Proposition of new computer artificial intelligence models for shear strength prediction of reinforced concrete beams

Mohammed, Hayder Riyadh Mohammed; Ismail, Sumarni

doi:10.1007/s00366-021-01400-z

Cited by 22 publications

(6 citation statements)

References 63 publications

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“…Based on the results a simple Python-based template was also developed by the researcher for easily calculating the shear buckling load and ultimate shear capacity of the channels. [64] reported on a new reliable soft computing model that can accurately predict the shear strength of RC beams. They considered the XGBoost and multivariable adaptive regression spline (MARS) algorithms to achieve this objective.…”

Section: Sanad and Sakamentioning

confidence: 99%

Shear strength prediction of reinforced concrete beams using machine learning

et al. 2023

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Section: Sanad and Sakamentioning

confidence: 99%

Shear strength prediction of reinforced concrete beams using machine learning

et al. 2023

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“…The outcomes of statistical measurement showed revealed the reliability and efficiency of ANN model in V s prediction. Other studies presented tree base models of V s prediction like random forest 50 , 51 , XGBoost 52 , 53 and M5 model 54 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2023

Sci Rep

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Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (Vs) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting Vs of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for Vs prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R2) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on Vs of FRP reinforced concrete beam with the potential of applying different computer aid models.

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“…The R 2 , RMSE, and MAE values of the models were 0.93, 16,634 kN and 0.98 kN, respectively, and they reported the best performance; however, they required the most training time. Mohammed and Ismail 31 used MARS, XGBoost and SVM models to predict the shear strength of RC beams. According to the research results, the developed MARS and XGBoost models for simulating the shear strength of RC beams have potential.…”

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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Proposition of new computer artificial intelligence models for shear strength prediction of reinforced concrete beams

Cited by 22 publications

References 63 publications

Shear strength prediction of reinforced concrete beams using machine learning

Shear strength prediction of reinforced concrete beams using machine learning

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

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

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