Data Driven Calibration of Analytical Concrete Creep Models Considering Preloading Effects Using Gaussian Processes

2026-04-28

AI Summary

Traditional concrete creep models struggle to accurately capture the influence of preloading on both the magnitude and variability of creep, leading to insufficient predictive accuracy. This study proposes a data-driven calibration approach based on Gaussian process regression that, for the first time, systematically incorporates preloading intensity, application timing, and concrete age into an analytical creep model. The proposed method not only significantly enhances prediction accuracy but also enables quantification of predictive uncertainty and supports optimal experimental design. By doing so, it provides a robust theoretical foundation for the sustainable design of concrete structures.

Abstract

The time-dependent deformation of concrete, particularly creep, remains a key challenge for reliable and material-efficient design. Experimental results show that tailored preloading, short-term loads exceeding the subsequent sustained load, can reduce both the magnitude and variability of creep strains which may be associated with beneficial microstructural changes. Building on these insights, this article employs Gaussian Process Regression (GPR) to calibrate analytical creep models, incorporating the effects of preloading intensity, timing, and concrete age into conventional predictions. The study pursues three main objectives: (i) calibrating a creep model using GPR based on experimental data, (ii) evaluating the impact of training data selection and preparation, and (iii) analysing model performance depending on the available experimental duration. The results demonstrate that GPR can improve model accuracy, quantify uncertainties, and support optimal test planning, while also enhancing understanding of preloading effects and contributing to more reliable and sustainable concrete creep predictions.

Problem

• concrete creep
• preloading effects
• model calibration
• data-driven
• time-dependent deformation

Innovation

• Gaussian Process Regression
• concrete creep
• preloading effects
• data-driven calibration
• uncertainty quantification