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Project Details
STATUS

In-Progress

START DATE

01/01/15

END DATE

12/31/15

RESEARCH CENTERS InTrans
SPONSORS

Montana Department of Transportation

Researchers
Principal Investigator
Peter Savolainen

Affiliate Researcher

About the research

Subcontractor to Wayne State University on this project.

Project Details
STATUS

In-Progress

START DATE

02/09/15

END DATE

12/31/17

RESEARCH CENTERS InTrans, CMAT
SPONSORS

Montana Department of Transportation

Researchers
Principal Investigator
Hyung Seok "David" Jeong

Affiliate Researcher

Co-Principal Investigator
Doug Gransberg

Affiliate Researcher

About the research

The overall objective is to develop a Montana-specific highway construction cost index system and process to update when needed.

Project Details
STATUS

Completed

START DATE

01/09/17

END DATE

01/31/19

RESEARCH CENTERS InTrans, CMAT
SPONSORS

Montana Department of Transportation

Researchers
Principal Investigator
Hyung Seok "David" Jeong

Affiliate Researcher

About the research

Accurate and practical production rate estimates are crucial for an accurate forecast of contract completion time. As costs of highway projects increase with time, the importance of estimating highway construction contract time has increased significantly, thereby emphasizing the need for effective production rates due to the interrelatedness between the two. By reviewing the literature, various aspects of production rate estimation were identified including factors that influence production rates, production rate adjustment factors, and statistical methods, and current practices of the Montana Department of Transportation (MDT). The purpose of this research was to develop historical data-driven estimates of production rates using daily work report (DWR) data in order to enhance current contract time determination practices.

The research team analyzed the MDT’s DWR data along with bid data and GIS data to estimate realistic production rates. Descriptive analysis, regression analysis, and Monte Carlo simulation were deployed to offer insights into historical projects’ characteristics and production rates of 31 controlling activities of MDT. The major findings of the descriptive analysis were statistical measures (i.e., mean, first quartile, median, and third quartile) of 31 controlling activities, which provide more practical, detailed, and updated estimates in comparison with the current published values. In addition, variations of production rates in terms of different seasons of work, districts, area types (urban/rural), and budget types were explored. The study also developed regression equations to estimate production rates of 27 out of 31 controlling activities. For each activity, factors that had a significant effect on production rate were included in the regression model as predictor variables. Besides, a production rate-based method was proposed to evaluate contractor’s performances, and a Microsoft Excel based Production Rate Estimation Tool (PRET) was developed to assist MDT practitioners.

Project Details
STATUS

In-Progress

START DATE

12/22/14

END DATE

06/30/17

RESEARCH CENTERS InTrans, CMAT
SPONSORS

Federal Highway Administration State Planning and Research Funding
Montana Department of Transportation

Researchers
Principal Investigator
Doug Gransberg

Affiliate Researcher

Co-Principal Investigator
Hyung Seok "David" Jeong

Affiliate Researcher

About the research

This report contains the information and background on top-down cost estimating using artificial neural networks (ANNs) to enhance the accuracy of Montana Department of Transportation (MDT) early estimates of construction costs. Upon conducting an extensive review of MDT’s budgeting and cost estimating efforts, and following a survey of agency experts on the identification of the most salient project attributes with the dual-objectives of low effort and high accuracy, a rational method for top-down variable selection is proposed.

Selected variables were further tested in their explanatory power of construction costs through the application of two cost estimating methodologies—multiple regression and artificial neural network methodologies. Both methods are shown to provide sizeable improvements over the agency’s current levels of prediction accuracy for its construction costs. Potential accuracy gains are also demonstrated to depend on project work types. The comparison of mean absolute percentage errors across different estimating methods confirms that the potential benefits from the proposed methodologies are expected to rise as the project level complexity and uncertainty increase. New construction and bridge replacement projects, for instance, are expected to gain the most in estimating accuracy since these two groups seem to exhibit considerably higher levels of deviation from the MDT’s preliminary cost estimates.

To facilitate MDT’s implementation of the suggested methodology described in this report, a cost estimation methodology was also presented in an Excel spreadsheet format. This achieves two goals. First, it provides an accessible tool to make top-down cost predictions for agency planners during the budgeting stage based on MDT’s historical project data. Second, it furnishes a process through which the proposed model can be improved as new project information becomes available. Ultimately, the insights gained from this study are expected to contribute to a better formulation of the agency’s early cost estimation and budgeting efforts.

 

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