Degree Type

Thesis

Date of Award

2014

Degree Name

Master of Science

Department

Civil, Construction, and Environmental Engineering

First Advisor

Douglas D. Gransberg

Abstract

Preconstruction Services (PCS) is defined as all work completed on a project once it has been authorized for funding and cost related to the project can be charged accordingly, up until construction contract is awarded. Due to the changing nature of State DOT work with increased funding uncertainties and shrinking budgets it is more important than ever to ensure proper allocation of funds for highway projects. Uneducated estimates for preconstruction services or using a fixed percentage across multiple projects can lead to a misallocation of available capital funding in the PCS phase, which may force the need to redistribute funding late in an agency's fiscal year to cover overages and to expend underruns before the authorization expires. Underestimation can lead to inadequate PCS budgets and poor construction documents. In short an educated thought out PCS cost estimate can lead to cost certainty within all aspects of a project.

Firstly the research focuses on developing a framework for a PCS cost estimate focusing on the type of estimate and the factors that affect it. Second, an artificial neural network model is proposed to estimate PCS costs, the research also investigates a method for defining projects to further refine the historic data that is used in the NN model. Finally the research focuses on a method to estimate a design cost contingency.

Two types of estimates were found top-down and bottom-up estimates the difference in the estimate was dependent on the end user and the amount of data available. Three factors complexity, project type and construction cost were found to be the three factors that had a major influence on PCS cost estimate. The NN model produce provided a top-down PCS estimate, the final model provided estimates with a weighted error of 1.4% over 13 projects. Iowa DOT's method of classifying projects based on project complexity was investigated and determined to be an appropriate method for project classification considering project complexity was considered a major influence factor. Finally a method was determined to estimate an appropriate design contingency using design cost estimate accuracy index. All methods and models developed in this thesis are expected to be applied to individual agency's historic data and estimating systems. It is also stressed that models have limitations and should not be used outside the range which it was developed.

DOI

https://doi.org/10.31274/etd-180810-17

Copyright Owner

Kate Hunter

Language

en

File Format

application/pdf

File Size

116 pages

Included in

Engineering Commons

Share

COinS