to obtain joint angles and body segment positions in different work situations, convert the measurements to objective identification of activities and their frequencies, and then automatically populate the PDA forms. The framework is tested and validated in both laboratory and on-site environments by comparing the generated forms with PDA forms filled out by ergonomists. The results indicate that the MOCAP-/AI-based automated PDA framework successfully improves the performance of PDA in terms of accuracy, consistency, and time consumption. Ultimately, this framework can aid in the design of job tasks and work environments with the goal of promoting health, safety, and productivity in the workplace.
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Data Availability Statement
The corresponding author will provide any or all of the data, models, or algorithms that support the conclusions of this work upon reasonable request. The PDA form newly developed as part of this study is available in Fig. 5. The program code, meanwhile, is published on GitHub at https://github.com/xinmingliUofA/MOCAP-AI-based-Automated-PDA. Excerpts of the data appear in this manuscript, while any study data not appearing in this manuscript can be made available by the corresponding author upon reasonable request.
Acknowledgments
This study is partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance program (file no. ALLRP 567205-21) and by the NSERC Discovery program (file no. RGPIN-2019-04585). All findings and conclusions expressed in this paper are those of the authors and do not reflect those of the contributors.
References
Al-Aidaroos, K. M., A. A. Bakar, and Z. Othman. 2010. “Naive Bayes variants in classification learning.” In Proc., 2010 Int. Conf. on Information Retrieval & Knowledge Management (CAMP), 276–281. New York: IEEE.
Albert, J. A., V. Owolabi, A. Gebel, C. M. Brahms, U. Granacher, and B. Arnrich. 2020. “Evaluation of the pose tracking performance of the Azure Kinect and Kinect v2 for gait analysis in comparison with a gold standard: A pilot study.” Sensors 20 (18): 5104. https://doi.org/10.3390/s20185104.
Antwi-Afari, M. F., H. Li, W. Umer, Y. Yu, and X. Xing. 2020. “Construction activity recognition and ergonomic risk assessment using a wearable insole pressure system.” J. Constr. Eng. Manage. 146 (7): 04020077. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001849.
Astrand, P. O., K. Rodahl, H. A. Dahl, and S. B. Stromme. 2003. Textbook of work physiology: Physiological bases of exercise. New York: McGraw-Hill.
Austin, D., T. Gabbett, and D. Jenkins. 2011. “The physical demands of Super 14 rugby union.” J. Sci. Med. Sport 14 (3): 259–263. https://doi.org/10.1016/j.jsams.2011.01.003.
Bloomfield, J., R. Polman, and P. O’Donoghue. 2007. “Physical demands of different positions in FA premier league soccer.” J. Sports Sci. Med. 6 (1): 63–70.
British Columbia Municipal Safety Association. 2019. “Physical job demand analysis.” Accessed March 21, 2023. http://www.bcmsa.ca/resources/physical-job-demand-analysis.
Chandler, P. T., S. J. Pinder, J. D. Curran, and T. J. Gabbett. 2014. “Physical demands of training and competition in collegiate netball players.” J. Strength Cond. Res. 28 (10): 2732–2737. https://doi.org/10.1519/JSC.0000000000000486.
CPWR (Center for Construction Research and Training). 2013. “Work-related musculoskeletal disorders resulting in days away from work in construction, by body part, 2003–2010.” Accessed March 21, 2023. http://www.cpwr.com/publications/constructionchart-book.
Denyer, D., and D. Tranfield. 2009. “Producing a systematic review.” In The Sage handbook of organizational research methods, 671–689. New York: SAGE.
Diaz-Arias, A., and D. Shin. 2023. “ConvFormer: Parameter reduction in transformer models for 3D human pose estimation by leveraging dynamic multi-headed convolutional attention.” Preprint, submitted April 4, 2023. https://arxiv.org/abs/2304.02147.
