Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Regularization Of Inverse Problems

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019

Department of Civil Engineering, State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China Department of Civil, Environmental, and Geomatic Engineering, ETH Zurich, Zurich, Switzerland School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, Iowa, USA Department of Intelligent Technologies, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland

Recent progress and future trends on damage identification methods for bridge structures

Summary
Conventional vibration-based damage detection methods employ the Tikhonov regularization in model updating to deal with the problems of underdeterminacy and measurement noise. However, the Tikhonov regularization technique tends to provide over smooth solutions that the identified damage is distributed to many structural elements. This result does not match the sparsity property of the actual damage scenario, in which structural damage typically occurs at a small number of locations only in comparison with the total elements of the entire structure. In this study, an l1 regularization-based model updating technique is developed by utilizing the sparsity of the structural damage. Both natural frequencies and mode shapes are employed during the model updating. A strategy of selecting the regularization parameter for the l1 regularization problem is also developed. A numerical and an experimental examples are utilized to demonstrate the effectiveness of the proposed damage detection method. The results showed that the proposed l1 regularization-based method is able to locate and quantify the sparse damage correctly over a large number of elements. The effects of the mode number on the damage detection results are also investigated. The advantage of the present l1 regularization over the traditional l2 regularization method in damage detection is also demonstrated.

Structural damage detection based on l1 regularization using natural frequencies and mode shapes

Selection of regularization parameter for l1-regularized damage detection

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.2274

Rongrong Hou

Papers

Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019

Structural damage detection based on l1 regularization using natural frequencies and mode shapes

Selection of regularization parameter for l1-regularized damage detection

Genetic algorithm based optimal sensor placement for L1-regularized damage detection

Sparse Bayesian learning for structural damage detection using expectation–maximization technique