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.