Forecasting Construction Labor Productivity Metrics
Publication: Computing in Civil Engineering 2023
ABSTRACT
This study presents an autoregressive method for forecasting construction labor productivity metrics. Productivity is an essential parameter to monitor progress in construction projects as it can determine whether the project succeeds or fails in terms of cost and time. However, collecting productivity data, or data correlating with productivity, takes time and effort. Furthermore, the collected productivity data offers limited insights into the current and past performance of the construction activities, which can be valuable to project managers but are often too late to act on due to the transitory nature of construction projects. To increase the amount of information available for decision-makers and analyses, this paper investigates the possibility of using forecasting methods to estimate future values of direct work, indirect work, and waste. Four models are developed and evaluated on a dataset collected on Danish construction sites. Being able to forecast these metrics will add value for decision makers.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Adebowale, O. J., and Agumba, J. N. (2022). A scientometric analysis and review of construction labour productivity research, International Journal of Productivity and Performance Management, Vol. ahead-of-print, Issue ahead-of-print, https://doi.org/10.1108/IJPPM-09-2021-0505.
Box, G. E. P., and Jenkins, G. M. (1976). Time series analysis: Forecasting and control, Revised Edition, Holden Day, San Francisco, ISBN: 0816211043, 9780816211043.
Breiman, L. (2001). Random Forests, Machine Learning, Vol. 45, pp. 5–32, https://doi.org/10.1023/A:1010933404324.
Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, https://doi.org/10.1145/2939672.2939785.
Cheng, M., Cao, M., and Mendrofa, A. Y. J. (2021). Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine, Journal of Building Engineering, Vol. 35, 101973, https://doi.org/10.1016/j.jobe.2020.101973.
Ebrahimi, S., Fayek, A. R., and Sumati, V. (2021). Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization, Algorithms, Vol. 14, Issue 7, https://doi.org/10.3390/a14070214.
Golnaraghi, S., Zangenehmadar, Z., Moselhi, O., and Alkass, S. (2019). Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity, Advances in Civil Engineering, Vol. 2019, 5972620, https://doi.org/10.1155/2019/5972620.
Jacobsen, E. L., and Teizer, J. (2022). Deep learning in construction: Review of applications and potential avenues, Journal of Computing in Civil Engineering, Vol. 36, Issue 2, https://doi.org/10.1061/(ASCE)CP.1943-5487.0001010.
Jacobsen, E. L., Wandahl, S., and Teizer, J. (2023a). Work estimation of construction workers for productivity monitoring using kinematic data and deep learning, Automation in Construction, Vol 152, https://doi.org/10.1016/j.autcon.2023.104932.
Jacobsen, E. L., Teizer, J., Wandahl, S., and Brilakis, I. (2023b). Probabilistic Forecasting of Metrics for Construction Labor Productivity Monitoring, Manuscript submitted for publication.
Lim, B., Arık, S. Ö., Loeff, N., and Pfister, T. (2021). Temporal Fusion Transformers for interpretable multi-horizon time series forecasting, International Journal of Forecasting, Vol. 37, Issue 4, https://doi.org/10.1016/j.ijforecast.2021.03.012.
Logcher, R. D., and Collins, W. W. (1978). Management impacts on labor productivity, Journal of the Construction Division, Vol. 104, Issue 4, https://doi.org/10.1061/JCCEAZ.0000800.
Neve, H., Wandahl, S., Lindhard, S., Teizer, J., and Lerche, J. (2020). Learning to see value-adding and non-value-adding work time in renovation production systems, Production Planning & Control, Vol. 33, Issue 8, https://doi.org/10.1080/09537287.2020.1843730.
Oral, E. L., and Oral, M. (2010). Predicting construction crew productivity by using Self Organizing Maps, Automation in Construction, Vol. 19, Issue 6, https://doi.org/10.1016/j.autcon.2010.05.001.
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y. (2020). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, Proceedings of the 8th international conference on learning representations (ICLR 2020), https://openreview.net/pdf?id=r1ecqn4YwB.
Petropoulos, F., et al. (2022). Forecasting: theory and practice, International Journal of Forecasting, Vol. 38, Issue 3, https://doi.org/10.1016/j.ijforecast.2021.11.001.
Sanders, S. R., and Thomas, H. R. (1993). Masonry productivity forecasting model, Journal of Construction Engineering and Management, Vol. 119, Issue 1, https://doi.org/10.1061/(ASCE)0733-9364(1993)119:1(163).
Teizer, J., Neve, H., Li, H., Wandahl, S., König, J., Ochner, B., König, M., and Lerche, J. (2020). Construction resource efficiency improvement by Long Range Wide Area Network tracking and monitoring, Vol. 116, 103245, https://doi.org/10.1016/j.autcon.2020.103245.
Tsehayae, A. A., and Fayek, A. R. (2016). System model for analysing construction labour productivity, Construction Innovation, Vol. 16, Issue 2, https://doi.org/10.1108/CI-07-2015-0040.
Wandahl, S., Neve, H. H., and Lerche, J. (2021). What a Waste of Time, Proceedings of the 29th Annual Conference of the International Group for Lean Construction (IGLC29), pp. 157–166, https://doi.org/10.24928/2021/0115.
Wandahl, S., Pérez, C. T., Salling, S., and Lerche, J. (2022). Robustness of Work Sampling for Measuring Time Waste, Proceedings of the 30th Annual Conference of the International Group for Lean Construction (IGLC30), pp. 247–258, https://doi.org/10.24928/2022/0127.
Yi, W., and Chan, A. P. C. (2014). Critical Review of Labor Productivity Research in Construction Journals, Journal of Management in Engineering, Vol. 30, Issue 2, https://doi.org/10.1061/(ASCE)ME.1943-5479.0000194.
Zarei, B., Sharifi, H., and Chaghouee, Y. (2018). Delay causes analysis in complex construction projects: a Semantic Network Analysis approach, Production Planning & Control, Vol. 29, Issue 1, https://doi.org/10.1080/09537287.2017.1376257.
Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 1022 - 1029
History
Published online: Jan 25, 2024
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.