Predicting early-age temperature evolution in mass concrete is crucial for controlling thermal cracks. This process involves two distinct physical stages: an initial, hydration-driven heating stage (Stage I) and a subsequent, environment-dominated cooling stage (Stage II). To address this challenge, we propose a novel two-stage hybrid modeling strategy that decouples the underlying physical processes. This approach was developed and validated using a 450-h on-site monitoring dataset. For the deterministic heating phase (Stage I), we employed polynomial regression. For the subsequent stochastic cooling phase (Stage II), a Random Forest algorithm was used to model the complex environmental interactions. The proposed hybrid model was benchmarked against several alternatives, including a physics-based finite element model (FEM) and a single Random Forest model. During the critical cooling stage, our approach demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of $0.24°\mathrm{C}$. This represents a 17.2% improvement over the best-performing single model. Furthermore, cumulative error analysis indicated that the hybrid model maintained a stable and unbiased prediction trend throughout the monitoring period. This addresses a key weakness in single-stage models, where underlying phase-specific errors can accumulate and lead to long-term drift. The proposed framework offers an accurate, robust, and transferable paradigm for modeling other complex engineering processes that exhibit distinct multi-stage characteristics.

There exist various methods for solving the dynamic analysis problem in earthquake engineering. While numerical integration techniques are conventionally classified as either explicit or implicit approaches, both categories suffer from fundamental constraints that compromise their general effectiveness. A type of model-based integration algorithm combines explicit and implicit algorithm advantages, making it a hot topic in research. Based on the generalized-α algorithms, this study proposes a model-based integration algorithm by embedding upon Newton iteration to make its displacement solution in explicit form. The root locus method was employed to analyze the stability of the algorithm for single-degree-of-freedom systems containing nonlinear restoring force. Two models were selected to verify the algorithm’s accuracy and stability: three-storey and eight-storey shear-type structural systems with metal dampers. The proposed algorithm, Chang method, and CR method were utilized for the dynamic analysis of the emulated systems. The results indicate that the proposed algorithm has high accuracy and favorable stability for nonlinear dynamic problems.

Accurately predicting the degree of hydration in cement systems blended with slag and fly ash is difficult when experimental data are limited. Empirical kinetic models typically need recalibration for each specific blend, while purely data-driven approaches often fail to maintain physical consistency. In this work, we present a physics-informed neural network for predicting isothermal hydration by incorporating the Avrami–Erofeev–Arrhenius ordinary differential equation into the training loss and using a composition-focused sub-network to relate blend proportions to blend-specific kinetic parameters. The model was trained and tested on 29,379 calorimetry data points from 77 multi-component cement systems gathered from two open-access datasets. For previously unseen test systems, it reached an $R^2$ of 0.9864 and a symmetric mean absolute percentage error (sMAPE) of 25.3%. Its overall sMAPE was lower than that of a neural network with the same architecture but without physics-based constraints (34.9%); stage-resolved analysis showed that the largest difference occurred during early hydration. Across eight random seeds, the physics constraint did not confer a data-efficiency or training-stability advantage over the same-architecture network. The distinguishing features of the PINN are instead its explicit ODE and monotonicity regularization and its composition-conditioned effective parameters. Although random forest regression produced lower pointwise error, it failed to maintain physically consistent hydration-rate behavior. Overall, the framework provides a composition-conditioned surrogate that incorporates kinetic regularization within the composition range investigated.