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

Abdallah, Marwa Hameed; Thoeny, Zainab Abdulrdha; Henedy, Sadiq N.; Al-Abdaly, Nadia Moneem; Imran, Hamza; Bernardo, Luís Filipe Almeida; Al-Khafaji, Zainab

doi:10.3390/app13148325

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Analyzing the impact of input parameters on compressive strength is a crucial guiding factor in designing RCFST columns. In this study, the Shapley Additive Explanation (SHAP) method is utilized to assess the impact of input parameters on the strength index 33 , 36 . As depicted in Fig.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Application of machine learning models in the capacity prediction of RCFST columns

Megahed,

Mahmoud,

Abd-Rabou

2023

Sci Rep

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Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength.

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Section: Feature Importance Analysismentioning

confidence: 99%

Application of machine learning models in the capacity prediction of RCFST columns

Megahed,

Mahmoud,

Abd-Rabou

2023

Sci Rep

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Enhanced nonlinear models for critical compressive stress-strain characteristics of rubberized concrete: Comprehensive experimental data and robust evaluation methodology

Abbas,

Alsaif

2024

Construction and Building Materials

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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Wahab,

Mahmoudabadi,

Waqas

et al. 2024

Advances in Civil Engineering

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This research focuses on a comprehensive comparative analysis of shear strength prediction in slab–column connections, integrating machine learning, design codes, and finite element analysis (FEA). The existing empirical models lack the influencing parameters that decrease their prediction accuracy. In this paper, current design codes of American Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innovative approaches like the compressive force path method and machine learning models are employed to predict the punching shear strength using a comprehensive database of 610 samples. The database consists of seven key parameters including slab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength of longitudinal steel (fy), longitudinal reinforcement ratio (ρl), ultimate load-carrying capacity (Vu), and concrete compressive strength (fc). Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. PSOFNN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% and 1.214%, respectively. The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. The research also provides insight into the application of metaheuristic models along with ANN, revealing that not all metaheuristic models can outperform ANN as usually perceived. The study also highlights superior predictive capabilities of EC2 over ACI 318-19 for punching shear values.

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

Cited by 3 publications

References 20 publications

Application of machine learning models in the capacity prediction of RCFST columns

Application of machine learning models in the capacity prediction of RCFST columns

Enhanced nonlinear models for critical compressive stress-strain characteristics of rubberized concrete: Comprehensive experimental data and robust evaluation methodology

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

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