Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations

Wu, Yanqi; Zhou, Yisong

doi:10.1080/15376494.2022.2068209

Cited by 32 publications

(9 citation statements)

References 27 publications

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“…Equations () indicate how to calculate the statistical criteria 30 :

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {(y_{i} - {\overset{y}{true}}_{i})}^{2}}{n}}

MAPE = \frac{1}{n} \sum_{i = 1}^{n} |\frac{y_{i} - {\overset{truê}{y}}_{i}}{y_{i}}|

MAE = \frac{\sum_{i = 1}^{n} |y_{i} - {\overset{y}{true}}_{i}|}{n}

R^{2} = 1 - \frac{\sum_{i = 1}^{n} {((), y_{i} goodbreak- {\overset{truê}{y}}_{i})}^{2}}{\sum_{i = 1}^{n} {((), y_{i} goodbreak- \overset{y}{true})}^{2}}

SI = \frac{RMSE}{\overset{Y}{true}}

where n denotes the total number of data. Accordingly,

y_{i}

and

{\overset{y}{true}}_{i}

show the actual and predicted load values at the corresponding time of

y_{i}

, respectively, and the bar denotes the mean of variables.…”

Section: Materials and Methodologies Explanationmentioning

confidence: 99%

“…Furthermore, the input parameters of the prediction dataset should be in the range of the input variables of the trained data, which is a drawback of AI methods. [28][29][30] These are favorable approaches but rely heavily on initial variables, a serious limitation that impacts performance. 31 The contribution of the presented study is placed in the framework of utilizing the Adaptive Boosting (ADA) methodology to deliver a flexible and trustable model to handle the inherited nonlinearity of the HPC mixture for the case of TS and CS prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The AI methods ignore physical interactions between input and output components. Furthermore, the input parameters of the prediction dataset should be in the range of the input variables of the trained data, which is a drawback of AI methods 28–30 . These are favorable approaches but rely heavily on initial variables, a serious limitation that impacts performance 31…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

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The evaluation of fly ash (FA) and micro‐silica (MS) effects on the mechanical properties of concrete at various ages prompts the search for efficient factors in forecasting the compressive strength (CS) and tensile strength (TS), which are extendable for future investigation as well as practical use. The main aim of this article is to overcome the complexity caused by nonlinearity, which is rooted in the relationship between input variables and outputs. Also, the nonlinearity is getting worse through the existence of admixtures. The Adaptive Boosting (ADA) approach was utilized in the related study as a hybrid model to provide an optimal relation between the ingredients and the mechanical strengths for evaluating the parameters most useful in predicting the CS and TS of high‐performance concrete. Furthermore, model boosting is applied using the starling murmuration optimization (SMO), termite queen algorithm (TQO), and gannet optimization algorithm (GOA) algorithms to reach the highest convergence. As a result of assessing the related models, the ADA‐SMO obtained a coefficient correlation of 0.9981 and 0.9825 for the predictions of CS and TS, respectively. Considering the adaptive framework provided to have the best compatibility with the complex physics of the problem, the ADA‐SMO earned the highest level of accuracy in estimating CS and TS among other hybrid models and was introduced as a trustable model in the prediction of strength properties.

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“…Equations () indicate how to calculate the statistical criteria 30 :

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {(y_{i} - {\overset{y}{true}}_{i})}^{2}}{n}}

MAPE = \frac{1}{n} \sum_{i = 1}^{n} |\frac{y_{i} - {\overset{truê}{y}}_{i}}{y_{i}}|

MAE = \frac{\sum_{i = 1}^{n} |y_{i} - {\overset{y}{true}}_{i}|}{n}

R^{2} = 1 - \frac{\sum_{i = 1}^{n} {((), y_{i} goodbreak- {\overset{truê}{y}}_{i})}^{2}}{\sum_{i = 1}^{n} {((), y_{i} goodbreak- \overset{y}{true})}^{2}}

SI = \frac{RMSE}{\overset{Y}{true}}

where n denotes the total number of data. Accordingly,

y_{i}

and

{\overset{y}{true}}_{i}

show the actual and predicted load values at the corresponding time of

y_{i}

, respectively, and the bar denotes the mean of variables.…”

Section: Materials and Methodologies Explanationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

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show abstract

“…The Evolutionary Polynomial Regression (EPR) added another dimension to this field, successfully enhancing the prediction of shear strength in reinforced concrete circular columns and refining existing models and empirical rules [20]. Lastly, a unique hybrid model that combined Particle Swarm Optimization and Support Vector Regression (PSO-SVR) showcased its predictive prowess, notably improving the prediction of punching shear strength in two-way reinforced slabs and emphasizing the importance of slab depth and thickness [21].…”

Section: Literature Reviewmentioning

confidence: 99%

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

Abdallah,

Thoeny,

Henedy

et al. 2023

Applied Sciences

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Reinforced concrete (RC) flat slabs are widely employed in modern construction, and accurately predicting their load-carrying capacity is crucial for ensuring safety and reliability. Existing design methods and empirical equations still exhibit discrepancies in determining the ultimate load capacity of flat slabs. This study aims to develop a robust machine learning model, specifically the M5P model tree, for predicting the punching shear capacity of a RC flat slab without shear reinforcement. A comprehensive dataset of 482 experimentally tested flat slabs without shear reinforcement was gathered through an extensive literature review and utilized for the development of the M5P model. The model takes into account influential parameters, such as slab thickness, longitudinal reinforcement ratios, and concrete strength. The performance of the proposed M5P model was compared with existing design codes and other empirical models. The comparison highlights that the developed M5P model tree provides a more accurate and reliable prediction of the punching shear capacity of RC flat slabs. This study contributes to the advancement of structural engineering knowledge and has the potential to improve the design and safety assessment of concrete flat slab structures.

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“…Additionally, the following four metrics were used to evaluate the model's performance: correlation coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Their definitions are depicted below 40 , 41 . where T and Y are the experimental and predicted results, respectively, while and are the mean values.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Predicting compressive strength of RCFST columns under different loading scenarios using machine learning optimization

Wu,

Tang,

et al. 2023

Sci Rep

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Accurate bearing capacity assessment under load conditions is essential for the design of concrete-filled steel tube (CFST) columns. This paper presents an optimization-based machine learning method to estimate the ultimate compressive strength of rectangular concrete-filled steel tube (RCFST) columns. A hybrid model, GS-SVR, was developed based on support vector machine regression (SVR) optimized by the grid search (GS) algorithm. The model was built based on a sample of 1003 axially loaded and 401 eccentrically loaded test data sets. The predictive performance of the proposed model is compared with two commonly used machine learning models and two design codes. The results obtained for the axial loading dataset with R2 of 0.983, MAE of 177.062, RMSE of 240.963, and MAPE of 12.209%, and for the eccentric loading dataset with R2 of 0.984, MAE of 93.234, RMSE of 124.924, and MAPE of 10.032% show that GS-SVR is the best model for predicting the compressive strength of RCFST columns under axial and eccentric loadings. It is an effective alternative method that can be used to assist and guide the design of RCFST columns to save time and cost of some laboratory experiments. Additionally, the impact of input parameters on the output was investigated.

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Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations

Cited by 32 publications

References 27 publications

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Optimal boosting method of HPC concrete compressive and tensile strength prediction

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

Predicting compressive strength of RCFST columns under different loading scenarios using machine learning optimization

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