Duration Estimation of a Heavy Industrial Scaffolding Project: A Case Study
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 4
Abstract
Accurate project duration estimation is crucial for effective scheduling, budgeting, resource allocation, and overall construction management. Leveraging historical data from completed projects is an effective strategy to achieve this. In heavy industrial projects, where scaffolding activities can span from thousands to millions of hours, refining the estimation of scaffolding time is vital during the planning phase. This study undertook the analysis of data from a completed heavy industrial scaffolding project, aiming to propose a methodology and models for predicting future projects durations. The proposed methodology not only aids in improved duration but also contributes to cost estimation, scheduling, and project delivery of similar future endeavors. Commencing with data cleaning and categorizing the data based on activity types, the scatter plots of person-hours versus task weight within each category revealed a linear relationship. Consequently, linear models for each category were developed. Statistical factors such as data size, coefficient of determination, and mean absolute error were then utilized to calculate a score for each model, guiding the model selection process which substituted low score models with a parent category with a higher score. The data analysis and modeling were performed five times to ensure robustness and consistency in the results. On average, the initial models yielded a project duration estimate of only 0.36% higher than the actual duration, while the selected models increased this deviation to 4.14%. The scoring and selection process enhances estimation accuracy while maintaining proximity to actual project durations. This research makes three significant contributions: (1) introducing a categorical linear regression approach for scaffolding activity duration prediction, (2) presenting a novel normalization and scoring method that scores models based on statistical factors, and (3) implementing a practical model selection process to substitute weaker models with stronger ones, ultimately strengthening the reliability of activity and project duration predictions.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge the support from the industry partner, Hinton Scaffold Solutions. The research funding support from the Alliance Grant by the Natural Sciences and Engineering Research Council is appreciated (funding No.: ALLRP 566979 - 21).
References
Altaf, M. S., A. Bouferguene, H. Liu, M. Al-Hussein, and H. Yu. 2018. “Integrated production planning and control system for a panelized home prefabrication facility using simulation and RFID.” Autom. Constr. 85 (Jan): 369–383. https://doi.org/10.1016/j.autcon.2017.09.009.
Arashpour, M., V. Kamat, Y. Bai, R. Wakefield, and B. Abbasi. 2018. “Optimization modeling of multi-skilled resources in prefabrication: Theorizing cost analysis of process integration in off-site construction.” Autom. Constr. 95 (Nov): 1–9. https://doi.org/10.1016/j.autcon.2018.07.027.
Baduge, S. K., S. Thilakarathna, J. S. Perera, M. Arashpour, P. Sharafi, B. Teodosio, A. Shringi, and P. Mendis. 2022. “Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications.” Autom. Constr. 141 (Sep): 104440. https://doi.org/10.1016/j.autcon.2022.104440.
Barbosa, F., J. Woetzel, and J. Mischke, M. João Ribeirinho, M. Sridhar, M. Parsons, N. Bertram, and S. Brown. 2017. Reinventing construction: A rout to higher productivity. New York: McKinsey Global Institute.
Chen, Y., G. E. Okudan, and D. R. Riley. 2010. “Decision support for construction method selection in concrete buildings: Prefabrication adoption and optimization.” Autom. Constr. 19 (6): 665–675. https://doi.org/10.1016/j.autcon.2010.02.011.
Chi, H. L., J. Chai, C. Wu, J. Zhu, C. Liu, and X. Wang. 2017. “Scaffolding progress monitoring of LNG plant maintenance project using BIM and image processing technologies.” In Proc., Int. Conf. on Research and Innovation in Information Systems, ICRIIS. New York: IEEE.
Cho, C., K. Kim, J. Park, and Y. K. Cho. 2018. “Data-driven monitoring system for preventing the collapse of scaffolding structures.” J. Constr. Eng. Manage. 144 (8): 04018077. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001535.
Chu, W., S. H. Han, L. Zhen, U. Hermann, and D. Hu. 2020. “A predictive model for scaffolding man-hours in heavy industrial construction projects.” In Proc., 37th Int. Symp. on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - to New Stage of Construction Robot, 976–983. Oulu, Finland: International Association on Automation and Robotics in Construction.
