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

TL;DR

This paper uses Gaussian Process Regression to calibrate concrete creep models, incorporating preloading effects, to improve prediction accuracy and understanding of creep behavior.

Contribution

It introduces a GPR-based calibration method that accounts for preloading effects, enhancing model accuracy and uncertainty quantification in concrete creep predictions.

Findings

GPR improves the accuracy of creep model calibration.

Preloading effects can be effectively incorporated into models.

Model performance depends on experimental data duration.

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.