# Steel Slag-Enhanced Cement-Stabilized Recycled Aggregate Bases: Mechanical Performance and PINN-Based Sulfate Diffusion Prediction

## TL;DR

Adding steel slag to recycled aggregate improves pavement base strength and sulfate resistance, with a new model predicting sulfate diffusion effectively.

## Contribution

Integrating steel slag into CSR and developing a PINN-based model to predict sulfate diffusion in pavement bases.

## Key findings

- CSRS showed 6.7% and 16.0% higher UCS at 7 and 28 days compared to CSR.
- Sulfate resistance coefficient of CSRS increased by 18.8% compared to CSR.
- PINN model predicted sulfate concentration reduction in CSRS with an average relative error below 14%.

## Abstract

The application of cement-stabilized recycled aggregate (CSR) in pavement bases is constrained by the high porosity and low strength of recycled aggregate (RA), whereas sulfate transport and durability mechanisms are less reported. To address this issue, this study incorporated high-strength and potentially reactive steel slag aggregate (SSA) into CSR to develop steel slag-enhanced cement-stabilized recycled aggregate (CSRS). The mechanical performance of the mixtures was evaluated through unconfined compressive strength (UCS) and indirect tensile strength (ITS) tests, and their durability was assessed using thermal shrinkage and sulfate resistance tests. In addition, a sulfate prediction model based on a physics-informed neural network (PINN) was developed. The results showed that, compared with CSR, the 7-day and 28-day UCS of CSRS increased by 6.7% and 16.0%, respectively, and the ITS increased by 4.3% and 5.9%. Thermal shrinkage tests indicated that CSR and CSRS, incorporating RA and SSA, exhibited slightly higher thermal shrinkage strain than cement-stabilized natural aggregate (CSN). During sulfate attack, SSA significantly improved the sulfate resistance of CSR, with the sulfate resistance coefficient of CSRS increasing by 18.8% compared to CSR. Furthermore, the PINN model predicted that, in 3%, 5%, and 7% sodium sulfate solutions, the sulfate concentration at a 1 mm depth in CSRS was reduced by 35.6%, 21.8%, and 29.4%, respectively, compared to CSR, with an average relative error below 14%, confirming its reliability. Therefore, these findings demonstrate that the incorporation of SSA markedly enhances the mechanical properties and sulfate resistance of CSR, and that the PINN model provides an effective tool for accurate simulation and prediction of sulfate diffusion.