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.

翻译：混凝土随时间变化的变形，特别是徐变效应，仍是实现可靠且材料高效设计的核心挑战。实验结果表明，定制化预加载——即短期荷载超过后续持续荷载——可降低徐变应变的幅值和变异性，这可能与有益的微观结构变化有关。基于这些认识，本文采用高斯过程回归对分析型徐变模型进行校准，将预加载强度、时机和混凝土龄期的影响纳入传统预测框架。研究围绕三个主要目标展开：（i）基于实验数据利用高斯过程回归校准徐变模型；（ii）评估训练数据选择与预处理的影响；（iii）分析模型性能随可用实验时长的变化。结果表明，高斯过程回归能提升模型精度、量化不确定性、支持最优试验规划，同时增进对预加载效应的理解，助力实现更可靠且可持续的混凝土徐变预测。