Data Driven Calibration of Analytical Concrete Creep Models Considering Preloading Effects Using Gaussian Processes
For civil engineers designing concrete structures, this work offers a data-driven calibration approach that accounts for preloading effects, but the improvement is incremental and domain-specific.
This study uses Gaussian Process Regression to calibrate analytical concrete creep models, incorporating preloading effects. The method improves model accuracy, quantifies uncertainties, and supports optimal test planning, enhancing reliability of creep predictions.
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.