Elghaish, F., M. R. Hosseini, S. Matarneh, S. Talebi, S. Wu, I. Martek, M. Poshdar, and N. Ghodrati. 2021. “Blockchain and the ‘internet of things’ for the construction industry: Research trends and opportunities.” Autom. Constr. 132 (Dec): 103942. https://doi.org/10.1016/j.autcon.2021.103942.
ElNimr, A., M. Fagiar, and Y. Mohamed. 2016. “Two-way integration of 3D visualization and discrete event simulation for modeling mobile crane movement under dynamically changing site layout.” Autom. Constr. 68 (Feb): 235–248. https://doi.org/10.1016/j.autcon.2016.05.013.
Forcael, E., I. Ferrari, A. Opazo-Vega, and J. A. Pulido-Arcas. 2020. “Construction 4.0: A literature review.” Sustainability 12 (22): 9755. https://doi.org/10.3390/su12229755.
Ghobakhloo, M. 2020. “Industry 4.0, digitization, and opportunities for sustainability.” J. Cleaner Prod. 252 (Apr): 119869. https://doi.org/10.1016/j.jclepro.2019.119869.
Goh, M., and Y. M. Goh. 2019. “Lean production theory-based simulation of modular construction processes.” Autom. Constr. 101 (Aug): 227–244. https://doi.org/10.1016/j.autcon.2018.12.017.
González, V., and T. Echaveguren. 2012. “Exploring the environmental modeling of road construction operations using discrete-event simulation.” Autom. Constr. 24 (Jan): 100–110. https://doi.org/10.1016/j.autcon.2012.02.011.
Ho, C., Y. W. Kim, and Z. B. Zabinsky. 2022. “Prefabrication supply chains with multiple shops: Optimization for job allocation.” Autom. Constr. 136 (Apr): 104155. https://doi.org/10.1016/j.autcon.2022.104155.
Hou, L., C. Zhao, C. Wu, S. Moon, and X. Wang. 2016. “Discrete firefly algorithm for scaffolding construction scheduling.” J. Comput. Civ. Eng. 31 (3): 04016064. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000639.
Jang, S., and G. Lee. 2018. “Process, productivity, and economic analyses of BIM–based multi-trade prefabrication—A case study.” Autom. Constr. 89 (Feb): 86–98. https://doi.org/10.1016/j.autcon.2017.12.035.
Jato-Espino, D., E. Castillo-Lopez, J. Rodriguez-Hernandez, and J. C. Canteras-Jordana. 2014. “A review of application of multi-criteria decision making methods in construction.” Autom. Constr. 45 (Sep): 151–162. https://doi.org/10.1016/j.autcon.2014.05.013.
Khodabandelu, A., and J. W. Park. 2021. “Agent-based modeling and simulation in construction.” Autom Constr. 131 (Nov): 103882. https://doi.org/10.1016/j.autcon.2021.103882.
Kim, K., Y. Cho, and S. Zhang. 2016. “Integrating work sequences and temporary structures into safety planning: Automated scaffolding-related safety hazard identification and prevention in BIM.” Autom. Constr. 70 (Feb): 128–142. https://doi.org/10.1016/j.autcon.2016.06.012.
Kim, K., Y. K. Cho, and K. Kim. 2018. “BIM-based decision-making framework for scaffolding planning.” J. Manage. Eng. 34 (6): 04018046. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000656.
Kim, K., and J. Teizer. 2014. “Automatic design and planning of scaffolding systems using building information modeling.” Adv. Eng. Inf. 28 (1): 66–80. https://doi.org/10.1016/j.aei.2013.12.002.
Lei, Z., Y. Hu, J. Hua, B. Marton, P. Goldberg, and N. Marton. 2022. “An earned-value-analysis (Eva)-based project control framework in large-scale scaffolding projects using linear regression modeling.” J. Inf. Technol. Constr. 27 (May): 630–641. https://doi.org/10.36680/j.itcon.2022.031.
Li, H. X., M. Al-Hussein, Z. Lei, and Z. Ajweh. 2013. “Risk identification and assessment of modular construction utilizing fuzzy analytic hierarchy process (AHP) and simulation.” Can. J. Civ. Eng. 40 (12): 1184–1195. https://doi.org/10.1139/cjce-2013-0013.
Li, J., G. Yin, X. Wang, and W. Yan. 2022. “Automated decision making in highway pavement preventive maintenance based on deep learning.” Autom. Constr. 135 (Mar): 104111. https://doi.org/10.1016/j.autcon.2021.104111.
