Assessment of the Significance of Identified Attributes Affecting the Rebar-Fixing Productivity Using Multiple Regression
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 6
Abstract
Reinforced concrete structures are an inherent part of the construction sector. Every in situ reinforced concrete structure cardinally involves rebar-fixing works, which entails activities like transporting, marking, laying, and tying the rebar for the components under construction. Rebar-fixing absorbs a significant number of man-hours during the operation. Therefore, productivity of rebar fixers is a critical concern in construction. Several attributes influence rebar-fixing productivity. Former studies have investigated the impact of the attributes separately and predominantly using qualitative data. A few studies have assessed rebar productivity quantitatively but ignored the effects of some crucial parameters, such as congestion, crew composition, and construction delays. Moreover, studies are limited to account for all time proportions like productive, semiproductive, and nonproductive while assessing rebar productivity. Therefore, this study addresses the aforementioned research gaps by assessing the impact of diverse attributes using direct measures of rebar-fixing productivity and its attributes. Initially, hypotheses were developed to determine the influence of eight attributes identified in this study on rebar-fixing productivity. Next, the data were recorded from one large construction project in Gujarat, India. The work sampling approach was adopted for the productivity assessment of rebar fixers at the project site to evaluate each time proportion. Furthermore, the stepwise regression analysis was performed on the data set to examine the hypotheses. Results indicate that attributes such as quantity of reinforcement, characteristic rebar diameter, and congestion have a significant positive impact on rebar-fixing productivity, whereas work duration has a significant negative impact on it. The outcomes of this research can aid the site management, structural engineers, and construction planners in decision-making to enhance the productivity of rebar fixers, thereby adhering to the budget and schedule constraints of the project.
Practical Applications
Reinforcement fixing is a major activity that significantly controls the revenue and duration of any construction project. Labor is an indispensable part of this activity. Therefore, it is crucial to guard the output of the rebar fixers. This study is a guiding light for the construction stakeholders to manage the productivity of rebar fixers. The various identified attributes are practically examined to know their impact on the productivity of rebar fixers. The results can be helpful in decision-making and process improvement at the site management level. During the designing stage of a structure, it can support the design backed up with evidence to improve the rebar-fixing productivity significantly.
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Data Availability Statement
Some or all data that support the findings of this study are available from the corresponding author upon an admissible request.
References
Agrawal, A., and S. Halder. 2020. “Identifying factors affecting construction labour productivity in India and measures to improve productivity.” Asian J. Civ. Eng. 21 (4): 569–579. https://doi.org/10.1007/s42107-019-00212-3.
Alaghbari, W., A. A. Al-Sakkaf, and B. Sultan. 2017. “Factors affecting construction labour productivity in Yemen.” Int. J. Construct. Manage. 19 (1): 79–91. https://doi.org/10.1080/15623599.2017.1382091.
Allmon, E., C. T. Haas, J. D. Borcherding, and P. M. Goodrum. 2000. “US construction labor productivity trends, 1970–1998.” J. Constr. Eng. Manage. 126 (2): 97–104. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:2(97).
Alrefaie, A. M., Z. Abdul-Samad, H. Salleh, A. M. Alashwal, and D. Amos. 2021. “Modelling labour productivity of reinforcement bar using polynomial regression: A study on a tropical country’s weather.” Int. J. Construct. Manage. 1–9. https://doi.org/10.1080/15623599.2021.1997408.
Alzubi, K. M., A. M. Alkhateeb, and M. A. Hiyassat. 2021. “Factors affecting the job satisfaction of construction engineers: Evidence from Jordan.” Int. J. Construct. Manage. 23 (2): 319–328. https://doi.org/10.1080/15623599.2020.1867945.
Badawy, M., A. Hussein, S. M. Elseufy, and K. Alnaas. 2019. “How to predict the rebar labours’ production rate by using ANN model?” Int. J. Construct. Manage. 21 (4): 427–438. https://doi.org/10.1080/15623599.2018.1553573.
Bardsiri, K. V., D. N. A. Jawawi, S. Z. M. Hashim, and E. Khatibi. 2014. “A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons.” Empirical Software Eng. 19 (4): 857–884. https://doi.org/10.1007/s10664-013-9241-4.
Dabirian, S., M. Moussazadeh, M. Khanzadi, and S. Abbaspour. 2021. “Predicting the effects of congestion on labour productivity in construction projects using agent-based modeling.” Int. J. Construct. Manage. 23 (4): 606–618. https://doi.org/10.1080/15623599.2021.1901330.
Dave, Y., and N. Sohani. 2019. “Improving productivity through Lean practices in central India-based manufacturing industries.” Int. J. Lean Six Sigma 10 (2): 601–621. https://doi.org/10.1108/IJLSS-10-2017-0115.
Dozzi, S. P., and S. M. AbouRizk. 1993. Productivity in construction. Ottawa: National Research Council.
