A construction cost estimation framework using DNN and validation unit

Journal article


Saeidlou, S. and Ghadiminia, N. 2023. A construction cost estimation framework using DNN and validation unit. Building Research & Information. 51 (3), pp. 241-368. https://doi.org/10.1080/09613218.2023.2196388
AuthorsSaeidlou, S. and Ghadiminia, N.
Abstract

Accurate construction cost estimation is crucial to completing projects within the planned timeframe and expenditure. The estimation process depends on multiple variables maintaining complex relationships between themselves and the target cost. As a result, an in-depth analysis from an experienced construction consultant is required to estimate construction costs accurately. Machine learning (ML) technology can learn from previous data, which is equivalent to human experience. Many project-specific ML models estimate the construction cost, which misses the generalizability. This paper addresses the gap and designs, develops, implements, and analyzes a deep learning (DL) based novel framework that maps 94.67% of the independent variables with a mean average percentage error (MAPE) of 11.60%. The proposed framework is not limited to any specific project. It estimates the construction cost of similar projects, further validated by an innovative estimator validation unit.

KeywordsDeep learning; Construction cost estimation; Framework; Validator
Year2023
JournalBuilding Research & Information
Journal citation51 (3), pp. 241-368
PublisherTaylor & Francis
ISSN0961-3218
1466-4321
Digital Object Identifier (DOI)https://doi.org/10.1080/09613218.2023.2196388
Official URLhttps://www.tandfonline.com/doi/full/10.1080/09613218.2023.2196388
Publication dates
Print11 Apr 2023
Publication process dates
Accepted09 Mar 2023
Deposited12 Apr 2023
Publisher's version
License
File Access Level
Open
Output statusPublished
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