Assessing the Impact of the Lead/Lag Times on the Project Duration Estimates in Highway Construction

Authors

  • Mohamed Abdel Raheem Department of Civil Engineering, University of Texas Rio Grande Valley, Edinburg, TX, 78539, the U.S.
  • Jennifer Reyes Department of Civil Engineering, University of Texas Rio Grande Valley, Edinburg, TX, 78539, the U.S.
  • Xiaohui Wang School of Mathematical & Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, the U.S.
  • Grecia Silva Sanchez Department of Civil Engineering, University of Texas Rio Grande Valley, Edinburg, TX, 78539, the U.S.
  • Alyssa Marie Garza Department of Civil Engineering, University of Texas Rio Grande Valley, Edinburg, TX, 78539, the U.S.

DOI:

https://doi.org/10.30564/jaeser.v4i3.3383

Abstract

The literature mentions multiple factors that can affect the accuracy of estimating the project duration in highway construction, such as weather, location, and soil conditions. However, there are other factors that have not been explored, yet they can have significant impact on the accuracy of the project time estimate. Recently, TxDOT raised a concern regarding the importance of the proper estimating of the lead/lag times in project schedules. These lead/lag times are often determined based on the engineer’s experience. However, inaccurate estimates of the lead/lag time can result in unrealistic project durations. In order to investigate this claim, the study utilizes four time sensitivity measures (TSM), namely the Criticality Index (CI), Significance Index (SI), Cruciality Index (CRI), and the Schedule Sensitivity Index (SSI) to statistically analyze and draw conclusions regarding the impact of the lead/lag time estimates on the total duration in highway projects. An Excel-based scheduling software was developed with Monte Carlo simulation capabilities to calculate these TSM. The results from this paper show that the variability of some lead/lag times can significantly impact the accuracy of the estimated total project duration. It was concluded that the current practices used for estimating the lead/lag times are insufficient. As such, it is recommended to utilize more robust methods, such as the time sensitivity measures, to accurately estimate the lead/lad times in the projects scheduled.

Keywords:

Scheduling; Highway Projects; Lead; Lag; Time Sensitivity Measures

References

[1] Federal Highway Administration [FHWA] Legislations and Regulations, Technical Advisory (T5080.15). “Construction Contract Time Determination Procedures”, Retrieved from https://www.fhwa.dot.gov/construction/contracts/t508015.cfm, 23 September, 2017.

[2] Texas Department of Transportation (TxDOT). (2016). Enhanced Production Rate Establishment to Ascertain Construction Activity Durations (Deliverable Based).

[3] PMBOK (2004) A guide to the project management body of knowledge, 3rd edn. Project Management Institute, Inc., Newtown Square.

[4] Koehn, E., & Brown, G. (1985). Climatic Effects on Construction. Journal of Construction Engineering and Management, 111(2), 129-137.

[5] Oglesby, C. H., Parker, H. W., and Howell, G. A. (1989). Productivity improvement in construction. McGraw-Hill Publishing Inc., New York, NY.

[6] Jiang, Y., & Wu, H. (2007). Production rates of highway construction activities. International Journal of Construction Education and Research, 3(2), 81-98.

[7] Pan, N. (2005). Assessment of productivity and duration of highway construction activities subject to impact of rain. Expert Systems with Applications, 28(2), 313-326.

[8] Shahin, A., Abourizk, S., Mohamed, Y., & Fernando, S. (2007). A simulation-based framework for quantifying the cold regions weather impacts on construction schedules. 2007 Winter Simulation Conference.

[9] El-Rayes, K., & Moselhi, O. (2001). Impact of rainfall on the productivity of highway construction. Journal of construction engineering and management, 127(2), 125-131.

[10] Jung, M., Park, M., Lee, H., & Kim, H. (2016). Weather-Delay Simulation Model Based on Vertical Weather Profile for High-Rise Building Construction. Journal of Construction Engineering and Management, 142(6), 04016007.

