Symbolic machine learning improved MCFT model for punching shear resistance of FRP-reinforced concrete slabs

Liang, Shixue; Shen, Yuanxie; Gao, Xuejuan; Cai, Yiqing; Fei, Zhengyu

doi:10.1016/j.jobe.2023.106257

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…This innovative approach provided an accurate, unbiased model for shear strength in RC beams. Simultaneously, the Symbolic Regression-Modified Compression Field Theory (SR-MCFT), a hybrid grey-box model, took a step further [19]. By integrating the modified compression field theory and machine learning, it excelled in predicting punching shear resistance in Fiber-Reinforced-Polymer (FRP)-reinforced slabs.…”

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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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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“…One of the most dangerous failure modes in this type of slab is punching shear, which is a brittle two-way shear failure that initiates at the slab-column interface. Within this scope, various ML algorithms were developed and implemented to establish mathematical equations to predict the punching shear strength of RC slabs considering various parameters with relatively high accuracy [81][82][83][84][85][86]. This research revealed that 6.5% of the reviewed articles focused on the application of ML techniques for predicting punching shear strength.…”

Section: Reinforced Concrete Slabmentioning

confidence: 99%

“…It was found that the LS-SVM technique yielded highly accurate results, outperforming ANN with R 2 values of 0.9 and 0.97 for the training and testing processes, respectively. Recently, modified compression field theory (MCFT) was used to derive a hybrid ML model that involves XGBoost and SHAP methods to investigate further the punching shear capacity of FRP-RC slabs [86]. The resulting symbolic regression MCFT (SR-MCFT) model was superior in predicting the punching shear capacity of FRP-RC slabs as compared with existing empirical models.…”

Section: Reinforced Concrete Slabmentioning

confidence: 99%

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Alhusban,

Alkhawaldeh

2023

Sustainability

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Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.

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“…Shahnewaz et al 9 and Wakjira 10 used a genetic algorithm to predict the shear strength of RCDBs. Liang et al 22 devoloped a symbolic regression (SR) model based on the Modified Compression Field Theory to analyze the punching shear resistance of fiber-reinforced polymer (FRP) reinforced concrete slabs.…”

Section: Introductionmentioning

confidence: 99%

Prediction and reliability analysis of shear strength of RC deep beams

Megahed

2024

Sci Rep

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This study explores machine learning (ML) capabilities for predicting the shear strength of reinforced concrete deep beams (RCDBs). For this purpose, eight typical machine-learning models, i.e., symbolic regression (SR), XGBoost (XGB), CatBoost (CATB), random forest (RF), LightGBM, support vector regression (SVR), artificial neural networks (ANN), and Gaussian process regression (GPR) models, are selected and compared based on a database of 840 samples with 14 input features. The hyperparameter tuning of the introduced ML models is performed using the Bayesian optimization (BO) technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R2 = 0.997 and 0.947 in the training and testing sets, respectively). In addition, simple and practical design expressions for RCDBs have been proposed based on the SR model with a physical meaning and acceptable accuracy (an average prediction-to-test ratio of 0.935 and a standard deviation of 0.198). Meanwhile, the shear strength predicted by ML models was then compared with classical mechanics-driven shear models, including two prominent practice codes (i.e., ACI318, EC2) and two previous mechanical models, which indicated that the ML approach is highly reliable and accurate over conventional methods. In addition, a reliability-based design was conducted on two ML models, and their reliability results were compared with those of two code standards. The findings revealed that the ML models demonstrate higher reliability compared to code standards.

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Symbolic machine learning improved MCFT model for punching shear resistance of FRP-reinforced concrete slabs

Cited by 13 publications

References 37 publications

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

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

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Prediction and reliability analysis of shear strength of RC deep beams

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