Donisi, L., G. Cesarelli, N. Pisani, A. M. Ponsiglione, C. Ricciardi, and E. Capodaglio. 2022. “Wearable sensors and artificial intelligence for physical ergonomics: A systematic review of literature.” Diagnostics 12 (12): 3048. https://doi.org/10.3390/diagnostics12123048.
Ergonomics Plus Inc. 2023. “A step-by step guide to using the NIOSH lifting equation for single tasks.” Accessed March 21, 2023. http://ergo-plus.com/niosh-lifting-equation-single-task/.
Fan, C., Q. Mei, Q. Yang, and X. Li. 2022. “Computer-vision based rapid entire body analysis (REBA) estimation.” In Proc., Modular and offsite construction (MOC) Summit Proc., 90–97. Edmonton, AB, Canada: Univ. of Alberta. https://doi.org/10.29173/mocs269.
Fleisig, G. S., J. S. Slowik, D. Wassom, Y. Yanagita, J. Bishop, and A. Diffendaffer. 2022. “Comparison of marker-less and marker-based motion capture for baseball pitching kinematics.” Sports Biomech. 1–10. https://doi.org/10.1080/14763141.2022.2076608.
Gagne, R. 2010. “Developing a legally compliant job demands analysis.” Accessed March 21, 2023. https://fit2wrk.com/wp-content/uploads/2015/08/ARTICLE_Fit2wrk_ClinicalEd_vol1-04.pdf.
Gamra, M. B., and M. A. Akhloufi. 2021. “A review of deep learning techniques for 2D and 3D human pose estimation.” Image Vision Comput. 114: 104282. https://doi.org/10.1016/j.imavis.2021.104282.
Gao, Z., Y. Yu, Y. Zhou, and S. Du. 2015. “Leveraging two kinect sensors for accurate full-body motion capture.” Sensors 15 (9): 24297–24317. https://doi.org/10.3390/s150924297.
Gašová, M., M. Gašo, and A. Štefánik. 2017. “Advanced industrial tools of ergonomics based on Industry 4.0 concept.” Procedia Eng. 192 (Jan): 219–224. https://doi.org/10.1016/j.proeng.2017.06.038.
Getty, R. L. 1994. “Physical demands of work are the common reference for an integrated ergonomics program.” In Proc., Human Factors and Ergonomics Society 38th Annual Meeting, 683–687. London: SAGE. https://doi.org/10.1177/154193129403801031.
Gisev, N., J. S. Bell, and T. F. Chen. 2013. “Interrater agreement and interrater reliability: Key concepts, approaches, and applications.” Res. Social Administrative Pharm. 9 (3): 330–338. https://doi.org/10.1016/j.sapharm.2012.04.004.
Golabchi, A., X. Guo, M. Liu, S. U. Han, S. H. Lee, and S. AbouRizk. 2018. “An integrated ergonomics framework for evaluation and design of construction operations.” Autom. Constr. 95 (Nov): 72–85. https://doi.org/10.1016/j.autcon.2018.08.003.
Golabchi, A., S. Han, and A. R. Fayek. 2016. “A fuzzy logic approach to posture-based ergonomic analysis for field observation and assessment of construction manual operations.” Can. J. Civ. Eng. 43 (4): 294–303. https://doi.org/10.1139/cjce-2015-0143.
Golabchi, A., S. Han, A. R. Fayek, and S. AbouRizk. 2017. “Stochastic modelling for assessment of human perception and motion sensing errors in ergonomic analysis.” J. Comput. Civ. Eng. 31 (4): 04017010. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000655.
Golabchi, A., S. Han, J. Seo, S. Han, S. Lee, and M. Al-Hussein. 2015. “An automated biomechanical simulation approach to ergonomic job analysis for workplace design.” J. Constr. Eng. Manage. 141 (8): 04015020. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000998.
Grosan, C., and A. Abraham. 2011. “Rule-based expert systems.” In Intelligent systems reference library, 149–185. Berlin: Springer.
