Biomechanical Analysis in Construction Engineering and Management Research: A Review
Publication: Construction Research Congress 2022
ABSTRACT
In construction, the high amount of work musculoskeletal disorders (WMSD) continues to be a major source of concern for practitioners and researchers. In response to this concern, construction stakeholders have increasingly utilized advanced simulation technics to investigate musculoskeletal risks that contribute to WMSDs and inform ergonomic changes to work environments and operations. Specifically, studies at the intersection of biomechanical analysis (BA) and construction worker safety and health have increased recently. While using BA tools in construction research and practice is commendable, synthesized information on the application and utility of these tools is lacking. To enhance the quality of research within this domain, it is essential to provide a synthesis that summarizes BA efficacy in examining working postural faults in construction activities, their relevance in construction research, data collection limitations, and future directions. Utilizing a three-phase review process, the present study queried SCOPUS database, which helped identify 15 articles adherent to the pre-defined inclusion criteria. Results from the present study suggest that research utilizing BA in construction more than doubled in the last 10 years. To improve the quality and generalizability of biomechanics and ergonomics research in construction safety and health management, future construction studies should: assess under-researched construction activities with high WMSD prevalence; include a more significant number of experienced workers; increase participants diversity (female and non-Caucasian) in experiments; and utilize advanced Artificial Intelligence algorithms to build robust and precise biomechanical models which will be useful in analyzing the growing wealth of high-dimensional data.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Bao, R., Komatsu, R., Miyagusuku, R., Chino, M., Yamashita, A., and Asama, H. (2019). “Cost-effective and robust visual based localization with consumer-level cameras at construction sites.” 2019 IEEE 8th Glob. Conf. Consum. Electron. GCCE 2019, 983–985. https://doi.org/10.1109/GCCE46687.2019.9015417.
BLS (US Bureau of Labor Statistics). (2018). Back injuries prominent in work-related musculoskeletal disorder cases in 2016. U.S. Bureau of Labor Statistics. https://www.bls.gov/opub/ted/2018/back-injuries-prominent-in-work-related-musculoskeletal-disorder-cases-in-2016.htm.
Buchholz, B., Park, J. S., Gold, J. E., and Punnett, L. (2008). “Subjective ratings of upper extremity exposures: Inter-method agreement with direct measurement of exposures.” Http://Dx.Doi.Org/10.1080/00140130801915220, 51(7), 1064–1077. https://doi.org/10.1080/00140130801915220.
Chaffin, D. B., Andersson, G. B., and Martin, B. J. (2006). Occupational Biomechanics. John wiley & sons.
Chaffin, D. B., and Erig, M. (1991). “Three-Dimensional Biomechanical Static Strength Prediction Model Sensitivity to Postural and Anthropometric Inaccuracies.” Iie Trans., 23(3), 215–227. https://doi.org/10.1080/07408179108963856.
Chi, S., and Caldas, C. H. (2011). “Automated Object Identification Using Optical Video Cameras on Construction Sites.” Comput. Civ. Infrastruct. Eng., 26(5), 368–380. https://doi.org/10.1111/J.1467-8667.2010.00690.X.
Chu, W., Han, S., Luo, X., and Zhu, Z. (2020). “Monocular Vision–Based Framework for Biomechanical Analysis or Ergonomic Posture Assessment in Modular Construction.” J. Comput. Civ. Eng., 34(4), 04020018. https://doi.org/10.1061/(asce)cp.1943-5487.0000897.
CPWR (Center for Construction Research and Training). (2016). “The Construction Chart Book.” In Chart.
Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., and Ameyaw, E. E. (2020). “Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities.” In Automation in Construction (Vol. 112, p. 103081). Elsevier B.V. https://doi.org/10.1016/j.autcon.2020.103081.
Delhi, V. S. K., Sankarlal, R., and Thomas, A. (2020). “Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques.” Front. Built Environ., 6, 136. https://doi.org/10.3389/FBUIL.2020.00136.
Delp, S. L., Loan, J. P., Hoy, M. G., Zajac, F. E., Topp, E. L., and Rosen, J. M. (1990). “An Interactive Graphics-Based Model of the Lower Extremity to Study Orthopaedic Surgical Procedures.” IEEE Trans. Biomed. Eng., 37(8), 757–767. https://doi.org/10.1109/10.102791.
Eaves, S., Gyi, D. E., and Gibb, A. G. F. (2016). “Building healthy construction workers: Their views on health, wellbeing and better workplace design.” Appl. Ergon., 54, 10–18. https://doi.org/10.1016/J.APERGO.2015.11.004.
Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T. M., and An, W. (2018). “Detecting non-hardhat-use by a deep learning method from far-field surveillance videos.” Autom. Constr., 85, 1–9. https://doi.org/10.1016/J.AUTCON.2017.09.018.
Fang, Q., Li, H., Luo, X., Li, C., and An, W. (2020). “A sematic and prior-knowledge-aided monocular localization method for construction-related entities.” Comput. Civ. Infrastruct. Eng., 35(9), 979–996. https://doi.org/10.1111/MICE.12541.
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (pp. 580–587). http://arxiv.
Golabchi, A., Han, S., Seo, J., Han, S., Lee, S., and Al-Hussein, M. (2015). “An Automated Biomechanical Simulation Approach to Ergonomic Job Analysis for Workplace Design.” J. Constr. Eng. Manag., 141(8), 04015020. https://doi.org/10.1061/(asce)co.1943-7862.0000998.
Halilaj, E., Rajagopal, A., Fiterau, M., Hicks, J. L., Hastie, T. J., and Delp, S. L. (2018). “Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.” In Journal of Biomechanics (Vol. 81, pp. 1–11). Elsevier Ltd. https://doi.org/10.1016/j.jbiomech.2018.09.009.
Hall, A. D., La Delfa, N. J., Loma, C., and Potvin, J. R. (2021). “A comparison between measured female linear arm strengths and estimates from the 3D Static Strength Prediction Program (3DSSPP).” Appl. Ergon., 94, 103415. https://doi.org/10.1016/J.APERGO.2021.103415.
Huang, C., Kim, W., Zhang, Y., and Xiong, S. (2020). “Development and Validation of a Wearable Inertial Sensors-Based Automated System for Assessing Work-Related Musculoskeletal Disorders in the Workspace.” J. Environ. Res. Public Heal., 17(17), 1–15. https://doi.org/10.3390/ijerph17176050.
Kim, J., Hwang, J., Chi, S., and Seo, J. O. (2020). “Towards database-free vision-based monitoring on construction sites: A deep active learning approach.” Autom. Constr., 120, 103376. https://doi.org/10.1016/J.AUTCON.2020.103376.
Marras, W. S., and Granata, K. P. (1997). “Spine loading during trunk lateral bending motions.” J. Biomech., 30(7), 697–703. https://doi.org/10.1016/S0021-9290(97)00010-9.
Mehrizi, R., Peng, X., Xu, X., Zhang, S., and Li, K. (2019). “A Deep Neural Network-based method for estimation of 3D lifting motions.” J. Biomech., 84, 87–93. https://doi.org/10.1016/j.jbiomech.2018.12.022.
Nath, N. D., Akhavian, R., and Behzadan, A. H. (2017). “Ergonomic analysis of construction worker’s body postures using wearable mobile sensors.” Appl. Ergon., 62, 107–117. https://doi.org/10.1016/j.apergo.2017.02.007.
Ning, X., Zhou, J., Dai, B., and Jaridi, M. (2014). “The assessment of material handling strategies in dealing with sudden loading: The effects of load handling position on trunk biomechanics.” Appl. Ergon., 45(6), 1399–1405. https://doi.org/10.1016/j.apergo.2014.03.008.
Occhipiniti, E. (1998). “OCRA: A concise index for the assessment of exposure to repetitive movements of the upper limbs.” Ergonomics, 41(9), 1290–1311. https://doi.org/https://doi.org/10.1080/001401398186315.
Radwin, R. G., Marras, W. S., and Lavender, S. A. (2001). “Biomechanical aspects of work-related musculoskeletal disorders.” Theor. Issues Ergon. Sci., 2(2), 153–217. https://doi.org/10.1080/14639220110102044.
