Construction Insurance Machine-Learning Estimation Approach for Multiple Attempted Bids
Publication: Construction Research Congress 2022
ABSTRACT
Accurate insurance cost estimation is crucial for contractors. This paper presents a machine-learning decision support tool to estimate optimal insurance premiums for multiple attempted bids. The authors adopted a three-step methodology: (1) construct a Past Project Insurance Database (PPID) including estimated versus post-award premiums; (2) create an Insurance Providers Database (IPD) including insurer attributes and evaluations; and (3) develop a Neural Network Selection Engine (NNSE) for premium estimation. To demonstrate the model, a Class A general contractor in Egypt is presented, featuring 40 of its past projects and 22 insurers, while considering 12 attempted bids. Using the Multilayer Perception (MLP) back-propagation algorithm, the NNSE achieved a training error of 2.2%. Additional validation was performed using the J48 Decision Tree Classifier algorithm, achieving an error of 0.99%. Further validation is recommended to compare actual policy premiums with the results predicted by the model. This paper contributes to the body of knowledge by presenting a machine learning approach for contractors in developing countries to optimize their construction insurance profiles.
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REFERENCES
Assaad, R., El-Adaway, I. H., El Hakea, A. H., Parker, M. J., Henderson, T. I., Salvo, C. R., and Ahmed, M. O. (2020). Contractual perspective for BIM utilization in US construction projects. Journal of Construction Engineering and Management. ASCE. 146(12), 04020128.
Barrett, J. E. (1977). Insurance for Urban Transportation Construction. US Department of Transportation and National Technical Information Service. Springfield, VA.
Bunni, N. G. (2003). Risk and Insurance in Construction. Spon, London.
Dickson, G. C. (1983). An Experimental Study of Attitudes towards Risk in Insurance Purchasing, Doctoral dissertation, Glasgow College of Technology 12(1), pp. 90-100.
Dinku, A. (2000). Insurance requirements and practices of Ethiopia’s construction sector. Journal of EAEA, Vol. 17.
El-Adaway, I., and Kandil, A. (2009). Contractor’s claims insurance: A risk retention approach. Journal of Construction Engineering and Management, Vol. 135, No. 9, pp. 819–825. ASCE.
El-Adaway, I., and Kandil, A. (2010). Construction risks: Single versus portfolio insurance. Journal of Management in Engineering, 26(1); pp. 2–8. ASCE.
Ferreira, C. (2006). “Designing Neural Networks Using Gene Expression Programming”. In A. Abraham, De Baets, B., Koeppen, M. and Nickolay, B., eds., Applied Sofy Computing Technologies: The Challenge of Complexity, pp. 517–536, Springer-Verlag.
FRA (Financial Regulatory Authority). (2019). Yearly Statistical Book on Insurance Activity during Fiscal Year 2018-19. Financial Regulatory Authority. Smart Village, Abu Rawash, Giza, Egypt (Published in Arabic).
Frank, E., Hall, M. A., and Witten, I. H. (2016). The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, Fourth Edition, 2016.
Hansell, D. S. (1974) Elements of Insurance. MacDonald and Evans.
Hatmoko, J. U. D., Astuti, P. K., and Farania, S. N. (2021) Insuring project risks: Contractor expectations versus insurance company policies. International Journal of Technology.
Liu, J., Li, B., Lin, B., and Nguyen, V. (2007) Key issues and challenges of risk management and insurance in China’s construction industry. Industrial Management and Data Systems. Vol 107, No. 3, pp. 382–396. Emerald Group Publishing Limited. https://doi.org/10.1108/02635570710734280, URL: https://emeraldinsight.com/0263-5577.htm.
Liu, J., Li, B., and Zhang, J. (2004). Insurance and construction project risks. Proceedings of the 12th Annual PBFEA Conference. Retrieved form: www.centerforpbbefr.rutgers.edu.
Owusu-Manu, D.-G., Ghansah, F. A., Darko, A., and Asiedu, R. O. (2020). Service quality of insurance in complex project deals in the construction industry in Ghana. International Journal of Building Pathology and Adaptation. Emerald Publishing Limited. https://doi.org/10.1108/IJPBA-09-2019-0078, URL: https://www.emerald.com/insight/2398-4708.htm.
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Construction Research Congress 2022
Pages: 274 - 282
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Published online: Mar 7, 2022
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