Preconstruction Support Cost Hours Estimating on Caltrans Pavement Rehabilitation Projects

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Preconstruction Support Cost Hours Estimating on Caltrans Pavement Rehabilitation Projects


Because the construction phase accounts for the majority of project costs for pavement rehabilitation projects, most research on infrastructure project cost estimating focuses on that phase, rather than on the preconstruction phases. Nevertheless, costs incurred prior to construction, referred to in this report as "preconstruction costs" are significant and worthy of consideration (See Section 2.1 of the report for a more detailed and precise definition of preconstruction). In the 2020–2021 fiscal year, for instance, the California Department of Transportation (Caltrans) spent more than $169 million on preconstruction work for pavement rehabilitation projects. This report presents the results of a study of preconstruction cost estimating for pavement rehabilitation projects undertaken by Caltrans. It uses data on the 139 pavement rehabilitation projects for which Caltrans opened bids in the five-year period from April 26, 2016 to May 11, 2021. A data set was developed that combined the preconstruction hours for each project with the primary bid items for the pavement rehabilitation projects. Two models were developed to estimate preconstruction hours from the bid items, one using an Artificial Neural Network (ANN) and the other a parametric exponential model developed using multiple regression. The models had coefficients of determination of 0.85 and 0.80, respectively. Tools were then developed to assist professional users in validating their preconstruction cost estimates using each of the models. CTC staff or Caltrans can use these tools to evaluate the reasonableness of the preconstruction estimate on an individual project, or on the sum of an entire biennial SHOPP pavement rehabilitation portfolio, in order to assure the most efficient use of infrastructure funding to best serve the community's transportation needs.

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Dr. Blampied’s research and teaching focuses on project management in public transportation agencies. He teaches in the Master of Transportation Management program at San José State University and is a Research Associate at the Mineta Transportation Institute. His doctoral dissertation at the University of California, Berkeley discussed parametric cost estimating at the early conceptual phase of projects, the subject, in part, of this report, and used data from the Caltrans State Highway Operation and Protection Program (SHOPP), as does this report. While his earlier work considered pedestrian accessibility projects in the SHOPP, this report focuses on pavement rehabilitation projects.  


Dr. Shehab is an expert in developing cost estimating applications and the field of artificial intelligence. He has more than 25 years of experience in developing intelligent cost estimating applications, during which he developed systems for highway projects, utility pipe networks, and school buildings. His contributions included also the determination of key factors that attribute to the maintenance costs of major infrastructure facilities. He is the author and co-author of numerous articles published in many ASCE journals and other equally prestigious periodicals.


Dr. Nasr is a Subject Matter Expert in Construction and Project Management. He built his career of over three decades with dual careers in academia and industry. He is a Professor of Construction Engineering Management at California State University, Long Beach. Previously, he served as Vice Provost, Interim Provost, and Vice President of Academic Affairs at Florida Polytechnic University, industry (California Department of Transportation (Caltrans)) and various national and international consulting engagements where he initiated, developed, and delivered hundreds of specialized and customized companywide project management training programs for large-scale professional organizations, in both private and government sectors.


Laxmi Sindhu Samudrala received her Master’s degree in Data Analytics from San José State University and is currently working for PayPal Inc. as a software engineer in the Product Security Team. Her work involves data engineering and machine learning, and her interests lie in machine learning and artificial intelligence. She helped assemble the data sets for this report, and she has also worked on projects requiring object detection and behavior classification for intelligent autonomous vehicle safety.

May 2023
Cost estimating
Neural networks
Regression analysis
Planning and design



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