In this paper, an active learning framework for structural baseline model exploration is proposed based on the Kriging method. The framework is built to solve the problem that when the traditional Kriging approach needs to calibrate more structural parameters, the performance of Kriging predictors hinges very much upon the number of samples obtained from the finite element analysis. A novel information entropy-probability learning function is derived based on Bayesian inference and information entropy. To give a full picture, the uncertainties of different responses should also be quantified during the updating process. Then the effects of uncertainties are considered in both the learning function and objective function constructed from the posterior probabilities. The proposed algorithm is first verified experimentally by a two-span continuous beam considering different types of responses. The framework is then further improved for updating the baseline model of a cable-stayed bridge using field data. The active learning approach, as compared to the ordinary Kriging method, can achieve good performance without undue computational cost and the need for imposing weights on different responses. The proposed baseline model exploration method can be extensively applied to bridge engineering because it facilitates the calibration of numerical models using field measurements and response simulation of extreme loading with significant improvement in computational efficiency and performance of Kriging predictors.

本文提出了一种基于克里金法的结构基线模型探索的主动学习框架。该框架的建立是为了解决传统克里金方法需要标定更多结构参数时，克里金预测器的性能很大程度上取决于有限元分析获得的样本数量的问题。基于贝叶斯推理和信息熵，推导了一种新的信息熵-概率学习函数。为了全面了解情况，在更新过程中还应该量化不同响应的不确定性。然后在学习函数和由后验概率构建的目标函数中考虑不确定性的影响。所提出的算法首先通过考虑不同类型响应的两跨连续梁进行实验验证。然后进一步改进该框架，以使用现场数据更新斜拉桥的基线模型。与普通克里金法相比，主动学习方法可以实现良好的性能，而无需过多的计算成本，也不需要对不同的响应施加权重。所提出的基线模型探索方法可以广泛应用于桥梁工程，因为它有助于使用现场测量和极端载荷响应模拟来校准数值模型，并显着提高克里金预测器的计算效率和性能。