Durdyev, S., and J. Mbachu. 2017. “Key constraints to labour productivity in residential building projects: Evidence from Cambodia.” Int. J. Construct. Manage. 18 (5): 385–393. https://doi.org/10.1080/15623599.2017.1326301.
El-Gohary, K. M., and R. F. Aziz. 2014. “Factors influencing construction labor productivity in Egypt.” J. Manage. Eng. 30 (1): 1–9. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000168.
Enshassi, A., S. Mohamed, Z. A. Mustafa, and P. E. Mayer. 2007. “Factors affecting labour productivity in building projects in the Gaza Strip.” J. Civ. Eng. Manage. 13 (4): 245–254. https://doi.org/10.3846/13923730.2007.9636444.
Evans, J. D. 1996. Straightforward statistics for the behavioral sciences. Pacific Grove, CA: Brooks/Cole Publishing.
Gundecha, M. M. 2013. “Study of factors affecting labor productivity at a building construction project in the USA: Web survey.” Ph.D. dissertation, Dept. of Construction Management and Engineering, North Dakota State Univ.
Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2016. Multivariate data analysis. London: Pearson Education.
Hanna, A. S., C. K. Chang, J. A. Lackney, and K. T. Sullivan. 2007. “Impact of overmanning on mechanical and sheet metal labor productivity.” J. Constr. Eng. Manage. 133 (1): 22–28. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:1(22).
Hasan, A., B. Baroudi, A. Elmualim, and R. Rameezdeen. 2018. “Factors affecting construction productivity: A 30 year systematic review.” Eng. Constr. Archit. Manage. 25 (7): 916–937. https://doi.org/10.1108/ECAM-02-2017-0035.
Hiyassat, M. A., M. A. Hiyari, and G. J. Sweis. 2016. “Factors affecting construction labour productivity: A case study of Jordan.” Int. J. Construct. Manage. 16 (2): 138–149. https://doi.org/10.1080/15623599.2016.1142266.
Jarkas, A. M. 2010. “The effects of buildability factors on rebar fixing labour productivity of isolated foundations.” Int. J Constr. Manage. 10 (2): 33–51. https://doi.org/10.1080/15623599.2010.10773143.
Jarkas, A. M. 2015. “Factors influencing labour productivity in Bahrain’s construction industry.” Int. J. Construct. Manage. 15 (1): 94–108. https://doi.org/10.1080/15623599.2015.1012143.
Jarkas, A. M., and C. G. Bitar. 2012. “Factors affecting construction labor productivity in Kuwait.” J. Constr. Eng. Manage. 138 (7): 811–820. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000501.
Jarkas, A. M., C. Y. Kadri, and J. H. Younes. 2012. “A survey of factors influencing the productivity of construction operatives in the state of Qatar.” Int. J. Construct. Manage. 12 (3): 1–23. https://doi.org/10.1080/15623599.2012.10773192.
Karthik, D., and K. C. B. Rao. 2019. “Identifying the significant factors affecting the masonry labour productivity in building construction projects in India.” Int. J. Construct. Manage. 22 (3): 464–472. https://doi.org/10.1080/15623599.2019.1631978.
Lee, C. 2022. “Evaluating construction delays using productivity-based resource reallocation for economic feasibility.” Eng. Constr. Archit. Manage. https://doi.org/10.1108/ECAM-09-2019-0503.
Li, X., K. H. Chow, Y. Zhu, and Y. Lin. 2016. “Evaluating the impacts of high-temperature outdoor working environments on construction labor productivity in China: A case study of rebar workers.” Build. Environ. 95 (Jan): 42–52. https://doi.org/10.1016/j.buildenv.2015.09.005.
Lim, E. C. 1996. “The analysis of productivity in building construction.” Doctoral dissertation, Architecture, Building and Civil Engineering, Loughborough Univ.
Liou, F.-S., and J. D. Borcherding. 1986. “Work sampling can predict unit rate productivity.” J. Constr. Eng. Manage. 112 (1): 90–103. https://doi.org/10.1061/(ASCE)0733-9364(1986)112:1(90).
Loganathan, S., and P. Forsythe. 2020. “Unravelling the influence of teamwork on trade crew productivity: A review and a proposed framework.” Construct. Manage. Econ. 38 (11): 1040–1060. https://doi.org/10.1080/01446193.2020.1795900.
Luo, X., H. Li, D. Cao, Y. Yu, X. Yang, and T. Huang. 2018. “Towards efficient and objective work sampling: Recognizing workers’ activities in site surveillance videos with two-stream convolutional networks.” Autom. Constr. 94 (Oct): 360–370. https://doi.org/10.1016/j.autcon.2018.07.011.
Maloney, W. F. 1983. “Productivity improvement: The influence of labor.” J. Constr. Eng. Manage. 109 (3): 321–334. https://doi.org/10.1061/(ASCE)0733-9364(1983)109:3(321).
