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Towards a Geotechnical Input-Based Conceptual Cost Estimating Methodology for Highway Projects

Amadi Alolote Ibim

Abstract


The trend in the literature reveals a wide range of innovative approaches which have been proposed to improve the accuracy of conceptual estimates. These techniques are usually based on differing analytical approaches. Although these techniques obtain estimates that closely approximate actual costs, the requisite time and resources to gather sufficient data or obtain relevant information during initial project stages may not be possible. The study proposes that for a road project whose route has been identified during planning, pending budgetary authorization, the basic geologic data that defines the subsoil conditions along the proposed route as revealed from desk studies and preliminary surveys, should be made to bear upon the pavement cost estimate developed at this point using a more deterministic base estimate costing approach. This involves using a cost prototype methodology whereby relative cost adjustment factors are computed for each segregated soil type aligning with the geologic zones, which will be used as multipliers during the preliminary costing of road works. The utility of the proposed methodology will thus be improved by the carrying out of more comprehensive and detailed engineering geological mapping of the specific region and setting up of a geotechnical database. The suggested conceptual costing approach has the potential to function as both a risk management instrument and a predictive foundation for estimating costs in diverse terrains.


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References


Aibinu, A. A., and Pasco, T. (2008). The accuracy of pre-tender building cost estimates in Australia. Construction Management & Economics, 26(12), 1257–1269. doi: 10.1080/01446190802527514.

Al-Tabtabai, H., Alex, A.P and Tantash, M. (1999). Preliminary cost estimation of highway construction using neural networks. Analysis and Manufacturing, 11(1), 45-58.

Anderson, S., Molenaar, K., and Schexnayder, C. (2006). “Guidance for cost estimation and management for highway projects during planning, programming, and preconstruction.” Final Report 574, National Cooperative Highway Research Program (NCHRP), Washington, DC.

Association for the Advancement of Cost Engineering (AACE, 1997) Recommended Practice No. 17R-97

Cantarelli, C.C., Flyvbjerg, B., Molin, E.J.E and Van Wee ,B. (2010) Cost Overruns in Large-scale Transportation Infrastructure Projects: Explanations and Their Theoretical Embeddedness. EJTIR 10(1), March, pp. 5-18

Chou, J. (2009) Expert Systems with Applications Web-based CBR system applied to early cost budgeting for pavement maintenance project (36) pp. 2947–2960

Chou, J. (2005). Item-level quantity-based preliminary cost estimating system for highway earthwork, landscape, subgrade treatments, base, surface courses, pavement and traffic control. Doctoral dissertation presented to the faculty of the graduate school of the University of Texas at

Chou, J. (2010). Cost simulation in an item-based project involving construction engineering and management. International Journal of Project Management 29 (2011) 706–717

consulting Committee on Cost Engineering, 1983).

Creedy. G.D.2006. Risk Factors leading to cost overruns in the delivery of highway construction projects. An unpublished PhD thesis submitted to the Queensland University of Technology.

Department of Energy (DOE) cost estimating Guide 413.3B, Program and Project Management for the Acquisition of Capital Assets, U.S. Department of Energy Washington, D.C. 20585 11-29-10

Dysert, L. R. (2005). So You Think You’re an Estimator? AACE International Transactions, 1 (6).

Flyvbjerg, B., Bruzelius, N., and Rothengatter, W. (2003) Megaprojects and Risk: An Anatomy of Ambition, Cambridge University Press, Cambridge.

Government Accountability Office, (2009) Cost Estimating and Assessment Guide, Best Practices for Developing and Managing Capital Program Costs, GAO-09-3SP, United States.

Institution of Civil Engineers. (1999). Managing Geotechnical Risk. Thomas Telford

Kim, G., An,, and Kang, K. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment Volume 39, Issue 10. Pages 1235–1242.

Kim, K. and Kim, K. (2010). Preliminary cost estimation model using case-based reasoning and genetic algorithms. Journal of Computer and Civil Engineering, 24(6), 499-505.

Kolodner J. L. (1992) An introduction to case-based reasoning. Artificial Intelligence Review;6:3–34.

Lowe, D., Emsley, M., and Harding, A. (2006). ”Predicting Construction Cost Using Multiple Regression Techniques.” Journal of Construction Engineering Management. 132(7), 750–758

Maher, M. L and Balachandran B. (1993). Multimedia approach to case-based structure design. Journal of Computing in Civil Engineering 8(3):359–76.

Mazouz, S and Zerouala , M. S. (2001).The integration of environmental variables in the process of architectural design: the contribution of expert systems. Energy and Buildings;33(7):699–710.

National Aeronautics And Space Administration (NASA) (2013) Analytic Method for Probabilistic Cost and Schedule Risk Analysis. Final Report Number: NNH12PV48D.

Oberlender, G. D., Trost, S. M. (2001). Predicting accuracy of early cost estimates based on estimate quality. Journal of Construction Engineering and Management, 127(3), 173–182.

Rao and Ranade, (2013) risk based estimation of capital intensive road projects using Monte-Carlo simulation. International Public Works Conference, Darwin, Northern Territory, Australia

Romero, V. S. and J. M. Stolz, (2009). Cost Estimating For Underground Transit: Too Dangerous To “Guesstimate”. San Francisco, CA

Rouhana, K. G. (1995). Neural networks versus parameter based applications in cost estimating. Cost Engineering, 37(2), 14–18

Salem, O., AbouRizk, S., Ariaratnam, S., 2003. Risk-based life-cycle costing of infrastructure rehabilitation and construction alternatives. Journal of Infrastructure Systems 9, 6–15.

Sanders, S., and Maxwell, R., Glagola, C., 1(992.) “Preliminary Estimating Models for Infrastructure Projects,” Cost Engineering, Association for the Advancement of Cost Engineering International, Morgantown, WV, 34(8), 7-13,

Schexnayder, C.J., Weber, L.S and Fiori, C. (2003) Project Cost Estimating: A Synthesis Of Highway Practice. American Association of State Highway and Transportation Officials (AASHTO)

Smith, A. and Mason, K. (1997). Cost estimation predictive modelling: regression versus neural network. The Engineering Economist. 42(2), pages 137-161

Touran, A.. (1993). “Probabilistic Cost Estimating with Subjective Correlations,” Journal of Construction Engineering and Management, American Society of Civil Engineers, New York, NY, 119(1), 58-71.

Trost, S and Oberlender, G. (2003). Predicting accuracy of early cost estimates using factor analysis and multivariate Regression. Journal of Construction Engineering and Management, 129(2), 198-204.

Turochy, R. E., Lester A. H., Lacy, L. A. and Robert S. D. (2001). Highway project cost estimating methods used in the planning stage of project development. Technical Assistance Report. Virginia Transportation Research Council

Wall, D.M., 1997. Distributions and correlations in Monte-Carlo simulation. Construction Management and Economics 15, 241–258.

Watson I. (1995). Case-based cost estimation, case-based reasoning: prospects for application. IEE Colloquium pp. 1–5.

Wenhua, L and Yuwen, L. (1999). “Study on Forecasting Method of Construction Cost Based on Artificial Neural Network”. Chinese Journal of Management Science, 4, 29-34, April


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