Dr Nirodha Fernando N.Fernando@napier.ac.uk
Associate Professor
An artificial neural network (ANN) approach for early cost estimation of concrete bridge systems in developing countries: the case of Sri Lanka
Fernando, Nirodha; T.A., Kasun Dilshan; Zhang, Hexin (Johnson)
Authors
Kasun Dilshan T.A.
Prof Johnson Zhang j.zhang@napier.ac.uk
Professor
Abstract
Purpose
The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.
Design/methodology/approach
The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.
Findings
An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.
Originality/value
The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.
Citation
Fernando, N., T.A., K. D., & Zhang, H. (. (2024). An artificial neural network (ANN) approach for early cost estimation of concrete bridge systems in developing countries: the case of Sri Lanka. Journal of Financial Management of Property and Construction, 29(1), 23-51. https://doi.org/10.1108/JFMPC-09-2022-0048
Journal Article Type | Article |
---|---|
Acceptance Date | May 31, 2023 |
Online Publication Date | Jun 27, 2023 |
Publication Date | Feb 7, 2024 |
Deposit Date | Jun 27, 2023 |
Publicly Available Date | Aug 24, 2023 |
Print ISSN | 1366-4387 |
Publisher | Emerald |
Peer Reviewed | Peer Reviewed |
Volume | 29 |
Issue | 1 |
Pages | 23-51 |
Series ISSN | 1366-4387 |
DOI | https://doi.org/10.1108/JFMPC-09-2022-0048 |
Keywords | Artificial neural network, Concrete bridge project, Early-stage cost estimating, Cost estimating models |
Public URL | http://researchrepository.napier.ac.uk/Output/2922250 |
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An artificial neural network (ANN) approach for early cost estimation of concrete bridge systems in developing countries: the case of Sri Lanka (accepted version)
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