[11] Chong, W. K., Lee, S. H., & O’Connor, J. T. (2011). Estimating highway construction production rates during design: Elements of a useful estimation tool. Leadership and Management in Engineering, 11(3), 258-266.

[12] Koehn, E., & Ahmed, F. (2001). Production rates for urban/rural projects in developing areas. AACE International Transactions, IN31.

[13] Jiang, Y., & Wu, H. (2007). Production rates of highway construction activities. International Journal of Construction Education and Research, 3(2), 81-98.

[14] Jiang, Y. (2003). The effects of traffic flow rates at freeway work zones on asphalt pavement construction productivity. In Journal of the Transportation Research Forum (Vol. 57, No. 3, pp. 83-103).

[15] Edara, P. K., & Cottrell, B. H. (2007, January). Estimation of traffic mobility impacts at work zones: state of the practice. In Proceedings of the Transportation Research Board 2007 Annual Meeting.

[16] Odeh, A. M., & Battaineh, H. T. (2002). Causes of construction delay: traditional contracts. International journal of project management, 20(1), 67-73.

[17] Smith, S. D. (1999). Earthmoving productivity estimation using linear regression techniques. Journal of Construction Engineering and Management, 125(3), 133-141.

[18] Goodrum, P. M., & Haas, C. T. (2002). Partial factor productivity and equipment technology change at activity level in US construction industry. Journal of construction engineering and management, 128(6), 463-472.

[19] Goodrum, P. M., & Haas, C. T. (2004). Long-term impact of equipment technology on labor productivity in the US construction industry at the activity level. Journal of construction engineering and management, 130(1), 124-133.

[20] Ok, S. C., & Sinha, S. K. (2006). Construction equipment productivity estimation using artificial neural network model. Construction Management and Economics, 24(10), 1029-1044.

[21] Sonmez, R., & Rowings, J. E. (1998). Construction labor productivity modeling with neural networks. Journal of Construction Engineering and Management, 124(6), 498-504.

[22] O’Connor, J. T., & Huh, Y. (2006). Crew production rates for contract time estimation: Beam erection, deck, and rail of highway bridges. Journal of Construction Engineering and Management, 132(4), 408- 415.

[23] Sanders, S. R., & Thomas, H. R. (1993). Masonry productivity forecasting model. Journal of construction engineering and management, 119(1), 163-179.

[24] Riley, D. R., Thomas, H. R., & Sanvido, V. E. (1999). Loss of labor productivity due to delivery methods and weather. Journal of Construction Engineering and Management, 125(1), 39-46.

[25] Mostafavi, A., Valentin, V., Abraham, D. M., & Louis, J. (2012). Assessment of the productivity of nighttime asphalt paving operations. Journal of Construction Engineering and Management, 138(12), 1421- 1432.

[26] Zhou, J., Love, P. E., Wang, X., Teo, K. L., & Irani, Z. (2013). A review of methods and algorithms for optimizing construction scheduling. Journal of the Operational Research Society, 64(8), 1091-1105.

[27] Yamin, R. A., & Harmelink, D. J. (2001). Comparison of linear scheduling model (LSM) and critical path method (CPM). Journal of Construction Engineering and Management, 127(5), 374-381.

[28] Kelley Jr, J. E., & Walker, M. R. (1959, December). Critical-path planning and scheduling. In Papers presented at the December 1-3, 1959, eastern joint IREAIEE-ACM computer.

[29] Callahan, M. T., Quackenbush, D. G., & Rowings, J. E. (1992). Construction Project Scheduling.

[30] Cottrell, W. D. (1999). Simplified program evaluation and review technique (PERT). Journal of construction Engineering and Management, 125(1), 16-22.

[31] Moder, J. J., Phillips, C. R., & Davis, E. W. (1983). Project management with CPM, PERT, and precedence diagramming.

[32] Golenko-Ginzburg, D. (1988). On the distribution of activity time in PERT. Journal of the Operational Research Society, 39(8), 767-771.

