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

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Abstract

The study is investigated the capacity of new artificial intelligence (AI) methodologies for shear strength (Vs) computation of reinforced concrete (RC) beams. The development of extreme gradient boosting (XGBoost) and multivariate adaptive regression splines (MARS) models as a robust AI methodology are tested for Vs prediction. The proposed models are developed based on collected experimental data from the literature including the beam geometric and concrete properties parameters. There are nine input combinations adopted based on the associated input parameters for the predictive models construction. Support vector machine (SVM) model is conducted for validation purpose. In addition, several empirical formulations are recalled from the literature for comparison. Research findings evidenced the potential of the proposed XGBoost and MARS models for modeling the Vs reinforced concrete beams. The modeling accuracy performance comparison with the established AI models and the empirical formulas confirmed the capacity of the proposed models. Results indicated that all the utilized beam geometric and concrete properties parameters are significant for the predictive model development. In quantitative terms, MARS model attained minimum root mean square error (RMSE = 89.96 KN). In general, the research provided a reliable and robust soft computing model for Vs reinforced concrete beams computation that contribute to the basic knowledge of structural engineering design and sustainability.

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Abbreviations

AI:

Artificial intelligence

V s :

Shear strength

RC:

Reinforced concrete

XGBoost:

Extreme gradient boosting

MARS:

Multivariate adaptive regression splines

SVM:

Support vector machine

ANN:

Artificial neural network

USS:

Ultimate shear strength

FRP:

Fiber-reinforced polymer

NSM:

Near surface mounted

PSO:

Particle swarm optimization

ANFIS:

Adaptive neuro-fuzzy inference system

RF:

Random forest

SFA:

Smart firefly algorithm

LSSVR:

Least squares support vector regression

GB:

Gradient boosting

GCV:

Generalised cross-validation

SRM:

Structural risk minimisation

R 2 :

Determination coefficient

RMSE:

Root mean square error

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

Nash:

Nash–Sutcliffe efficiency

md:

Modified index of agreement

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  1. Department of Architecture, Faculty of Design and Architecture, Universiti Putra Malaysia, Serdang, 43400, Seri Kembangan, Malaysia

    Hayder Riyadh Mohammed Mohammed & Sumarni Ismail

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  1. Hayder Riyadh Mohammed Mohammed
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Mohammed, H.R.M., Ismail, S. Proposition of new computer artificial intelligence models for shear strength prediction of reinforced concrete beams. Engineering with Computers 38, 3739–3757 (2022). https://doi.org/10.1007/s00366-021-01400-z

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