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

Autor: Tan Huy Tran, Mohammed Rashid, Sadaam Hadee Hussein, Abdalrhman Milad, Haitham Al-Msari, Ahlam R. Khekan
Rok vydání: 2021
Předmět:
Zdroj: Engineering with Computers. 38:3625-3637
ISSN: 1435-5663
0177-0667
DOI: 10.1007/s00366-021-01398-4
Popis: 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.
Databáze: OpenAIRE
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