[33] Mohan, S., Gopalakrishnan, M., Balasubramanian, H., & Chandrashekar, A. (2007). A lognormal approximation of activity duration in PERT using two time estimates. Journal of the Operational Research Society, 58(6), 827-831.

[34] Hancher, D. E., McFarland, W. F., & Alabay, R. T. (1992). Construction contract time determination.

[35] McCrary, S. W., Corley, M. R., Leslie, D. A., & Aparajithan, S. (1995). Evaluation of contract time estimation and contracting procedures for Louisiana Department of Transportation and Development construction projects (No. Report No. 296).

[36] Werkmeister, R., Luscher, B., & Hancher, D. (2000). Kentucky contract time determination system. Transportation Research Record: Journal of the Transportation Research Board, (1712), 185-195.

[37] Jiang, Y., & Wu, H. (2004). Determination of INDOT highway construction production rates and estimation of contract times.

[38] Jeong,, H. S., Oberlender,, G., Atreya, S., & Akella, V. (2008). Development of an Improved System for Contract Time Determination (Phase I & Ii) (pp. 1-148, Rep. No. FHWA-OK-08-02).

[39] O’Connor, J. T., Chong, W. K., Huh, Y., & Kuo, Y. C. (2004). Development of improved information for estimating construction time (No. FHWA/TX-05/0- 4416-1).

[40] Aoun, D. G. (2013). Developing Highway Construction Production Rates of Wisconsin Department of Transportation (Doctoral dissertation, UNIVERSITY OF WISCONSIN-MADISON).

[41] Herbsman Z and Ellis R., “NCHRP Synthesis of Highway Practice 215: Determination of Contract Time for Highway Construction Projects”, Transportation Research Board, Washington, D.C., 1995.

[42] Stoll, B. L., O’Reilly, J. E., & Bell, L. C. (2006). Methodologies for Determining Construction Contract Time and Evaluating Contract Time Extensions (No. FHWA-SC-06-01).

[43] Taylor, T. R., Sturgill Jr, R. E., & Li, Y. (2017). Practices for Establishing Contract Completion Dates for Highway Projects (No. Project 20-05, Topic 47-09).

[44] Vanhoucke, M. (2012). Project management with dynamic scheduling. Springer Berlin Heidelberg.

[45] Martin, J. J. (1965). Distribution of the time through a directed, acyclic network. Operations Research, 13(1), 46-66.

[46] Vanhoucke, M. (2010). Using activity sensitivity and network topology information to monitor project time performance. Omega, 38(5), 359-370.

[47] Dodin, B. M., & Elmaghraby, S. E. (1985). Approximating the criticality indices of the activities in PERT networks. Management Science, 31(2), 207-223.

[48] Ghomi, S. F., & Teimouri, E. (2002). Path critical index and activity critical index in PERT networks. European Journal of Operational Research, 141(1), 147-152.

[49] Cho, J. G., & Yum, B. J. (2004). Functional estimation of activity criticality indices and sensitivity analysis of expected project completion time. Journal of the Operational Research Society, 55(8), 850-859.

[50] Williams, T. M. (1992). Criticality in stochasticnetworks. Journal of the Operational Research Society, 43(4), 353-357.

[51] Elmaghraby, S. E. (2000). On criticality and sensitivity in activity networks. European Journal of Operational Research, 127(2), 220-238.

[52] Elshaer, R. (2013). Impact of sensitivity information on the prediction of project’s duration using earned schedule method. International Journal of Project Management, 31(4), 579-588.

[53] Larson, E. W., & Gray, C. F. (2015). A Guide to the Project Management Body of Knowledge: PMBOK (®) Guide. Project Management Institute.

[54] Kwak, Y. H., & Ingall, L. (2007). Exploring Monte Carlo simulation applications for project management. Risk Management, 9(1), 44-57.

[55] Johnson, D. (1997). The triangular distribution as a proxy for the beta distribution in risk analysis. Journal of the Royal Statistical Society: Series D (The Statistician), 46(3), 387-398.

