TITLE:
Prediction Model of Compressive Strength of Fly Ash-Slag Concrete Based on Multiple Adaptive Regression Splines
AUTHORS:
Jianjun Dong, Hongyang Xie, Yiwen Dai, Yong Deng
KEYWORDS:
Fly Ash-Slag Concrete, Compressive Strength, Multiple Adaptive Regression Splines, Prediction Model
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.12 No.3,
March
15,
2022
ABSTRACT: Accurate prediction of compressive strength of concrete is one of the key
issues in the concrete industry. In this
paper, a prediction method of fly ash-slag concrete compressive strength
based on multiple adaptive regression splines (MARS) is proposed, and the model
analysis process is determined by analyzing the principle of this algorithm.
Based on the Concrete Compressive Strength dataset of UCI, the MARS model for
compressive strength prediction was constructed with cement content, blast
furnace slag powder content, fly ash content, water content, reducing agent
content, coarse aggregate content, fine aggregate content and age as
independent variables. The prediction results of artificial neural network
(BP), random forest (RF), support vector machine (SVM), extreme learning
machine (ELM), and multiple nonlinear regression (MnLR) were compared and
analyzed, and the prediction accuracy and model stability of MARS and RF models
had obvious advantages, and the comprehensive performance of MARS model was
slightly better than that of RF model. Finally, the explicit expression of the
MARS model for compressive strength is given, which provides an effective
method to achieve the prediction of compressive strength of fly ash-slag
concrete.