Predictive modeling of the long-term effects of combined chemical admixtures on concrete compressive strength using machine learning algorithms

Abstract

The combinations of chemical admixtures play a significant role in producing concrete. Understanding their mechanical properties is crucial for ensuring safety and durability. Among these properties, compressive strength (CS) stands out as the most critical attribute of concrete. This research introduced a distinct concrete mix utilizing a blend of superplasticizers, retarders, and air-entraining agents, designed to meet specific construction requirements for enhanced durability and workability. The study examines the long-term effects of combining various chemical admixtures on the compressive strength of concrete, utilizing advanced experimental data and machine learning models with a level of precision and detail that has been relatively underexplored.This investigation includes a substantial increase in samples (7845) compared to previous research. Samples were tested at different ages, ranging from 3 days to 3 years. To enhance the accuracy of machine learning (ML) models, a novel approach to data distribution simulation based on K-means clustering was employed for generating synthetic data. Various ML models, including Nonlinear Autoregressive with Exogenous Inputs (NARX), Support Vector Regression (SVR), Radial Basis Function (RBF), Multilayer Perceptron (MLP), Decision Tree (DT), and Random Forest (RF), were evaluated for predicting the compressive strength of concrete (CS). Results show the NARX model outperforms the other models, validated by experimental data and k-fold cross-validation. This model showed a coefficient of determination (R2 = 0.9932), normalized mean square error (NMSE = 18.97), normalized mean absolute error (NMAE = 2.49), and normalized root-mean-square error (NRMSE = 6.18). The findings revealed that in Mix - 1, the compressive strength increased from 450 (kg/cm2) at 28 days to 480 (kg/cm2)at 90 days, but then decreased to 420 (kg/cm2) after three years. This reduction in strength may lead to decreased load-bearing capacity and higher repair costs, highlighting the need to revise concrete design standards. This study emphasizes revising some current concrete structure design standards to accommodate the observed long-term reductions in compressive strength.

Keywords

• Chemical admixtures
• Compressive strength prediction
• Machine learning
• Synthetic data
• NARX
• RBF