Guo, Z. X., and W. K. Wong. 2013. “Fundamentals of artificial intelligence techniques for apparel management applications.” In Optimizing decision making in the apparel supply chain using Artificial Intelligence (AI): From production to retail, 13–40. Amsterdam, Netherlands: Elsevier.
Hachaj, T., and M. R. Ogiela. 2014. “Rule-based approach to recognizing human body poses and gestures in real time.” Multimedia Syst. 20: 81–99. https://doi.org/10.1007/s00530-013-0332-2.
Hamzeh, F., H. Abou-Ibrahim, A. Daou, M. Faloughi, and N. Kawwa. 2019. “3D visualization techniques in the AEC industry: The possible uses of holography.” J. Inf. Technol. Construct. 24 (Jun): 239–255. https://doi.org/10.36680/j.itcon.2019.013.
Hamzeh, F., V. A. González, L. F. Alarcon, and S. Khalife. 2021. “Lean construction 4.0: Exploring the challenges of development in the AEC industry.” In Proc., 29th Annual Conf. of the Int. Group for Lean Construction (IGLC), 207–216. Lima, Peru: Pontificia Universidad Católica del Perú. https://doi.org/10.24928/2021/0181.
Hayes-Roth, F. 1985. “Rule-based systems.” Commun. ACM 28 (9): 921–932. https://doi.org/10.1145/4284.4286.
Hignett, S., and L. McAtamney. 2000. “Rapid entire body assessment (REBA).” Appl. Ergon. 31 (2): 201–205. https://doi.org/10.1016/S0003-6870(99)00039-3.
Humadi, A., M. Nazarahari, R. Ahmad, and H. Rouhani. 2021. “In-field instrumented ergonomic risk assessment: Inertial measurement units versus Kinect V2.” Int. J. Ind. Ergon. 84: 103147. https://doi.org/10.1016/j.ergon.2021.103147.
Hwang, S., and S. Lee. 2017. “Wristband-type wearable health devices to measure construction workers’ physical demands.” Autom. Constr. 83 (Nov): 330–340. https://doi.org/10.1016/j.autcon.2017.06.003.
Kanko, R. M., E. K. Laende, E. M. Davis, W. S. Selbie, and K. J. Deluzio. 2021. “Concurrent assessment of gait kinematics using marker-based and markerless motion capture.” J. Biomech. 127 (Oct): 110665. https://doi.org/10.1016/j.jbiomech.2021.110665.
Karatsidis, A., M. Jung, H. M. Schepers, G. Bellusci, M. de Zee, P. H. Veltink, and M. S. Andersen. 2019. “Musculoskeletal model-based inverse dynamic analysis under ambulatory conditions using inertial motion capture.” Med. Eng. Phys. 65 (Mar): 68–77. https://doi.org/10.1016/j.medengphy.2018.12.021.
Kim, M., and S. Lee. 2022. “Fusion poser: 3D human pose estimation using sparse IMUs and head trackers in real time.” Sensors 22 (13): 4846. https://doi.org/10.3390/s22134846.
Kim, S., and M. A. Nussbaum. 2013. “Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks.” Ergonomics 56 (2): 314–326. https://doi.org/10.1080/00140139.2012.742932.
Kumar, A., J. Naughton, and J. M. Patel. 2015. “Learning generalized linear models over normalized data.” In Proc., 2015 ACM SIGMOD Int. Conf. on Management of Data, 1969–1984. Melbourne, VIC, Australia: Association for Computing Machinery.
LeBreton, J. M., and J. L. Senter. 2008. “Answers to 20 questions about interrater reliability and interrater agreement.” Organ. Res. Methods 11 (4): 815–852. https://doi.org/10.1177/1094428106296642.
Lee, S., L. Liu, R. Radwin, and J. Li. 2021. “Machine learning in manufacturing ergonomics: Recent advances, challenges, and opportunities.” IEEE Rob. Autom. Lett. 6 (3): 5745–5752. https://doi.org/10.1109/LRA.2021.3084881.