Manoharan, K., P. Dissanayake, C. Pathirana, D. Deegahawature, and R. Silva. 2020. “Assessment of critical factors influencing the performance of labour in Sri Lankan construction industry.” Int. J. Construct. Manage. 23 (1): 144–155. https://doi.org/10.1080/15623599.2020.1854042.
Marill, K. A. 2004. “Advanced statistics: Linear regression, Part II: Multiple linear regression.” Acad. Emerg. Med. 11 (1): 94–102. https://doi.org/10.1111/j.1553-2712.2004.tb01379.x.
Nguyen, L. D., and H. T. Nguyen. 2013. “Relationship between building floor and construction labor productivity: A case of structural work.” Eng. Constr. Archit. Manage. 20 (6): 563–575. https://doi.org/10.1108/ECAM-03-2012-0034.
Oglesby, C., H. Parker, and G. Howell. 1989. Productivity improvement in construction. New York: McGraw-Hill.
O’Neill, C., and K. Panuwatwanich. 2013. “The impact of fatigue on labour productivity: Case study of dam construction project in Queensland.” In Proc., from Engineering, Project, and Production Management, 993–1005. Nathan, QLD, Australia: Griffith Univ.
Palikhe, S., S. Kim, and J. J. Kim. 2019. “Critical success factors and dynamic modeling of construction labour productivity.” Int. J. Civ. Eng. 17 (3): 427–442. https://doi.org/10.1007/s40999-018-0282-3.
Proverbs, D. G., G. D. Holt, and P. O. Olomolaiye. 1999. “Construction resource/method factors influencing productivity for high rise concrete construction.” Construct. Manage. Econ. 17 (5): 577–587. https://doi.org/10.1080/014461999371196.
Sahu, S., M. Sett, and T. Kjellstrom. 2013. “Heat exposure, cardiovascular stress and work productivity in rice harvesters in India: Implications for a climate change future.” Ind. Health 51 (4): 424–431. https://doi.org/10.2486/indhealth.2013-0006.
Silhavy, R., P. Silhavy, and Z. Prokopova. 2017. “Analysis and selection of a regression model for the use case points method using a stepwise approach.” J. Syst. Software 125 (Mar): 1–14. https://doi.org/10.1016/j.jss.2016.11.029.
Sonmez, R., and J. E. Rowings. 1998. “Construction labor productivity modeling with neural networks.” J. Constr. Eng. Manage. 124 (6): 498–504. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(498).
Sun, M., and X. Meng. 2009. “Taxonomy for change causes and effects in construction projects.” Int. J. Project Manage. 27 (6): 560–572. https://doi.org/10.1016/j.ijproman.2008.10.005.
Tao, S., C. Wu, Z. Sheng, and X. Wang. 2018. “Space-time repetitive project scheduling considering location and congestion.” J. Comput. Civ. Eng. 32 (3): 04018017. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000745.
Thomas, H. R., and J. Daily. 1983. “Crew performance measurement via activity sampling.” J. Constr. Eng. Manage. 109 (3): 309–320. https://doi.org/10.1061/(ASCE)0733-9364(1983)109:3(309).
Tsehayae, A. A., and R. A. Fayek. 2016. “Developing and optimizing context-specific fuzzy inference system-based construction labor productivity models.” J. Constr. Eng. Manage. 142 (7): 04016017. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001127.
Ullah, M. I., M. Aslam, S. Altaf, and M. Ahmed. 2019. “Some new diagnostics of multicollinearity in linear regression model.” Sains Malaysiana 48 (9): 2051–2060. https://doi.org/10.17576/jsm-2019-4809-26.
Watkins, M., A. Mukherjee, N. Onder, and K. Mattila. 2009. “Using agent-based modeling to study construction labor productivity as an emergent property of individual and crew interactions.” J. Constr. Eng. Manage. 135 (7): 657–667. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000022.
Yi, W., and A. P. Chan. 2017. “Effects of heat stress on construction labor productivity in Hong Kong: A case study of rebar workers.” Int. J. Environ. Res. Public Health 14 (9): 1055. https://doi.org/10.3390/ijerph14091055.
Zheng, C., C. Yi, and M. Lu. 2019. “Integrated optimization of rebar detailing design and installation planning for waste reduction and productivity improvement.” Autom. Constr. 101 (May): 32–47. https://doi.org/10.1016/j.autcon.2019.01.012.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 27, 2022
Accepted: Feb 8, 2023
Published online: Apr 13, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 13, 2023
ASCE Technical Topics:
- Analysis (by type)
- Business management
- Concrete
- Concrete construction
- Concrete structures
- Construction (by type)
- Construction engineering
- Construction management
- Design (by type)
- Engineering fundamentals
- Engineering materials (by type)
- Materials engineering
- Metals (material)
- Personnel management
- Practice and Profession
- Productivity
- Regression analysis
- Reinforced concrete
- Reinforcing steel
- Statistical analysis (by type)
- Steel
- Structural design
- Structural engineering
- Structure reinforcement
- Structures (by type)
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