A Deep Learning Approach to Predicting the Durability of Limestone Aggregates Using the FT-Transformer

The durability of limestone aggregates is a critical factor affecting the long-term performance of concrete, particularly under aggressive environmental conditions. However, conventional durability tests such as the magnesium sulfate soundness test are time-consuming and labor-intensive. In this study, a transformer-based deep learning model, namely the FT-Transformer, was employed to predict the magnesium sulfate soundness loss of limestone aggregates using standard aggregate index properties. The dataset used in the modeling stage consisted of 108 limestone aggregate samples, each characterized by particle density, water absorption, Los Angeles fragmentation, and magnesium sulfate soundness loss. Although four standardized laboratory tests were conducted for each sample, yielding 432 individual test results in total, the prediction dataset comprised 108 complete observations. The predictive performance of the FT-Transformer was evaluated and compared with Linear Regression, Polynomial Regression, and Support Vector Regression models. Under the single-split evaluation, the FT-Transformer achieved a test R2 value of 0.6473 and a test MSE value of 0.0212. In addition, a repeated random-split statistical analysis demonstrated that the FT-Transformer achieved better average predictive performance than Linear Regression across 50 repeated train, validation and test partitions. These findings indicate that transformer-based tabular learning can provide an effective and practically applicable framework for aggregate durability prediction and may support preliminary material assessment and engineering decision-making processes.