Journal ArticleDOI
Hierarchical sparse Bayesian learning for structural damage detection: Theory, computation and application
TLDR
The results show that, even for the real data, the proposed method can reliably detect, locate and assess damage of the benchmark structure by inferring substructure stiffness losses using the identified modal parameters from the calibration and monitoring stages.About:
This article is published in Structural Safety.The article was published on 2017-01-01. It has received 71 citations till now. The article focuses on the topics: Structural health monitoring & Synthetic data.read more
Citations
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Journal ArticleDOI
Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019
Rongrong Hou,Yong Xia +1 more
TL;DR: The progress in the area of vibration-based damage identification methods over the past 10 years is reviewed to help researchers and practitioners in implementing existing damage detection algorithms effectively and developing more reliable and practical methods for civil engineering structures in the future.
Journal ArticleDOI
State-of-the-art review on Bayesian inference in structural system identification and damage assessment
TL;DR: The focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but the presented theories also have a considerably broader applicability for inverse problems in science and technology.
Journal ArticleDOI
Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment
TL;DR: This paper proposes two Gibbs sampling algorithms based on a similar hierarchical sparse Bayesian learning model that have much broader applicability for inverse problems in science and technology where system matrices are to be inferred from noisy partial information about their eigenquantities.
Journal ArticleDOI
AKOIS: An adaptive Kriging oriented importance sampling method for structural system reliability analysis
TL;DR: This paper presents an adaptive Kriging oriented importance sampling (AKOIS) approach, which is able to adaptively cover all branches of the investigated limit-state surface for structural system reliability analysis.
Journal ArticleDOI
Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring
Yong Huang,James L. Beck,Hui Li +2 more
TL;DR: It is shown that the data correlations for different tasks are taken into account more effectively by using the hierarchical model with a common prediction‐error precision parameter across all related tasks, which leads to a better learning performance.
References
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Journal ArticleDOI
Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
Journal ArticleDOI
Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
BookDOI
Probability theory : the logic of science
TL;DR: In this article, a survey of elementary applications of probability theory can be found, including the following: 1. Plausible reasoning 2. The quantitative rules 3. Elementary sampling theory 4. Elementary hypothesis testing 5. Queer uses for probability theory 6. Elementary parameter estimation 7. The central, Gaussian or normal distribution 8. Sufficiency, ancillarity, and all that 9. Repetitive experiments, probability and frequency 10. Advanced applications: 11. Discrete prior probabilities, the entropy principle 12. Simple applications of decision theory 15.
Journal ArticleDOI
A summary review of vibration-based damage identification methods
TL;DR: In this paper, the authors provide an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response, including frequency, mode shape, and modal damping.
Journal ArticleDOI
Bayesian Compressive Sensing
Shihao Ji,Ya Xue,Lawrence Carin +2 more
TL;DR: The underlying theory, an associated algorithm, example results, and comparisons to other compressive-sensing inversion algorithms in the literature are presented.