Data-Driven Assessment of Concrete Mixture Compositions on Chloride Transport via Standalone Machine Learning Algorithms
Mojtaba Aliasghar-Mamaghani, Mohammadreza Khalafi
TL;DR
The paper tackles chloride ingress in concrete and its dependence on concrete mixture compositions under diffusion-dominated conditions. It conducts a systematic comparison of standalone ML algorithms (LR, KNN, KRR, SVR, GPR, MLP, GRU) trained on a broad experimental dataset, with model performance assessed via $R^2$ and other error metrics using 10-fold CV. The study finds that $KRR$, $GPR$, and $MLP$ achieve the best predictive accuracy (test $R^2$ around $0.90$–$0.91$), while the GRU fails to generalize to unseen data. It identifies latent correlations, notably inverse relationships with several cementitious components and a direct relationship with coarse aggregate content, underscoring the potential of surrogate approaches to guide mix-design for improved durability.
Abstract
This paper employs a data-driven approach to determine the impact of concrete mixture compositions on the temporal evolution of chloride in concrete structures. This is critical for assessing the service life of civil infrastructure subjected to aggressive environments. The adopted methodology relies on several simple and complex standalone machine learning (ML) algorithms, with the primary objective of establishing confidence in the unbiased prediction of the underlying hidden correlations. The simple algorithms include linear regression (LR), k-nearest neighbors (KNN) regression, and kernel ridge regression (KRR). The complex algorithms entail support vector regression (SVR), Gaussian process regression (GPR), and two families of artificial neural networks, including a feedforward network (multilayer perceptron, MLP) and a gated recurrent unit (GRU). The MLP architecture cannot explicitly handle sequential data, a limitation addressed by the GRU. A comprehensive dataset is considered. The performance of ML algorithms is evaluated, with KRR, GPR, and MLP exhibiting high accuracy. Given the diversity of the adopted concrete mixture proportions, the GRU was unable to accurately reproduce the response in the test set. Further analyses elucidate the contributions of mixture compositions to the temporal evolution of chloride. The results obtained from the GPR model unravel latent correlations through clear and explainable trends. The MLP, SVR, and KRR also provide acceptable estimates of the overall trends. The majority of mixture components exhibit an inverse relation with chloride content, while a few components demonstrate a direct correlation. These findings highlight the potential of surrogate approaches for describing the physical processes involved in chloride ingress and the associated correlations, toward the ultimate goal of enhancing the service life of civil infrastructure.