Factors affecting implementation of computer vision-based technologies adopted for monitoring buildings construction projects
Abstract
Construction monitoring in dynamic construction site environments poses significant challenges for construction management. To overcome these challenges, the implementation of computer vision (CV) technologies for construction project monitoring has gained traction. This study focuses on investigating the factors influence the successful implementation of CV technologies in monitoring construction activities within building projects. A comprehensive methodology was employed, including a systematic review of CV technologies implemented in construction and qualitative surveys conducted with construction experts. Additionally, a quantitative questionnaire was developed, and the collected data was analysed using structural equation modelling. The findings reveal the presence of 10 factors categorized into four constructs. Notably, all 10 factors demonstrate high value factor loadings and statistical significance, and among the four constructs (device, jobsite, environment, human), device (0.82) has the highest impact on the implementation of CV-based technologies on the construction site, followed by jobsite condition (0.62), human (0.61), and environment (0.51) came in the last place. By addressing these influential factors and mitigating their effects, construction stakeholders can enhance the implementation of CV technologies for monitoring construction sites. This study contributes valuable insights that inform the implementation and optimization of CV technologies in construction projects, ultimately advancing the field of construction management.
Keyword : automated monitoring, computer vision, factors, construction monitoring, automated technologies, structural equation modelling
How to Cite
Alzubi, K. M., Alaloul, W. S., Al Salaheen, M., Alsaaidah, B., Musarat, M. A., & Qureshi, A. H. (2024). Factors affecting implementation of computer vision-based technologies adopted for monitoring buildings construction projects. Journal of Civil Engineering and Management, 30(7), 600–613. https://doi.org/10.3846/jcem.2024.20951
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Fink, A. (2005). Conducting research literature reviews: From internet to paper (2nd ed.). SAGE Publications.
Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2015). Automated progress monitoring using unordered daily construction photographs and IFC-based building information models. Journal of Computing in Civil Engineering, 29(1), Article 04014025. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000205
Gong, J., & Caldas, C. H. (2011). An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Automation in Construction, 20(8), 1211–1226. https://doi.org/10.1016/j.autcon.2011.05.005
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Ho, D. C. W., Yau, Y., Poon, S. W., & Liusman, E. (2012). Achieving sustainable urban renewal in Hong Kong: Strategy for dilapidation assessment of high rises. Journal of Urban Planning and Development, 138(2), 153–165. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000104
Huang, Y., Hammad, A., & Zhu, Z. (2021). Providing proximity alerts to workers on construction sites using Bluetooth Low Energy RTLS. Automation in Construction, 132, Article 103928. https://doi.org/10.1016/j.autcon.2021.103928
Israel, G. D. (1992). Determining sample size. University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS, Florida.
Khosrowpour, A., Niebles, J. C., & Golparvar-Fard, M. (2014). Vision-based workface assessment using depth images for activity analysis of interior construction operations. Automation in Construction, 48, 74–87. https://doi.org/10.1016/j.autcon.2014.08.003
Konstantinou, E., Lasenby, J., & Brilakis, I. (2019). Adaptive computer vision-based 2D tracking of workers in complex environments. Automation in Construction, 103, 168–184. https://doi.org/10.1016/j.autcon.2019.01.018
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/1743727X.2015.1005806
Luo, H., Xiong, C., Fang, W., Love, P. E., Zhang, B., & Ouyang, X. (2018). Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction, 94, 282–289. https://doi.org/10.1016/j.autcon.2018.06.007
Maalek, R., Lichti, D. D., & Ruwanpura, J. Y. (2019). Automatic recognition of common structural elements from point clouds for automated progress monitoring and dimensional quality control in reinforced concrete construction. Remote Sensing, 11(9), Article 9. https://doi.org/10.3390/rs11091102
McCulloch, B. (1997). Automating field data collection in construction organizations. In Proceeding of the 1997 ASCE Construction Congress (pp. 957–963), Minneapolis, Minesota, USA.
Mneymneh, B. E., Abbas, M., & Khoury, H. (2018). Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications. Frontiers of Engineering Management, 5(2), 227–239. https://doi.org/10.15302/J-FEM-2018071
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Ogunsanya, O. A., Aigbavboa, C. O., Thwala, D. W., & Edwards, D. J. (2022). Barriers to sustainable procurement in the Nigerian construction industry: An exploratory factor analysis. International Journal of Construction Management, 22(5), 861–872. https://doi.org/10.1080/15623599.2019.1658697
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Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. McGraw-Hill Education (UK). https://doi.org/10.4324/9781003117407
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. SAGE Publications.
