Accessibility navigation


The impact of artificial intelligence on construction costing practice

Ayinla, K., Saka, A., Seidu, R. and Madanayake, U. ORCID: https://orcid.org/0000-0002-9122-1882 (2023) The impact of artificial intelligence on construction costing practice. In: 39th Annual ARCOM Conference, 04 - 06 Sep 2023, University of Leeds, Leeds, UK, pp. 65-74. (ISBN: 9780995546370)

[thumbnail of AI on construction costing ARCOM Conference Paper.pdf]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.

419kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Official URL: https://www.arcom.ac.uk/conf-archive-indexed.php

Abstract/Summary

Cost estimation is a crucial process in the construction sector as the efficiency of the overall project cost serves as one metric in determining project success. Prevailing traditional approach suffers from human subjectivity and bias which affect accuracy. With the development and adoption of Artificial Intelligence (AI) such as the use of machine learning (ML) and deep learning (DL) algorithms, the construction industry is experiencing brisk technological change and new ways of working, particularly in terms of cost predictions and estimations. However, the application of AI is still in its infancy and the industry still prioritises traditional cost modelling approaches in determining early estimates. This research explores the application of the various ML methods for costing and assesses their usage and application in the costing practice via an exploratory critical review. Findings indicate that ML algorithms would improve the accuracy and efficiency of costing practice but cannot replace the professionals and data availability.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of the Built Environment > Construction Management and Engineering
Science > School of the Built Environment > Organisation, People and Technology group
ID Code:116266

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation