Supporting data for "Structural Identification and Model Updating Based on Active Learning Kriging Approach and Bayesian Inference"

Supporting data for "Structural Identification and Model Updating Based on Active Learning Kriging Approach and Bayesian Inference"

Model updating, which is also a typical application of structural identification, is an essential topic in structural health monitoring because it calibrates the numerical models for response simulation, reliability analysis and damage assessment. A novel active learning Kriging model updating framework is proposed along with three algorithms and application cases. With the active learning approach, those regions which might help improve the current Kriging predictor for model updating are refined and explored automatically. The Kriging model generated by this data-driven process with a limited sample size has satisfactory local accuracy, high efficiency and robust performance for model updating. The framework is also extended to structural identification of time-varying systems, e.g. cable force monitoring. 

A PhD thesis named "Structural Identification and Model Updating Based on Active Learning Kriging Approach and Bayesian Inference" is going to be submitted for examination as well.

The dataset includes:
1. Program code of proposed algorithms
2. Numerical and application cases (results) in Chapter 4, 5 and 7 for the proposed algorithms
3. Figures and tables used in the thesis

Sub-folders:
Folder1: Chapter4_AlTestBeam
The datafile of the test example of aluminum test beam shown in Chapter 4 of the thesis
Folder2: Chapter5_NumericalExampleOfAContinuousBridge
The datafile of the numerical example of a continuous bridge shown in Chapter 5 of the thesis
Folder3: Chapter7_CableForceMonitoring
The datafile of the application test cases of cable force monitoring in Chapter 7 of the thesis
Folder4: Figures&TablesInThesis
The figures and tables used in this thesis.

本数据集旨在支持题为《基于主动学习克里金方法和贝叶斯推断的结构识别与模型更新》的研究，该研究是结构健康监测领域的一项关键议题，其核心在于对数值模型进行校准，以实现响应模拟、可靠性分析和损伤评估。本研究提出了一种创新的主动学习克里金模型更新框架，并伴随三种算法及其实际应用案例。通过主动学习方法，能够自动细化并探索那些有助于提升当前克里金预测器模型更新性能的区域。基于有限样本数据驱动的克里金模型，在模型更新方面展现出令人满意的局部精度、高效性及稳健性能。此外，该框架亦被拓展应用于时变系统的结构识别，例如电缆张力监测。同时，一份名为《基于主动学习克里金方法和贝叶斯推断的结构识别与模型更新》的博士论文也将提交审议。数据集包含以下内容：

1. 提出算法的程序代码

2. 第4章、第5章和第7章中针对提出算法的数值与应用案例（结果）

3. 论文中使用的图表

子文件夹结构如下：

Folder1: Chapter4_AlTestBeam

该文件夹包含论文第4章中展示的铝制测试梁的测试用例数据文件。

Folder2: Chapter5_NumericalExampleOfAContinuousBridge

该文件夹包含论文第5章中展示的连续桥梁数值示例的数据文件。

Folder3: Chapter7_CableForceMonitoring

该文件夹包含论文第7章中电缆张力监测应用测试案例的数据文件。

Folder4: Figures&TablesInThesis

该文件夹包含论文中使用的图表。