Strength prediction of concrete with large amount of fly ash based on improved random forest Online publication date: Thu, 09-Nov-2023
by Fangfang Zhang; Huajian Fang; Pengyun Li; Yuandong Qiao
International Journal of Microstructure and Materials Properties (IJMMP), Vol. 16, No. 6, 2023
Abstract: In order to solve the problems of the large gap between the predicted results and the actual strength value, the high MSE value and the long time in the traditional method, a strength prediction method for concrete with large amount of fly ash based on an improved random forest is proposed. Firstly, genetic algorithm and BP neural network are used to screen suitable prediction indicators. Then, the bee mating intelligent optimisation algorithm is used to improve the traditional random forest algorithm. Finally, taking the strength grade and cement consumption as the input variables of the model and the concrete strength value as the output variables, the improved random forest algorithm is used to establish the concrete strength prediction model. Experimental results show that the difference between the predicted results of the proposed method and the actual strength value is small, the MSE value is low, and the time is short.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Microstructure and Materials Properties (IJMMP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook