Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system

doi:10.1007/s11709-024-1091-1

2024, Vol. 18

Issue (4) : 551-567 https://doi.org/10.1007/s11709-024-1091-1

Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system

Dinh-Nhat TRUONG¹, Van-Lan TO¹, Gia Toai TRUONG²(

), Hyoun-Seung JANG³(

)

¹. Department of Civil Engineering, University of Architecture Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
². Faculty of Civil Engineering and Technology, Dong A University, Da Nang City 550000, Vietnam
³. School of Architecture, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea

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Abstract

Reinforced concrete (RC) flat slabs, a popular choice in construction due to their flexibility, are susceptible to sudden and brittle punching shear failure. Existing design methods often exhibit significant bias and variability. Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management. This study introduces a novel computation method, the jellyfish-least square support vector machine (JS-LSSVR) hybrid model, to predict punching shear strength. By combining machine learning (LSSVR) with jellyfish swarm (JS) intelligence, this hybrid model ensures precise and reliable predictions. The model’s development utilizes a real-world experimental data set. Comparison with seven established optimizers, including artificial bee colony (ABC), differential evolution (DE), genetic algorithm (GA), and others, as well as existing machine learning (ML)-based models and design codes, validates the superiority of the JS-LSSVR hybrid model. This innovative approach significantly enhances prediction accuracy, providing valuable support for civil engineers in estimating RC flat slab punching shear strength.

Keywords punching shear strength reinforced concrete flat slabs machine learning jellyfish search support vector machine

Corresponding Author(s): Gia Toai TRUONG,Hyoun-Seung JANG

Just Accepted Date: 08 May 2024 Online First Date: 29 May 2024 Issue Date: 13 June 2024

Cite this article:

Dinh-Nhat TRUONG,Van-Lan TO,Gia Toai TRUONG, et al. Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system[J]. Front. Struct. Civ. Eng., 2024, 18(4): 551-567.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-024-1091-1
https://academic.hep.com.cn/fsce/EN/Y2024/V18/I4/551

Fig.1 Flowchart of the JS-LSSVR hybrid model.

Tab.1 Statistical characteristics of punching shear strength data set

Fig.2 Punching shear failure mode in flat slabs.

Fig.3 Feature correlation matrix for punching shear strength data set.

Fig.4 Box plot of the original data set (unit: MN).

Fold No.	Regularization parameter, $C$	Kernel parameter, $γ$
1	2.0230 × 10¹³	116811.2957
2	3.4279 × 10¹³	59433275.6036
3	1.9604 × 10¹³	190384.6682
4	2.6947 × 10¹³	215769.0747
5	703.6472	6.3849
6	5.1162 × 10¹³	84469.8523
7	1.1625 × 10¹⁴	39003.8533
8	3.3484 × 10¹³	128558322.1544
9	3.5765 × 10¹³	52169046.4106
10	1.7363 × 10¹³	39534306.2050

Tab.2 Optimal values for least squares SVR hyperparameters

Tab.3 Optimal values for least squares SVR hyperparameters

Fig.5 Observed and predicted punching shear strength of concrete flat slabs in test data for 10-folds: (a) test data of fold 1; (b) test data of fold 2; (c) test data of fold 3; (d) test data of fold 4; (e) test data of fold 5; (f) test data of fold 6; (g) test data of fold 7; (h) test data of fold 8; (i) test data of fold 9; (j) test data of fold 10.

Tab.4 Performance of JS vs. well-known optimizers in searching hyper paramters for the punching shear strength of RC flat slabs model

Fig.6 Boxplot of RMSE, MAE, and R of LSSVR model using various optimizers: (a) boxplot of RMSE; (b) boxplot of MAE; (c) boxplot of R.

Fig.7 Convergence comparisons on validation data: (a) convert graph of fold 1; (b) convert graph of fold 2; (c) convert graph of fold 3; (d) convert graph of fold 4; (e) convert graph of fold 5; (f) convert graph of fold 6; (g) convert graph of fold 7; (h) convert graph of fold 8; (i) convert graph of fold 9; (j) convert graph of fold 10.

Tab.5 Prediction performance of various models for the punching shear strength of RC flat slabs

Fig.8 Comparison of prediction performance of JS-LSSVR and existing ML-based models: (a) R; (b) RMSE.

Fig.9 Scatter plots comparing current design codes and the JS-LSSVR model for predicting the punching shear strength of RC flat slabs: (a) ACI 318-19; (b) KDS 14 20 22; (c) Eurocode 2; (d) BS 8110; (e) JS-LSSVR.

Fig.10 Variation of current design codes and JS-LSSVR model according to effective depth: (a) ACI 318-19; (b) KDS 14 20 22; (c) Eurocode 2; (d) BS 8110; (e) JS-LSSVR.

Fig.11 Normal distribution of observed-to-predicted punching shear strength of current design codes and JS-LSSVR model.

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