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Using Historical Bid Data for Enhanced Conceptual Estimating

8 pagesPublished: May 15, 2022


Several small and medium-size contractors store bid day data regarding potential projects creating large datasets of bid day information without meaningful utilization. Many of these companies fail to leverage the archived bid day data because of their format or lack of effort to use historical data. Thus, most conceptual estimates are done using personal judgment and experience with little to no historical data support. Because of this approach, many small and medium-sized companies lack a data-driven approach to develop conceptual estimates. As such, this study aims at leveraging historical bid data to build a data-driven approach for creating conceptual estimates. This objective is achieved by presenting a framework for one company's historical bid day data to develop a conceptual cost estimating model. The framework uses bid day data for the past 45 years to build a data-driven conceptual estimating model using a case-based reasoning approach. The model allows estimators to retrieve the most similar projects from a historical database to create an informed conceptual estimate for potential projects. It is expected that this research will help many small and medium-size contractors leverage their historical bid data by utilizing it.

Keyphrases: Conceptual estimating, Data Analytics, preconstruction services, project comparison

In: Tom Leathem, Wesley Collins and Anthony J. Perrenoud (editors). ASC2022. 58th Annual Associated Schools of Construction International Conference, vol 3, pages 192--199

BibTeX entry
  author    = {Ahmed Abdelaty and Kevin Nessalhauf and Nathan Munie},
  title     = {Using Historical Bid Data for Enhanced Conceptual Estimating},
  booktitle = {ASC2022. 58th Annual Associated Schools of Construction International Conference},
  editor    = {Tom Leathem and Wes Collins and Anthony Perrenoud},
  series    = {EPiC Series in Built Environment},
  volume    = {3},
  pages     = {192--199},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2632-881X},
  url       = {},
  doi       = {10.29007/7x4j}}
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