Machine Learning-Aided Prediction of Pile Behaviour: The Role of Data Quality

Nguyen, TT; Huynh, TQ; Khabbaz, H; Le Nguyen, K

Machine Learning-Aided Prediction of Pile Behaviour: The Role of Data Quality

Nguyen, TT Huynh, TQ Khabbaz, H Le Nguyen, K

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Proceedings of the 5th International Conference on Geotechnics for Sustainable Infrastructure Development, 2024, 395, pp. 515-526
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Nguyen, TT
dc.contributor.author	Huynh, TQ
dc.contributor.author	Khabbaz, H
dc.contributor.author	Le Nguyen, K
dc.date.accessioned	2024-09-09T03:44:48Z
dc.date.available	2024-09-09T03:44:48Z
dc.date.issued	2024-01-01
dc.identifier.citation	Proceedings of the 5th International Conference on Geotechnics for Sustainable Infrastructure Development, 2024, 395, pp. 515-526
dc.identifier.isbn	9789819997213
dc.identifier.uri	http://hdl.handle.net/10453/180753
dc.description.abstract	Machine learning (ML), a data-based approach, has recently emerged as an effective method to predict the behaviour of pile foundation. However, the assessment of data quality is usually carried out with a lack of thorough consideration and/or robust methodology. This paper emphasizes key aspects to evaluate data quality with reference to the most common ML algorithms and practice of pile foundation. An investigation into the data randomness and uniformity during selection of data sets for training and testing ML model based on the Artificial Neural Network (ANN) is carried out to demonstrate the possible influence of inadequate data quality on the predicted outcome. The results imply the utmost importance of having high-quality data for building cost-effective and reliable ML model.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Proceedings of the 5th International Conference on Geotechnics for Sustainable Infrastructure Development
dc.relation.ispartofseries	Lecture Notes in Civil Engineering
dc.relation.isbasedon	10.1007/978-981-99-9722-0_35
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Machine Learning-Aided Prediction of Pile Behaviour: The Role of Data Quality
dc.type	Chapter
utslib.citation.volume	395
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	University of Technology Sydney/Strength - TRC - Transport Research Centre
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Technology in Water and Wastewater Treatment (CTWW)
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Built Infrastructure (CBI)
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Technology in Water and Wastewater Treatment (CTWW)/Associate Member
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-09T03:44:47Z
pubs.publication-status	Published
pubs.volume	395

Abstract:

Machine learning (ML), a data-based approach, has recently emerged as an effective method to predict the behaviour of pile foundation. However, the assessment of data quality is usually carried out with a lack of thorough consideration and/or robust methodology. This paper emphasizes key aspects to evaluate data quality with reference to the most common ML algorithms and practice of pile foundation. An investigation into the data randomness and uniformity during selection of data sets for training and testing ML model based on the Artificial Neural Network (ANN) is carried out to demonstrate the possible influence of inadequate data quality on the predicted outcome. The results imply the utmost importance of having high-quality data for building cost-effective and reliable ML model.

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http://hdl.handle.net/10453/180753