Deep active sequential learning of stress evolution in early-age concrete informed by thermo-chemo-mechanical modelling

This study presents an integrated finite-element–machine-learning framework for predicting early-age stress evolution in concrete materials/structures by combining an enhanced thermo-chemo-mechanical (TCM) model, deep sequential learning (DSL), and active learning (AL). The proposed TCM model incorporates experimentally informed viscoelasticity, a stable exponential creep–relaxation conversion, and an efficient exponential algorithm for the Maxwell-chain formulation in finite element analysis, which is further validated by a temperature stress testing machine. This model generates high-fidelity stress–time data across diverse mixtures, temperatures, and structural configurations. These simulations are used to train a Gated Recurrent Unit with Monte Carlo Dropout (GRU-MCD) model, whose predictive performance surpasses conventional point-wise approaches such as Light Gradient Boosting Machine and Gaussian Process Regression, yielding higher accuracy with reduced overfitting. The AL strategy further enhances efficiency by enabling the GRU-MCD model to achieve the accuracy of ∼900 Latin Hypercube samples using only ∼200 samples selected by active learning. Although demonstrated on a wall–base structure, the proposed framework is general and applicable to other cementitious material or structural systems, providing an effective tool for cracking-risk evaluation, reliability analysis, and the design of low-carbon concrete structures.