Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches
Artificial neural network
Composite material
Technology
Artificial intelligence
High-performance Cement-based Materials
Self-Compacting Concrete
Naturwissenschaften
Fiber Reinforced Concrete in Civil Engineering
Compressive strength
TP1-1185
02 engineering and technology
0201 civil engineering
Engineering
Machine learning
Multilayer perceptron
Geopolymer and Alternative Cementitious Materials
alkali-activated concrete
Civil and Structural Engineering
Perceptron
T
Chemical technology
Technik
624
Computer science
Materials science
rheological properties
Sonstiges
Mechanisms and Mitigation of Autogenous Shrinkage in Concrete
Physical Sciences
Rheology
multilayer perceptron neural networks
Alkali-Activated Materials
DOI:
10.1515/rams-2024-0006
Publication Date:
2024-04-12T13:10:57Z
AUTHORS (6)
ABSTRACT
Abstract
Using artificial intelligence-based tools, this research aims to establish a direct correlation between the alkali-activated concrete (AAC) mix design factors and their performances. More specifically, the machine learning system was fed new property data obtained from AAC mixes used in laboratory experiments. The rheological parameters (yield stress [static/dynamic] and plastic viscosity) of AAC were predicted using the multilayer perceptron neural network (MLPNN) and bagging ensemble (BE) models. In addition, the R
2 values, k-fold analyses, statistical checks, and the dissimilarity between the experimental and predicted compressive strength were employed to assess the performance of the created models. Also, the SHapley additive exPlanation (SHAP) approach was used for examining the relevance of influencing parameters. The BE approach was found to be significantly accurate in all prediction models, with R
2 greater than 0.90, and MLPNN models were found to be moderately precise, with R
2 slightly below 0.90. However, the error assessment through statistical checks and k-fold analysis also validated the higher precision of BE models over the MLPNN models. Building models that can calculate rheological properties of AAC for different values of input parameters could save a lot of time and money compared to doing the tests in a laboratory. In order to ascertain the required amounts of raw materials of AAC, investigators, as well as businesses, may find the SHAP study helpful.
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