[56] Tysiak, W., & Sereseanu, A. (2010). Project risk management using Monte Carlo simulation and Excel. International Journal of Computing, 9(4), 362- 367.

[57] Lee, D. E. (2005). Probability of project completion using stochastic project scheduling simulation. Journal of construction engineering and management, 131(3), 310-318.

[58] Finley, E. D., & Fisher, D. J. (1994). Project scheduling risk assessment using Monte Carlo methods. Cost Engineering, 36(10), 24.

[59] Hulett, D. T. (1996). Schedule risk analysis simplified. Project Management Journal.

[60] Akintoye, A. S., & MacLeod, M. J. (1997). Risk analysis and management in construction. International Journal of Project Management, 15(1), 31-38.

[61] Al-Bahar, J. F., & Crandall, K. C. (1990). Systematic risk management approach for construction projects. Journal of Construction Engineering and Management, 116(3), 533-546.

[62] Dawood, N. (1998). Estimating project and activity duration: a risk management approach using network analysis. Construction Management & Economics, 16(1), 41-48.

[63] Edwards, P. J., & Bowen, P. A. (1998). Risk and risk management in construction: a review and future directions for research. Engineering, Construction and Architectural Management, 5(4), 339-349.

[64] Sadeghi, N., Fayek, A. R., & Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer-Aided Civil and Infrastructure Engineering, 25(4), 238-252.

[65] Tokdemir, O. B., Erol, H., & Dikmen, I. (2019). Delay risk assessment of repetitive construction projects using line-of-balance scheduling and Monte Carlo simulation. Journal of Construction Engineering and Management, 145(2), 04018132.

[66] Paz, J. C., Rozenboim, D., Cuadros, Á., Cano, S., & Escobar, J. W. (2018). A simulation-based scheduling methodology for construction projects considering the potential impacts of delay risks. Construction Economics and Building, 18(2), 41.

[67] Francis, A. (2017). Simulating uncertainties in construction projects with chronographical scheduling logic. Journal of Construction Engineering and Management, 143(1), 04016085.

[68] Kerkhove, L. P., & Vanhoucke, M. (2017). Optimised scheduling for weather sensitive offshore construction projects. Omega, 66, 58-78.

[69] Choudhry, R. M. (2019). Risk Analysis Related to Cost and Schedule for a Bridge Construction Project. In Perspectives on Risk, Assessment and Management Paradigms. IntechOpen.

[70] Arunmohan, A. M., & Lakshmi, M. (2018). Analysis of modern construction projects using montecarlo simulation technique. International Journal of Engineering & Technology, 7(2.19), 41-44.

[71] Wali, K. I., & Othman, S. A. (2019). Schedule Risk Analysis Using Monte Carlo Simulation for Residential Projects. ZANCO Journal of Pure and Applied Sciences, 31(5), 90-103.

[72] Prateapusanond, A. (2003). A comprehensive practice of total float pre-allocation and management for the application of a CPM-based construction contract (Doctoral dissertation, Virginia Tech).

[73] Neely, L. (2017). Project Scheduling Disputes: Expert Characterization and Estimate Aggregation (Doctoral dissertation).

[74] Goodrum PM, Haas CT. Partial factor productivity and equipment technology change at activity level in US construction industry. Journal of construction engineering and management. 2002 Dec;128(6):463- 72.

Downloads

How to Cite

Raheem, M. A., Reyes, J., Wang, X., Sanchez, G. S., & Garza, A. M. (2021). Assessing the Impact of the Lead/Lag Times on the Project Duration Estimates in Highway Construction. Journal of Architectural Environment & Structural Engineering Research, 4(3), 47–62. https://doi.org/10.30564/jaeser.v4i3.3383

Issue

Vol. 4 , Iss. 3 (July 2021)

Article Type

Article

License

Copyright © 2021 Mohamed Abdel Raheem, Jennifer Reyes, Xiaohui Wang, Grecia Silva Sanchez, Alyssa Marie Garza

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC