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Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model

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Abstract

Over the past few decades, it has been observed a remarkable progression in the development of computer aid models in the field of civil engineering. Machine learning models provide a reliable and robust alternative modeling design for solving complex engineering issues. The current study introduced three versions of newly explored ensemble machine learning models [extreme gradient boosting (XGBoost), multivariate adaptive regression spline (MARS) and random forest (RF)] for fiber-reinforced polymer (FRP) composite strain prediction. An experimental dataset were collected from the literature with total number of 729 experiments. The dataset is presented the FRP strain and its influential parameters including material geometry, strength properties, strain properties, FRP properties, and confinement properties. Five different input combination were built for the prediction process of the FRP composite strain. The current research results were validated against the well-established literature review empirical formulations and the machine learning models. In general, the modeling results confirmed the capacity of the proposed new ML models in predicting the strain enhancement ratio. The fifth input combination incorporated all the predators attained the best modeling accuracy results. However, the developed MARS model could achieve acceptable and superior prediction results using only strain properties parameters. Overall, the research finding confirmed the significant of the proposed ensemble ML as reliable alternative computer aid model for solving strain enhancement ratio problem.

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Abbreviations

XGBoost:

Extreme gradient boosting

MARS:

Multivariate adaptive regression spline

RF:

Random forest

FRP:

Fiber-reinforced polymer

AI:

Artificial intelligence

ANN:

Artificial neural network

GB:

Gradient boosting

ANFIS:

Adaptive neuro fuzzy inference system

M5Tree:

M5 model tree

RBNN:

Radial basis neural network

OA:

Objective function

GCV:

Cross-validation criterion

CART:

Classification and regression trees

RT:

Regression tree

MSE:

Mean square error

R 2 :

Determination coefficient

RMSE:

Root mean square error

MAE:

Mean absolute error

RD:

Relative index of agreement

NSE:

Nash–Sutcliffe coefficient

KGE:

Kling–Gupta efficiency

IoT:

Internet of Things

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Authors and Affiliations

  1. Department of Civil Engineering, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

    Abdalrhman Milad, Mohammed Rashid & Haitham Al-Msari

  2. Department of Civil Engineering, Al-Maaref University College, Ramadi, 31001, Iraq

    Sadaam Hadee Hussein

  3. College of Biomedical Information, University of Information Technology and Communications, Baghdad, Iraq

    Ahlam R. Khekan

  4. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tan Huy Tran

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  1. Abdalrhman Milad
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  3. Ahlam R. Khekan
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  4. Mohammed Rashid
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Milad, A., Hussein, S.H., Khekan, A.R. et al. Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model. Engineering with Computers 38, 3625–3637 (2022). https://doi.org/10.1007/s00366-021-01398-4

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