Li, X. 2017. Ergonomic risk assessment in construction manufacturing facilities. Edmonton, AB, Canada: Univ. of Alberta. https://doi.org/10.7939/R3PZ51Z8F.
Li, X., M. Gül, and M. Al-Hussein. 2019. “An improved physical demand analysis framework based on ergonomic risk assessment tools for the manufacturing industry.” Int. J. Ind. Ergon. 70 (Mar): 58–69. https://doi.org/10.1016/j.ergon.2019.01.004.
Lind, C. M., F. Abtahi, and M. Forsman. 2023. “Wearable motion capture devices for the prevention of work-related musculoskeletal disorders in ergonomics—An overview of current applications, challenges, and future opportunities.” Sensors 23 (9): 4259. https://doi.org/10.3390/s23094259.
Lind, C. M., M. Forsman, and L. M. Rose. 2020. “Development and evaluation of RAMP II—A practitioner’s tool for assessing musculoskeletal disorder risk factors in industrial manual handling.” Ergonomics 63 (4): 477–504. https://doi.org/10.1080/00140139.2019.1710576.
Liu, W., Q. Meng, Z. Li, and X. Hu. 2021. “Applications of computer vision in monitoring the unsafe behavior of construction workers: Current status and challenges.” Buildings 11 (9): 409. https://doi.org/10.3390/buildings11090409.
Luinge, H. J., and P. H. Veltink. 2005. “Measuring orientation of human body segments using miniature gyroscopes and accelerometers.” Med. Biol. Eng. Comput. 43 (2): 273–282. https://doi.org/10.1007/BF02345966.
Mahesh, B. 2020. “Machine learning algorithms: A review.” Int. J. Sci. Res. 9 (1): 381–386.
MassirisFernández, M., J. Á. Fernández, J. M. Bajo, and C. A. Delrieux. 2020. “Ergonomic risk assessment based on computer vision and machine learning.” Comput. Ind. Eng. 149 (Nov): 106816. https://doi.org/10.1016/j.cie.2020.106816.
McAtamney, L., and E. N. Corlett. 1993. “RULA: A survey method for investigation of work related upper limb disorders.” Appl. Ergon. 24 (2): 91–99. https://doi.org/10.1016/0003-6870(93)90080-S.
Michalsik, L. B., and P. Aagaard. 2015. “Physical demands in elite team handball: Comparisons between male and female players.” J. Sports Med. Phys. Fitness 55 (9): 878–891.
Minnesota Occupational Health. 2012. “Job task analysis: Physical demands.” Accessed March 21, 2023. https://www.ramseycounty.us/sites/default/files/Departments/Human%20Resources%20Dept/Land%20Surveyor_Civil%20Eng%20Tech%20JTA%20Report%205%2012%20%28rev1%29-1bd610e5377a4760800f8e558d3e0f6a.pdf.
Moeslund, T. B., A. Hilton, and V. Krüger. 2006. “A survey of advances in vision-based human motion capture and analysis.” Comput. Vision Image Understanding 104 (2–3): 90–126. https://doi.org/10.1016/j.cviu.2006.08.002.
Murthy, S. K. 1998. “Automatic construction of decision trees from data: A multi-disciplinary survey.” Data Min. Knowl. Discovery 2 (4): 345–389. https://doi.org/10.1023/A:1009744630224.
Myn, U., M. Link, and M. Awinda. 2015. “Xsens MVN user manual.” Accessed March 21, 2023. https://www.xsens.com/hubfs/Downloads/usermanual/MVN_User_Manual.pdf.
OHCOW (Occupational Health Clinics for Ontario Workers). 1998. “Guidelines to implementing and performing physical demand analysis handbook.” Accessed March 21, 2023. http://www.mtpinnacle.com/pdfs/pdamanualbook.pdf.
OHCOW (Occupational Health Clinics for Ontario Workers). 2021. “Physical demands description (PDD) toolkit.” Accessed March 21, 2023. https://www.ohcow.on.ca/injury-prevention/workplace_ergonomics/physical-demands-description-pdd/.
