Development of Time Series Models and Algorithms: Creep Prediction for Low-Carbon Concrete Materials

In practical engineering applications, the use of low-carbon concrete materials is in line with the principles of sustainable development and helps to reduce the impact on the environment. Creep effects are particularly critical in the research on such materials. However, traditional characterization methods are time-consuming and often fail to account for the interactions of multiple factors. This study constructs a time-series database capturing the behavioral characteristics of low-carbon concrete materials over time. Three temporal prediction models-Artificial Neural Network (ANN), Random Forest (RF), and Long Short-Term Memory (LSTM) networks-were retrained for creep prediction. To address limitations in model architecture and algorithmic frameworks, an enhanced Adaptive Crowned Porcupine Optimization algorithm (ACCPO) was implemented. The improved performance of the ACCPO was validated using four diverse benchmark test functions. Post-optimization results showed remarkable improvements. For ANN, RF, and LSTM, single-metric accuracies increased by 20%, 19%, and 6%, reaching final values of 95.9%, 93.9%, and 97.8%, respectively. Comprehensive evaluation metrics revealed error reductions of 22.6%, 7.9%, and 8% across the respective models. These results confirm the rationality of the proposed temporal modeling framework and the effectiveness of the ACCPO algorithm. Among them, the ACCPO-LSTM time series model is the best choice.