Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Uddin, Md Nasir; Yu, Kequan; Li, Lingzhi; Ye, Junhong; Tafsirojjaman, T.; Alhaddad, Wael

doi:10.1007/s41062-022-00826-8

Cited by 8 publications

(2 citation statements)

References 74 publications

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“…To date, ML approaches have been used for strength predictions of concrete structures, capitalizing on the extensive amount of experimental data and low accuracy of traditional mechanical prediction models, especially for shear problems [26][27][28][29][30][31][32][33][34]. The current investigations have demonstrated that the data-driven ML approaches could provide strength predictions with significantly high accuracy compared with the design provisions.…”

Section: Introductionmentioning

confidence: 91%

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Yang

Liu

2023

Buildings

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Due to the intrinsic complexity, there has been no widely accepted mechanics-based estimation model of the shear performance of Fiber-Reinforced Polymer (FRP)-reinforced concrete beams. Capitalizing on a large amount of previous experimental data, data-driven machine learning (ML) models could be potentially suitable for addressing this problem. In this paper, four existing shear design provisions are reviewed and four typical ML models are analyzed. The accuracy of codified methods and ML models are compared and analyzed based on our established extensive database of FRP-reinforced concrete beams with rectangular cross sections. A series of artificially selected features considering the shear-carrying mechanisms of FRP-reinforced beams are incorporated into the proposed ML models to show their influence on the model validity. Bayesian optimization is utilized to automatically tune the hyperparameters of different ML models. Compared to the most satisfying codified predictions from CSA S806, the best ML model, XGBoost, can provide more accurate and consistent predictions for the database, with R2 enhanced by 15% and the MAE and RMSE reduced by 59% and 52%, respectively. With the selected features based on domain knowledge, the performance of ML models is further enhanced, shown by the most important features being the added ones. With outstanding performance on a large database and singular test, the ML approaches have great potential in guiding the shear design of FRP-reinforced concrete.

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Section: Introductionmentioning

confidence: 91%

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Yang

Liu

2023

Buildings

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“…According to the results of the study findings, the XGBoost model performs well compared to other ML techniques and existing guidelines. Uddin et al 27 used ANN, RF, GEP, and GBDT to predict the shear strength of RC beams. The performance of GBDT algorithm was good compared to ANN, RF and GEP algorithms.…”

Section: Introductionmentioning

confidence: 99%

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

Kumar

Arora

Kapoor

et al. 2023

Sci Rep

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The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

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Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning

Uddin,

Shanmugasundaram,

Praveenkumar

et al. 2024

Int J Mech Mater Des

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Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Abstract: Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Cited by 8 publications

References 74 publications

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning

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