Paneru, S., and I. Jeelani. 2021. “Computer vision applications in construction: Current state, opportunities & challenges.” Autom. Constr. 132 (Dec): 103940. https://doi.org/10.1016/j.autcon.2021.103940.
Paulich, M., M. Schepers, N. Rudigkeit, and G. Bellusci. 2018. Xsens MTw Awinda: Miniature wireless inertial-magnetic motion tracker for highly accurate 3D kinematic applications. Enschede, Netherlands: Xsens.
Ren, L., R. K. Jones, and D. Howard. 2008. “Whole body inverse dynamics over a complete gait cycle based only on measured kinematics.” J. Biomech. 41 (12): 2750–2759. https://doi.org/10.1016/j.jbiomech.2008.06.001.
Reuter, A. S., and M. Schindler. 2023. “Motion capture systems and their use in educational research: Insights from a systematic literature review.” Educ. Sci. 13 (2): 167. https://doi.org/10.3390/educsci13020167.
Roberts, D., et al. 2020. “Vision-based construction worker activity analysis informed by body posture.” J. Comput. Civ. Eng. 34 (4): 04020017. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000898.
Rodrigues, P. B., Y. Xiao, Y. E. Fukumura, M. Awada, A. Aryal, B. Becerik-Gerber, G. Lucas, and S. C. Roll. 2022. “Ergonomic assessment of office worker postures using 3D automated joint angle assessment.” Adv. Eng. Inf. 52 (Apr): 101596. https://doi.org/10.1016/j.aei.2022.101596.
Roetenberg, D., H. Luinge, and P. Slycke. 2009. “Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors.” Accessed March 21, 2023. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=cc2ba84a4d6e06fd85ad434f5b1a8545c1cc993c.
Roetenberg, D., H. J. Luinge, C. T. M. Baten, and P. H. Veltink. 2005. “Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation.” IEEE Trans. Neural Syst. Rehabil. Eng. 13 (3): 395–405. https://doi.org/10.1109/TNSRE.2005.847353.
Russell, J. L., B. D. McLean, F. M. Impellizzeri, D. S. Strack, and A. J. Coutts. 2021. “Measuring physical demands in basketball: An explorative systematic review of practices.” Sports Med. 51 (1): 81–112. https://doi.org/10.1007/s40279-020-01375-9.
Ryu, J. H., L. Zhang, C. T. Haas, and E. Abdel-Rahman. 2018. “Motion data based construction worker training support tool: Case study of masonry work.” In Proc., 35th Int. Symp. on Automation and Robotics in Construction and Mining and the International AEC/FM Hackathon: The Future of Building Things Country (ISAARC), 1079–1084. Berlin: Technische Universität. https://doi.org/10.22260/ISARC2018/0150.
Seo, J., and S. Lee. 2021. “Automated postural ergonomic risk assessment using vision-based posture classification.” Autom. Constr. 128 (Aug): 103725. https://doi.org/10.1016/j.autcon.2021.103725.
Sydenham, P. H., and R. Thorn. 2005. Vol. 2 of Handbook of measuring system design. Chichester, UK: Wiley.
Varshney, N., B. Bakariya, A. K. S. Kushwaha, and M. Khare. 2021. “Rule-based multi-view human activity recognition system in real time using skeleton data from RGB-D sensor.” Soft Comput. 27: 1–17. https://doi.org/10.1007/s00500-021-05649-w.
Wang, D., F. Dai, and X. Ning. 2015. “Risk assessment of work-related musculoskeletal disorders in construction: State-of-the-art review.” J. Constr. Eng. Manage. 141 (6): 04015008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000979.
Wang, J. 2022. Ergonomic-centric methods for workplace design in industrialized construction. Edmonton, AB, Canada: Univ. of Alberta.
Wang, J., X. Li, S. Han, and M. Al-Hussein. 2023. “3D standard motion time-based ergonomic risk analysis for workplace design in modular construction.” Autom. Constr. 147 (Mar): 104738. https://doi.org/10.1016/j.autcon.2022.104738.