Qureshi, A. H., Alaloul, W. S., Manzoor, B., Saad, S., Alawag, A. M., & Alzubi, K. M. (2021). Implementation challenges of automated construction progress monitoring under Industry 4.0 framework towards sustainable construction. In 2021 Third International Sustainability and Resilience Conference: Climate Change (pp. 322–326). IEEE. https://doi.org/10.1109/IEEECONF53624.2021.9668074
Qureshi, A. H., Alaloul, W. S., Wing, W. K., Saad, S., Alzubi, K. M., & Musarat, M. A. (2022a). Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies. Construction Innovation. https://doi.org/10.1108/CI-04-2022-0076
Qureshi, A. H., Alaloul, W. S., Wing, W. K., Saad, S., Ammad, S., & Musarat, M. A. (2022b). Factors impacting the implementation process of automated construction progress monitoring. Ain Shams Engineering Journal, 13(6), Article 101808. https://doi.org/10.1016/j.asej.2022.101808
Qureshi, A. H., Alaloul, W. S., Wing, W. K., Saad, S., Ammad, S., & Altaf, M. (2023). Characteristics-based framework of effective automated monitoring parameters in construction projects. Arabian Journal for Science and Engineering, 48(4), 4731–4749. https://doi.org/10.1007/s13369-022-07172-y
Said, H., Badru, B. B., & Shahid, M. (2011). Confirmatory factor analysis (CFA) for testing validity and reliability instrument in the study of education. Australian Journal of Basic and Applied Sciences, 5(12), 1098–1103.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. Pearson Education.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
Seo, J., Han, S., Lee, S., & Kim, H. (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics, 29(2), 239–251. https://doi.org/10.1016/j.aei.2015.02.001
Sherafat, B., Ahn, C. R., Akhavian, R., Behzadan, A. H., Golparvar-Fard, M., Kim, H., Lee, Y.-C., Rashidi, A., & Azar, E. R. (2020). Automated methods for activity recognition of construction workers and equipment: State-of-the-art review. Journal of Construction Engineering and Management, 146(6), Article 03120002. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001843
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8, 1–13. https://doi.org/10.33151/ajp.8.3.93
Zhang, M., Cao, T., & Zhao, X. (2017). Applying sensor-based technology to improve construction safety management. Sensors, 17(8), Article 1841. https://doi.org/10.3390/s17081841
Zhu, Z., Ren, X., & Chen, Z. (2016). Visual tracking of construction jobsite workforce and equipment with particle filtering. Journal of Computing in Civil Engineering, 30(6), Article 04016023. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000573
Alaloul, W. S., Alzubi, K. M., Malkawi, A. B., Al Salaheen, M., & Musarat, M. A. (2021a). Productivity monitoring in building construction projects: A systematic review. Engineering, Construction and Architectural Management, 29(7), 2760–2785. https://doi.org/10.1108/ECAM-03-2021-0211
Alaloul, W. S., Qureshi, A. H., Musarat, M. A., & Saad, S. (2021b). Evolution of close-range detection and data acquisition technologies towards automation in construction progress monitoring. Journal of Building Engineering, 43, Article 102877. https://doi.org/10.1016/j.jobe.2021.102877
Alshibani, A. (2018). Automation of measuring actual productivity of earthwork in urban area, a case study from Montreal. Buildings, 8(12), Article 178. https://doi.org/10.3390/buildings8120178
Álvares, J. S., & Costa, D. B. (2018). Literature review on visual construction progress monitoring using unmanned aerial vehicles. In Proceedings of the 26th Annual Conference of the International Group for Lean Construction: Evolving Lean Construction Towards Mature Production Management Across Cultures and Frontiers (pp. 18–22), Chennai, India. https://doi.org/10.24928/2018/0310
Alzubi, K. M., Alaloul, W. S., Al Salaheen, M., Qureshi, A. H., Musarat, M. A., & Baarimah, A. O. (2021). Automated monitoring for construction productivity recognition. In 2021 Third International Sustainability and Resilience Conference: Climate Change (pp. 489–494). IEEE. https://doi.org/10.1109/IEEECONF53624.2021.9668172
Alzubi, K. M., Alaloul, W. S., & Qureshi, A. H. (2022a). Applications of cyber-physical systems in construction projects. In W. S. Alaloul (Ed.), Cyber-physical systems in the construction sector. CRC Press. https://doi.org/10.1201/9781003190134
Alzubi, K. M., Alaloul, W. S., Al Salaheen, M., Qureshi, A. H., Musarat, M. A., & Alawag, A. M. (2022b). Reviewing the applications of Internet of Things in construction projects. In 2022 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 169–173). IEEE. https://doi.org/10.1109/DASA54658.2022.9765143
Alzubi, K. M., Salah Alaloul, W., Malkawi, A. B., Al Salaheen, M., Qureshi, A. H., & Musarat, M. A. (2022c). Automated monitoring technologies and construction productivity enhancement: Building projects case. Ain Shams Engineering Journal, 14(8), Article 102042. https://doi.org/10.1016/j.asej.2022.102042
Arbuckle, J. L. (2011). IBM SPSS Amos 20 user’s guide. Amos Development Corporation, SPSS Inc.
