Highway Project Clustering Using Unsupervised Machine Learning Approach
Publication: Computing in Civil Engineering 2021
ABSTRACT
Many studies were conducted to predict project time, cost, and risks using historical data of similar past projects, assuming similar projects within the same group follow similar patterns. However, the accuracy of prediction models depends on the project grouping accuracy in categorizing similar projects together. Current practice in US highway agencies indicates categorizing projects based on similar activities into different work types, which can be challenging due to the lack of a solid definition of project work types and the variety of projects’ activities. Besides, the accuracy of categorization with this method has not been determined. This paper develops a K-means clustering model to group similar highway projects together. The Euclidian distance was used as a metric to detect similarity and evaluate the performance of the method. The historical data of 730 projects from a highway agency in the US was used to validate the model and compare the performance of the model with the current categorization method used in US highway agencies based on project work type. The comparison showed the superiority of K-means clustering in grouping similar projects together. The proposed reliable clustering model can be used in future studies to increase the accuracy of prediction models that use the project type as an influential factor.
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REFERENCES
Abdelaty, A., Jeong, D., Dannen, B., Todey, F., and Jeong, H. D. (2016). Construction Management and Economics Enhancing life cycle cost analysis with a novel cost classification framework for pavement rehabilitation projects Enhancing life cycle cost analysis with a novel cost classification framework for pavement rehabilitation projects. https://doi.org/10.1080/01446193.2016.1205206.
Abdel-Raheem, M., and Reyes, J. (2020). Investigation of the Contract Time Determination Systems Used for Highway Projects in the U.S.A. Transportation Research Record: Journal of the Transportation Research Board, 2674(10), 715–728. https://doi.org/10.1177/0361198120939098.
Alikhani, H., Le, C., and David Jeong, H. (2020). A Deep Learning Algorithms to Generate Activity Sequences Using Historical As-built Schedule Data. 39. https://doi.org/10.3311/CCC2020-039.
Attal, A. (2010). Development of Neural Network Models for Prediction of Highway Construction Cost and Project Duration. Undefined.
Aziz, A. M. A. (2009). Time prediction for highway pavement projects using regression analysis. Building a Sustainable Future - Proceedings of the 2009 Construction Research Congress, 896–905. https://doi.org/10.1061/41020(339)91.
Aziz, R. F. (2013). Ranking of delay factors in construction projects after Egyptian revolution. Alexandria Engineering Journal, 52(3), 387–406. https://doi.org/10.1016/j.aej.2013.03.002.
Czarnigowska, A., and Sobotka, A. (2014). Estimating Construction Duration for Public Roads During the Preplanning Phase. Journal of Engineering, Project, and Production Management, 4(1), 26–35. https://doi.org/10.32738/jeppm.201401.0004.
Dai, Y., Lu, W., Huang, H., and Liu, L. (2019). Threshold division of urban road network traffic state based on macroscopic fundamental diagram and k-means clustering. ICTE 2019 - Proceedings of the 6th International Conference on Transportation Engineering, 31–39. https://doi.org/10.1061/9780784482742.005.
FHWA. (2002). FHWA Guide for Construction Contract Time Determination Procedures - Contract Administration - Construction - Federal Highway Administration. https://www.fhwa.dot.gov/construction/contracts/t508015.cfm.
Gondia, A., Siam, A., El-Dakhakhni, W., and Nassar, A. H. (2019). Machine Learning Algorithms for Construction Projects Delay Risk Prediction. https://doi.org/10.1061/(ASCE).
Gransberg, D. D., David, H., Karaca, J. I., and Gardner, B. (2017). Top-down construction cost estimating model using an artificial neural network.
Iowa DOT. (2018). Iowa transportation improvement program. https://www.iowadot.gov/program_management/FINAL-2018-2022-5YrProg.pdf.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011.
Jeong, H. D., and Alikhani, H. (2020). Activity Sequencing Logics Using Daily Work Report Data. Montana. Dept. of Transportation. Research Programs. https://doi.org/10.21949/1518308.
Jeong, H. S., Atreya, S., Oberlender, G. D., and Chung, B. Y. (2009). Automated contract time determination system for highway projects. Automation in Construction, 18(7), 957–965. https://doi.org/10.1016/j.autcon.2009.04.004.
Jumadi, B., Sitompul, D., Sitompul, O. S., and Sihombing, P. (2019). Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algorithm. 12015. https://doi.org/10.1088/1742-6596/1235/1/012015.
