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Engineering with Computers

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06.02.2021 | Original Article

Prediction of high-strength concrete: high-order response surface methodology modeling approach

verfasst von: Mohammed Majeed Hameed, Mohamed Khalid AlOmar, Wajdi Jaber Baniya, Mohammed Abdulhakim AlSaadi

Erschienen in: Engineering with Computers | Sonderheft 2/2022

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Abstract

In the concrete industry, compressive strength is the most essential mechanical property. Therefore, insufficient compressive strength may lead to dangerous failure and, thus, becomes very difficult to repair. Consequently, early, and precise prediction of concrete strength is a major issue facing researchers and concrete designers. In this study, high-order response surface methodology (HORSM) is used to develop a prediction model to accurately predict the compressive strength of high-strength concrete (HSC). Different polynomial degrees order ranging from 2 to 5 is used in this model. The HORSM, with five-order polynomial degree, model outperforms several artificial intelligence (AI) modeling approaches which are carried out widely in the prediction of HSC compression strength. Besides, support vector machine (SVM) model was developed in this study and compared with the HORSM. The HORSM models outperformed the SVM models according to different statistical measures. Additionally, HORSM models managed to perfectly predict the HSC compressive strength in less than one second to accomplish the learning processes. While, other AI models including SVM much longer time. Lastly, the use of HORSM for the first time in the concrete technology field provided much accurate prediction results and it has great potential in the field of concrete technology.

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Titel: Prediction of high-strength concrete: high-order response surface methodology modeling approach
verfasst von: Mohammed Majeed Hameed
Mohamed Khalid AlOmar
Wajdi Jaber Baniya
Mohammed Abdulhakim AlSaadi
Publikationsdatum: 06.02.2021
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe Sonderheft 2/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-021-01284-z

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