Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach
Kumar, Aman ; Arora, Harish Chandra ; Kumar, Krishna LU
; Mohammed, Mazin Abed
; Majumdar, Arnab
; Khamaksorn, Achara
and Thinnukool, Orawit
(2022)
In Sustainability (Switzerland)
14(2).
- Abstract
Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restora-tion of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM... (More)
Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restora-tion of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Kumar, Krishna
LU
; Mohammed, Mazin Abed
; Majumdar, Arnab
; Khamaksorn, Achara
and Thinnukool, Orawit
- publishing date
- 2022-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- ANN, Bond strength prediction, FRCM, FRCM–concrete interface, GPR, SVM
- in
- Sustainability (Switzerland)
- volume
- 14
- issue
- 2
- article number
- 845
- publisher
- MDPI AG
- external identifiers
-
- scopus:85122743584
- ISSN
- 2071-1050
- DOI
- 10.3390/su14020845
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- id
- b6c1f883-84db-4d91-a4fd-6212b272fd23
- date added to LUP
- 2024-04-15 12:56:18
- date last changed
- 2024-05-22 09:04:18
@article{b6c1f883-84db-4d91-a4fd-6212b272fd23,
abstract = {{<p>Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restora-tion of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Kumar, Krishna and Mohammed, Mazin Abed and Majumdar, Arnab and Khamaksorn, Achara and Thinnukool, Orawit}},
issn = {{2071-1050}},
keywords = {{ANN; Bond strength prediction; FRCM; FRCM–concrete interface; GPR; SVM}},
language = {{eng}},
month = {{01}},
number = {{2}},
publisher = {{MDPI AG}},
series = {{Sustainability (Switzerland)}},
title = {{Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach}},
url = {{http://dx.doi.org/10.3390/su14020845}},
doi = {{10.3390/su14020845}},
volume = {{14}},
year = {{2022}},
}