WCBA (Workers’ Compensation Board of Alberta). 2014. “Employer–physical demands analysis.” Accessed April 2, 2024. https://www.wcb.ab.ca/assets/pdfs/employers/C545.pdf.
WCBA (Workers’ Compensation Board of Alberta). 2015. “Millard health rehabilitation centre workshops.” Accessed April 2, 2024. https://www.wcb.ab.ca/millard-treatment-centre/workshops/.
WCBA (Workers’ Compensation Board of Alberta). 2023. “Employer: Physical demands analysis.” Accessed March 21, 2023. https://www.wcb.ab.ca/assets/pdfs/employers/C545.pdf.
Weinberger, K. Q., and L. K. Saul. 2009. “Distance metric learning for large margin nearest neighbor classification.” J. Mach. Learn. Res. 10 (9): 207–244.
WISHA (Washington Industrial Safety and Health Act). 2010. “Washington State WISHA screening tool (Modified).” Accessed March 21, 2023. https://health.usf.edu/publichealth/tbernard/∼/media/0400BFD4EDAB4C43A4695B33F9D6D005.ashx.
Workplace Safety and Prevention Services. 2020. “Physical demand analysis (instruction).” Accessed March 21, 2023. https://www.wsps.ca/resource -hub/guides/physical-demands-analysis-instruction.
Workplace Safety North. 2023. “Physical demands analysis.” Accessed March 21, 2023. https://www.workplacesafetynorth.ca/subsite/msds-priority-hazards/physical-demands-analysis.
WorksafeBC. 2023. “Job demands analysis.” Accessed March 21, 2023. https://www.worksafebc.com/en/resources/health-care-providers/forms/job-demands-analysis-report-form-83d224?lang=en.
WSIB (Workplace Safety and Insurance Board). 2019. “Completing the physical demands information form.” Accessed March 21, 2023. https://www.wsib.ca/sites/default/files/2019-02/pdif_1.pdf.
Xsens Technologies B.V. 2020. “MVN user manual. MVN manual, April, 162.” Accessed April 16, 2023. https://www.cleancss.com/user-manuals/QIL/MTW2-3A7G6.
Ye, D., Y. Xie, W. Chen, Z. Zhou, and H. Foroosh. 2023. “LPFormer: LiDAR pose estimation transformer with multi-task network.” Preprint, submitted June 23, 2023. https://arxiv.org/abs/2306.12525.
Yu, Y., X. Yang, H. Li, X. Luo, H. Guo, and Q. Fang. 2019. “Joint-level vision-based ergonomic assessment tool for construction workers.” J. Constr. Eng. Manage. 145 (5): 04019025. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001647.
Zainordin, F. D., H. Y. Lee, N. A. Sani, Y. M. Wong, and C. S. Chan. 2012. “Human pose recognition using Kinect and rule-based system.” In World Automation Congress 2012, 1–6. New York: IEEE.
Zhang, H., X. Yan, and H. Li. 2018. “Ergonomic posture recognition using 3D view-invariant features from single ordinary camera.” Autom. Constr. 94 (Oct): 1–10. https://doi.org/10.1016/j.autcon.2018.05.033.
Zhang, J. T., A. C. Novak, B. Brouwer, and Q. Li. 2013. “Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics.” Physiol. Meas. 34 (8): N63. https://doi.org/10.1088/0967-3334/34/8/N63.
Zheng, C., W. Wu, C. Chen, T. Yang, S. Zhu, J. Shen, N. Kehtarnavaz, and M. Shah. 2023. “Deep learning-based human pose estimation: A survey.” ACM Comput. Surv. 56 (1): 1–37. https://doi.org/10.1145/3603618.
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Journal of Construction Engineering and Management
Volume 150 • Issue 7 • July 2024
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© 2024 American Society of Civil Engineers.
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Received: Mar 26, 2023
Accepted: Jan 9, 2024
Published online: Apr 24, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 24, 2024
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