Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams
Kumar, Aman ; Arora, Harish Chandra ; Kapoor, Nishant Raj ; Kumar, Krishna LU
; Hadzima-Nyarko, Marijana
and Radu, Dorin
(2023)
In Scientific Reports
13(1).
- Abstract
The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement... (More)
The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Kapoor, Nishant Raj
; Kumar, Krishna
LU
; Hadzima-Nyarko, Marijana
and Radu, Dorin
- publishing date
- 2023-12
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Scientific Reports
- volume
- 13
- issue
- 1
- article number
- 2857
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85148354578
- pmid:36807317
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-023-30037-9
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2023, The Author(s).
- id
- fee6cfd0-7705-4a80-b0bd-bb94ace78f16
- date added to LUP
- 2024-04-15 13:22:27
- date last changed
- 2024-06-25 22:35:29
@article{fee6cfd0-7705-4a80-b0bd-bb94ace78f16,
abstract = {{<p>The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Kapoor, Nishant Raj and Kumar, Krishna and Hadzima-Nyarko, Marijana and Radu, Dorin}},
issn = {{2045-2322}},
language = {{eng}},
number = {{1}},
publisher = {{Nature Publishing Group}},
series = {{Scientific Reports}},
title = {{Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams}},
url = {{http://dx.doi.org/10.1038/s41598-023-30037-9}},
doi = {{10.1038/s41598-023-30037-9}},
volume = {{13}},
year = {{2023}},
}