RESULT ANALYSIS OF FLY ASH CONCRETE STRENGTH USING ADVANCED STRUCTURAL COMPUTING TECHNIQUE

Abstract

The compressive strength of concrete is a crucial parameter in structural design, yet its determination in a laboratory setting is both time-consuming and expensive. The prediction of compressive strength in fly ash-based concrete can be accelerated through the use of machine learning algorithms with artificial intelligence, which can effectively address the problems associated with this process. This paper presents the most innovative model algorithms established based on artificial intelligence technology. These include three single models a fully connected neural network model (FCNN), a convolutional neural network model (CNN), and a transformer model (TF) and three hybrid models FCNN + CNN, TF + FCNN, and TF + CNN. A total of 471 datasets were employed in the experiments, comprising 7 input features: cement (C), fly ash (FA), water (W), superplasticizer (SP), coarse aggregate (CA), fine aggregate (S), and age (D). Six models were subsequently applied to predict the compressive strength (CS) of fly ash-based concrete. Furthermore, the loss function curves, assessment indexes, linear correlation coefficient, and the related literature indexes of each model were employed for comparison. Keywords: HVFA Concrete, CANMET, Cementitious Material, FAC, Control Concrete, HSC, HPC, SCC, Examined Beneath.