Kapil, S., and Chawla, M. (2017, February 13). Performance evaluation of K-means clustering algorithm with various distance metrics. 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016. https://doi.org/10.1109/ICPEICES.2016.7853264.
Kapousouz, E., Seyrfar, A., Derrible, S., and Ataei, H. (2021). A Clustering Analysis of Energy and Water Consumption in US States from 1985 to 2015.
Karaca, I., Gransberg, D. D., and Jeong, H. D. (2020). Improving the Accuracy of Early Cost Estimates on Transportation Infrastructure Projects. Journal of Management in Engineering, 36(5), 04020063. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000819.
Karimzadeh, A., Sabeti, S., and Shoghli, O. (2020). Clustering-Based Similarity Detection of Pavement Segments Considering Multiple Contributors to Deterioration.
Le, C., Shrestha, K. J., Jeong, H. D., and Damnjanovic, I. (2021). A sequential pattern mining driven framework for developing construction logic knowledge bases. Automation in Construction, 121, 103439. https://doi.org/10.1016/j.autcon.2020.103439.
Liu, Y., Li, W., and Li, Y. (2008). Network Traffic Classification Using K-means Clustering. 360–365. https://doi.org/10.1109/imsccs.2007.52.
Müller, R., and Turner, R. (2007). The Influence of Project Managers on Project Success Criteria and Project Success by Type of Project. European Management Journal, 25(4), 298–309. https://doi.org/10.1016/j.emj.2007.06.003.
NY DOT. (2014). Highway design manual, Chapter 7- Resurfacing, Restoration And Rehabilitation (1R, 2R & 3R). https://www.dot.ny.gov/divisions/engineering/design/dqab/hdm/hdm-repository/chapt_07a.pdf.
Ohio DOT. (2020). Contract Time Determination Tool. http://www.dot.state.oh.us/Divisions/ConstructionMgt/Admin/Documents/Forms/DispForm.aspx?ID=554.
Okere, G. (2018). Evaluating the allocation of contingency on state DOT projects based on project types and rate of cost overruns. Asian Journal of Civil Engineering, 19(4), 463–472. https://doi.org/10.1007/s42107-018-0042-3.
Okere, G. (2019). An Evaluation of a Predictive Conceptual Method for Contract Time Determination on Highway Projects Based on Project Types. International Journal of Civil Engineering, 17(7), 1057–1073. https://doi.org/10.1007/s40999-018-0357-1.
Shenhar, A. J., Dvir, D., Levy, O., and Maltz, A. C. (2001). Project success: A multidimensional strategic concept. Long Range Planning, 34(6), 699–725. https://doi.org/10.1016/S0024-6301(01)00097-8.
Shrestha, K. J., Le, C., Jeong, H. D., and Le, T. (2019). Mining Daily Work Report Data for Detecting Patterns of Construction Sequences. 578–583. https://doi.org/10.3311/ccc2019-079.
Skitmore, R. M., and Ng, S. T. (2003). Forecast models for actual construction time and cost. Building and Environment, 38(8), 1075–1083. https://doi.org/10.1016/S0360-1323(03)00067-2.
Son, J., Khwaja, N., and Milligan, D. S. (2019). Planning-Phase Estimation of Construction Time for a Large Portfolio of Highway Projects. Journal of Construction Engineering and Management, 145(4), 04019018. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001637.
Taylor, T. R. B., Paul, P. E., Goodrum, M., Brockman, P. E. M., Bishop, E. I. T. B., Shan, E. I. T. Y., Roy, E. I. T., Sturgill, E., and Hout, P. E. K. (2013). Updating the Kentucky Contract Time Determination System.
Texas DOT. (2018). Contract Time Determination guidance.
Wisconsin DOT. (2015). Highway improvement type defenition. https://wisconsindot.gov/Documents/doing-bus/local-gov/astnce-pgms/highway/tools/definitions.pdf.
Zhai, D., Shan, Y., Sturgill, R. E., Taylor, T. R. B., and Goodrum, P. M. (2016). Using Parametric Modeling to Estimate Highway Construction Contract Time. Transportation Research Record: Journal of the Transportation Research Board, 2573(1), 1–9. https://doi.org/10.3141/2573-01.
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Computing in Civil Engineering 2021
Pages: 172 - 179
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Published online: May 24, 2022
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