Ray, P. K., Parida, R., and Sarkar, S. (2015). “Ergonomic Analysis of Construction Jobs in India: A Biomechanical Modelling Approach.” Procedia Manuf., 3, 4606–4612. https://doi.org/10.1016/j.promfg.2015.07.542.
Ren, S., He, K., Girshick, R., and Sun, J. (2017). “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE Trans. Pattern Anal. Mach. Intell., 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031.
Seo, J., Starbuck, R., Han, S., Lee, S., and Armstrong, T. J. (2015). “Motion Data-Driven Biomechanical Analysis during Construction Tasks on Sites.” J. Comput. Civ. Eng., 29(4), B4014005. https://doi.org/10.1061/(asce)cp.1943-5487.0000400.
Smale, K. B., Conconi, M., Sancisi, N., Krogsgaard, M., Alkjaer, T., Parenti-Castelli, V., and Benoit, D. L. (2019). “Effect of implementing magnetic resonance imaging for patient-specific OpenSim models on lower-body kinematics and knee ligament lengths.” J. Biomech., 83, 9–15. https://doi.org/10.1016/J.JBIOMECH.2018.11.016.
Teschke, K., Trask, C., Johnson, P., Chow, Y., Village, J., and Koehoorn, M. (2011). “Measuring posture for epidemiology: Comparing inclinometry, observations and self-reports.” Http://Dx.Doi.Org/10.1080/00140130902912811, 52(9), 1067–1078. https://doi.org/10.1080/00140130902912811.
Thordsen, M. L., Kroemer, K. H., and Laubach, L. L. (1972). Human Force Exertions in Aircraft Control Locations. WEBB Assoc. YELLOW SPRINGS OH.
Torgén, M., Winkel, J., Alfredsson, L., Åsa, K., and Stockholm, M. 1 S. G. (1999). “Evaluation of questionnaire-based information on previous physical work loads.” Scand. J. Work. Environ. Health, 25(3), 246–254. https://www.jstor.org/stable/40966894?seq=1#metadata_info_tab_contents.
Umer, W., Li, H., Szeto, G. P. Y., and Wong, A. Y. L. (2017). “Identification of Biomechanical Risk Factors for the Development of Lower-Back Disorders during Manual Rebar Tying.” J. Constr. Eng. Manag., 143(1), 04016080. https://doi.org/10.1061/(asce)co.1943-7862.0001208.
Yan, X., Li, H., Li, A. R., and Zhang, H. (2017). “Wearable IMU-based real-time motion warning system for construction workers’ musculoskeletal disorders prevention.” Autom. Constr., 74, 2–11. https://doi.org/10.1016/j.autcon.2016.11.007.
Yang, X., Li, H., Huang, T., Zhai, X., Wang, F., and Wang, C. (2018). “Computer-Aided Optimization of Surveillance Cameras Placement on Construction Sites.” Comput. Civ. Infrastruct. Eng., 33(12), 1110–1126. https://doi.org/10.1111/MICE.12385.
Yu, Y., Li, H., Umer, W., Dong, C., Yang, X., Skitmore, M., and Wong, A. Y. L. (2019). “Automatic Biomechanical Workload Estimation for Construction Workers by Computer Vision and Smart Insoles.” J. Comput. Civ. Eng., 33(3), 04019010. https://doi.org/10.1061/(asce)cp.1943-5487.0000827.
Yu, Y., Li, H., Yang, X., Kong, L., Luo, X., and Wong, A. Y. (2019). “An automatic and non-invasive physical fatigue assessment method for construction workers.” Autom. Constr., 1–12. https://doi.org/https://doi.org/10.1016/j.autcon.2019.02.020.
Yu, Y., Umer, W., Yang, X., and Antwi-Afari, M. F. (2021). “Posture-related data collection methods for construction workers: A review.” Autom. Constr., 124, 103538. https://doi.org/10.1016/J.AUTCON.2020.103538.
Information & Authors
Information
Published In
History
Published online: Mar 7, 2022
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.