Liu, J., S. M. Asce, M. Lu, P. Eng, and M. Asce. 2018. “Robust dual-level optimization framework for resource-constrained multiproject scheduling for a prefabrication facility in construction.” J. Comput. Civil Eng. 33 (2): 04018067. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000816.
Ma, Z., Y. Ren, X. Xiang, and Z. Turk. 2020. “Data-driven decision-making for equipment maintenance.” Autom. Constr. 112 (Apr): 103103. https://doi.org/10.1016/j.autcon.2020.103103.
Mohsen, O., Y. Mohamed, and M. Al-Hussein. 2022. “A machine learning approach to predict production time using real-time RFID data in industrialized building construction.” Adv. Eng. Inf. 52 (Apr): 101631. https://doi.org/10.1016/j.aei.2022.101631.
Moon, S., J. Forlani, X. Wang, and V. Tam. 2016. “Productivity study of the scaffolding operations in liquefied natural gas plant construction: Ichthys Project in Darwin, Northern Territory, Australia.” J. Civ. Eng. Educ. 142 (4): 04016008. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000287.
Moon, S., S. Xu, L. Hou, C. Wu, X. Wang, and V. W. Y. Tam. 2017. “RFID-aided tracking system to improve work efficiency of scaffold supplier: Stock management in Australasian supply chain.” J. Constr. Eng. Manage. 144 (2): 04017115 https://doi.org/10.1061/(ASCE)CO.1943-7862.0001432.
Nili, M. H., H. Taghaddos, and B. Zahraie. 2021. “Integrating discrete event simulation and genetic algorithm optimization for bridge maintenance planning.” Autom. Constr. 122 (Feb): 103513. https://doi.org/10.1016/j.autcon.2020.103513.
Ning, X., K. C. Lam, and M. C. K. Lam. 2011. “A decision-making system for construction site layout planning.” Autom. Constr. 20 (4): 459–473. https://doi.org/10.1016/j.autcon.2010.11.014.
Raoufi, M., and A. Robinson Fayek. 2018. “Fuzzy agent-based modeling of construction crew motivation and performance.” J. Comput. Civ. Eng. 32 (5): 04018035. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000777.
Rausch, C., M. Nahangi, C. Haas, and W. Liang. 2019. “Monte Carlo simulation for tolerance analysis in prefabrication and offsite construction.” Autom. Constr. 103 (Jun): 300–314. https://doi.org/10.1016/j.autcon.2019.03.026.
Sadeghi, N., A. R. Fayek, and W. Pedrycz. 2010. “Fuzzy Monte Carlo simulation and risk assessment in construction.” Comput.-Aided Civ. Infrastruct. Eng. 25 (4): 238–252. https://doi.org/10.1111/j.1467-8667.2009.00632.x.
Said, H. M., T. Chalasani, and S. Logan. 2017. “Exterior prefabricated panelized walls platform optimization.” Autom. Constr. 76 (Aug): 1–13. https://doi.org/10.1016/j.autcon.2017.01.002.
Shen, K., X. Li, X. Cao, and Z. Zhang. 2022. “Prefabricated construction process optimization based on rework risk.” J. Constr. Eng. Manage. 148 (6): 04022031. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002277.
Siddappa, K. 2019. A predictive model for scaffolding Manhours in heavy industrial construction projects: An application of machine learning. Quebec: Concordia Univ.
Wu, L., W. Ji, and S. M. AbouRizk. 2019. “Bayesian inference with Markov Chain Monte Carlo–based numerical approach for input model updating.” J. Comput. Civ. Eng. 34 (1): 04019043. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000862.
Wu, L., Y. Mohamed, H. Taghaddos, and R. Hermann. 2014. “Analyzing scaffolding needs for industrial construction sites using historical data.” In Proc., Construction Research Congress 2014: Construction in a Global Network, 1596–1605. Reston, VA: ASCE. https://doi.org/10.1061/9780784413517.163.
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© 2024 American Society of Civil Engineers.
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Received: Apr 19, 2023
Accepted: Nov 13, 2023
Published online: Feb 9, 2024
Published in print: Apr 1, 2024
Discussion open until: Jul 9, 2024
ASCE Technical Topics:
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