Awang, Z. (2012). The second order confirmatory factor analysis. In A handbook on SEM (pp. 163–181). MPWS Rich Resources.
Braun, A., Tuttas, S., Borrmann, A., & Stilla, U. (2020). Improving progress monitoring by fusing point clouds, semantic data and computer vision. Automation in Construction, 116, Article 103210. https://doi.org/10.1016/j.autcon.2020.103210
Bügler, M., Borrmann, A., Ogunmakin, G., Vela, P. A., & Teizer, J. (2017). Fusion of photogrammetry and video analysis for productivity assessment of earthwork processes. Computer-Aided Civil and Infrastructure Engineering, 32(2), 107–123. https://doi.org/10.1111/mice.12235
Demir, N., Serel Arslan, S., İnal, Ö., & Karaduman, A. A. (2016). Reliability and validity of the Turkish eating assessment tool (T-EAT-10). Dysphagia, 31(5), 644–649. https://doi.org/10.1007/s00455-016-9723-9
Deng, H., Hong, H., Luo, D., Deng, Y., & Su, C. (2020). Automatic indoor construction process monitoring for tiles based on BIM and computer vision. Journal of Construction Engineering and Management, 146(1), Article 04019095. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001744
Dragan, D., & Topolšek, D. (2014). Introduction to structural equation modeling: Review, methodology and practical applications. In The International Conference on Logistics & Sustainable Transport (pp. 19–21), Celje, Slovenia.
Ekanayake, B., Wong, J. K.-W., Fini, A. A. F., & Smith, P. (2021). Computer vision-based interior construction progress monitoring: A literature review and future research directions. Automation in Construction, 127, Article 103705. https://doi.org/10.1016/j.autcon.2021.103705
Fink, A. (2005). Conducting research literature reviews: From internet to paper (2nd ed.). SAGE Publications.
Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2015). Automated progress monitoring using unordered daily construction photographs and IFC-based building information models. Journal of Computing in Civil Engineering, 29(1), Article 04014025. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000205
Gong, J., & Caldas, C. H. (2011). An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Automation in Construction, 20(8), 1211–1226. https://doi.org/10.1016/j.autcon.2011.05.005
Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. SAGE Publications. https://doi.org/10.1007/978-3-319-05542-8_15-1
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature. https://doi.org/10.1007/978-3-030-80519-7
Hamledari, H., McCabe, B., & Davari, S. (2017). Automated computer vision-based detection of components of under-construction indoor partitions. Automation in Construction, 74, 78–94. https://doi.org/10.1016/j.autcon.2016.11.009
Ho, D. C. W., Yau, Y., Poon, S. W., & Liusman, E. (2012). Achieving sustainable urban renewal in Hong Kong: Strategy for dilapidation assessment of high rises. Journal of Urban Planning and Development, 138(2), 153–165. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000104
Huang, Y., Hammad, A., & Zhu, Z. (2021). Providing proximity alerts to workers on construction sites using Bluetooth Low Energy RTLS. Automation in Construction, 132, Article 103928. https://doi.org/10.1016/j.autcon.2021.103928
Israel, G. D. (1992). Determining sample size. University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS, Florida.
