Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU149782" target="_blank" >RIV/00216305:26110/23:PU149782 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/13/23/12744" target="_blank" >https://www.mdpi.com/2076-3417/13/23/12744</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app132312744" target="_blank" >10.3390/app132312744</a>

Result language

angličtina

Original language name

Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

Original language description

his paper presents research results on the development of an original cost prediction model for construction costs in sewerage projects. The focus is placed on fast cost estimates applicable in the early stages of a project, based on fundamental information available during the initial design phase of sanitary sewers prior to the detailed design. The originality and novelty of this research lie in the application of artificial neural network ensembles, which include a combination of several individual neural networks and the use of simple averaging and generalized averaging approaches. The research resulted in the development of two ensemble-based models, including five neural networks that were trained and tested using data collected from 125 sewerage projects completed in the Czech Republic between 2018 and 2022. The data included information relevant to various aspects of projects and contract costs, updated to account for changes in costs over time. The developed models present satisfactory predictive performance, especially the ensemble model based on simple averaging, which offers prediction accuracy within the range of ±30% (in terms of percentage errors) for over 90% of the training and testing samples. The developed models, based on the ensembles of neural networks, outperformed the benchmark model based on the classical approach and the use of multiple linear regression.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20101 - Civil engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences - Basel

ISSN

2076-3417

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

23

Country of publishing house

CH - SWITZERLAND

Number of pages

24

Pages from-to

1-24

UT code for WoS article

001116777600001

EID of the result in the Scopus database

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