Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs
This paper investigates the effectiveness of machine learning (ML) models available in MATLAB Regression Learner app and MATLAB App Designer, both low-code applications, for accurately predicting punching shear strength (PSS) in reinforced concrete (RC) slabs. A database of 379 RC slab samples witho...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/8853122 |
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| _version_ | 1850181398709665792 |
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| author | Khuong Le Nguyen Thanh Tu Do Giang Huu Nguyen Afaq Ahmad |
| author_facet | Khuong Le Nguyen Thanh Tu Do Giang Huu Nguyen Afaq Ahmad |
| author_sort | Khuong Le Nguyen |
| collection | DOAJ |
| description | This paper investigates the effectiveness of machine learning (ML) models available in MATLAB Regression Learner app and MATLAB App Designer, both low-code applications, for accurately predicting punching shear strength (PSS) in reinforced concrete (RC) slabs. A database of 379 RC slab samples without transverse reinforcement was compiled from renowned publications. RandomSearch and Bayesian optimisation were employed for tuning hyperparameters. The performance of these models was compared with six empirical models, which included three current design codes, three equations from other researchers, and 227 finite-element simulations conducted by the authors. The ML models and finite-element method (FEM) demonstrated superior performance compared with the literature and practical codes. Furthermore, the results emphasised the exceptional performance of the Gaussian process regression (GPR) with optimised hyperparameters, exhibiting the best performance in validation, training, and testing datasets with R2 values of 0.95, 0.99, and 0.98, respectively. A user-friendly standalone application was developed, providing real-time predictions of the PSS using the two best-developed ML models, GPR and support vector machine (SVM), as well as six empirical models from the literature. This tool offers users flexibility in choosing the most appropriate model for their specific needs, delivering reliable, and accurate results for estimating the PSS of RC slabs. |
| format | Article |
| id | doaj-art-4bd2e6afadb24d8288ce293331926e69 |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4bd2e6afadb24d8288ce293331926e692025-08-20T02:17:54ZengWileyAdvances in Civil Engineering1687-80942023-01-01202310.1155/2023/8853122Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced SlabsKhuong Le Nguyen0Thanh Tu Do1Giang Huu Nguyen2Afaq Ahmad3Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringThis paper investigates the effectiveness of machine learning (ML) models available in MATLAB Regression Learner app and MATLAB App Designer, both low-code applications, for accurately predicting punching shear strength (PSS) in reinforced concrete (RC) slabs. A database of 379 RC slab samples without transverse reinforcement was compiled from renowned publications. RandomSearch and Bayesian optimisation were employed for tuning hyperparameters. The performance of these models was compared with six empirical models, which included three current design codes, three equations from other researchers, and 227 finite-element simulations conducted by the authors. The ML models and finite-element method (FEM) demonstrated superior performance compared with the literature and practical codes. Furthermore, the results emphasised the exceptional performance of the Gaussian process regression (GPR) with optimised hyperparameters, exhibiting the best performance in validation, training, and testing datasets with R2 values of 0.95, 0.99, and 0.98, respectively. A user-friendly standalone application was developed, providing real-time predictions of the PSS using the two best-developed ML models, GPR and support vector machine (SVM), as well as six empirical models from the literature. This tool offers users flexibility in choosing the most appropriate model for their specific needs, delivering reliable, and accurate results for estimating the PSS of RC slabs.http://dx.doi.org/10.1155/2023/8853122 |
| spellingShingle | Khuong Le Nguyen Thanh Tu Do Giang Huu Nguyen Afaq Ahmad Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs Advances in Civil Engineering |
| title | Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs |
| title_full | Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs |
| title_fullStr | Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs |
| title_full_unstemmed | Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs |
| title_short | Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs |
| title_sort | low code application and practical implications of common machine learning models for predicting punching shear strength of concrete reinforced slabs |
| url | http://dx.doi.org/10.1155/2023/8853122 |
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