Khosrowpour, A., Niebles, J. C., & Golparvar-Fard, M. (2014). Vision-based workface assessment using depth images for activity analysis of interior construction operations. Automation in Construction, 48, 74–87. https://doi.org/10.1016/j.autcon.2014.08.003
Konstantinou, E., Lasenby, J., & Brilakis, I. (2019). Adaptive computer vision-based 2D tracking of workers in complex environments. Automation in Construction, 103, 168–184. https://doi.org/10.1016/j.autcon.2019.01.018
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/1743727X.2015.1005806
Luo, H., Xiong, C., Fang, W., Love, P. E., Zhang, B., & Ouyang, X. (2018). Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction, 94, 282–289. https://doi.org/10.1016/j.autcon.2018.06.007
Maalek, R., Lichti, D. D., & Ruwanpura, J. Y. (2019). Automatic recognition of common structural elements from point clouds for automated progress monitoring and dimensional quality control in reinforced concrete construction. Remote Sensing, 11(9), Article 9. https://doi.org/10.3390/rs11091102
McCulloch, B. (1997). Automating field data collection in construction organizations. In Proceeding of the 1997 ASCE Construction Congress (pp. 957–963), Minneapolis, Minesota, USA.
Mneymneh, B. E., Abbas, M., & Khoury, H. (2018). Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications. Frontiers of Engineering Management, 5(2), 227–239. https://doi.org/10.15302/J-FEM-2018071
Mohanty, L., Chae, S., & Yang, Y. (2020). Identifying productive working patterns at construction sites using BLE sensor networks. Developments in the Built Environment, 4, Article 100025. https://doi.org/10.1016/j.dibe.2020.100025
Ogunsanya, O. A., Aigbavboa, C. O., Thwala, D. W., & Edwards, D. J. (2022). Barriers to sustainable procurement in the Nigerian construction industry: An exploratory factor analysis. International Journal of Construction Management, 22(5), 861–872. https://doi.org/10.1080/15623599.2019.1658697
Omar, H., Mahdjoubi, L., & Kheder, G. (2018). Towards an automated photogrammetry-based approach for monitoring and controlling construction site activities. Computers in Industry, 98, 172–182. https://doi.org/10.1016/j.compind.2018.03.012
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. McGraw-Hill Education (UK). https://doi.org/10.4324/9781003117407
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. SAGE Publications.
Qureshi, A. H., Alaloul, W. S., Manzoor, B., Saad, S., Alawag, A. M., & Alzubi, K. M. (2021). Implementation challenges of automated construction progress monitoring under Industry 4.0 framework towards sustainable construction. In 2021 Third International Sustainability and Resilience Conference: Climate Change (pp. 322–326). IEEE. https://doi.org/10.1109/IEEECONF53624.2021.9668074
Qureshi, A. H., Alaloul, W. S., Wing, W. K., Saad, S., Alzubi, K. M., & Musarat, M. A. (2022a). Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies. Construction Innovation. https://doi.org/10.1108/CI-04-2022-0076
Qureshi, A. H., Alaloul, W. S., Wing, W. K., Saad, S., Ammad, S., & Musarat, M. A. (2022b). Factors impacting the implementation process of automated construction progress monitoring. Ain Shams Engineering Journal, 13(6), Article 101808. https://doi.org/10.1016/j.asej.2022.101808
Qureshi, A. H., Alaloul, W. S., Wing, W. K., Saad, S., Ammad, S., & Altaf, M. (2023). Characteristics-based framework of effective automated monitoring parameters in construction projects. Arabian Journal for Science and Engineering, 48(4), 4731–4749. https://doi.org/10.1007/s13369-022-07172-y
Said, H., Badru, B. B., & Shahid, M. (2011). Confirmatory factor analysis (CFA) for testing validity and reliability instrument in the study of education. Australian Journal of Basic and Applied Sciences, 5(12), 1098–1103.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. Pearson Education.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
Seo, J., Han, S., Lee, S., & Kim, H. (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics, 29(2), 239–251. https://doi.org/10.1016/j.aei.2015.02.001
Sherafat, B., Ahn, C. R., Akhavian, R., Behzadan, A. H., Golparvar-Fard, M., Kim, H., Lee, Y.-C., Rashidi, A., & Azar, E. R. (2020). Automated methods for activity recognition of construction workers and equipment: State-of-the-art review. Journal of Construction Engineering and Management, 146(6), Article 03120002. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001843
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8, 1–13. https://doi.org/10.33151/ajp.8.3.93
Zhang, M., Cao, T., & Zhao, X. (2017). Applying sensor-based technology to improve construction safety management. Sensors, 17(8), Article 1841. https://doi.org/10.3390/s17081841
Zhu, Z., Ren, X., & Chen, Z. (2016). Visual tracking of construction jobsite workforce and equipment with particle filtering. Journal of Computing in Civil Engineering, 30(6), Article 04016023. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000573