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

Probabilistic engineering mechanics

Concrete gravity dams model parameters updating using static measurements

Probabilistic model updating via variational Bayesian inference and adaptive Gaussian process modeling

The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases.

/pdf/dynamic-structural-health-monitoring-for-concrete-gravity-5eceo2wn26.pdf

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in s...

https://journals.sagepub.com/doi/pdf/10.1177/1369433218811540

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

https://iopscience.iop.org/article/10.1088/1361-665X/aae9b4/pdf

Diagnosis and accuracy enhancement of compressive-sensing signal reconstruction in structural health monitoring using multi-task sparse Bayesian learning

Changsong Shao

Papers

State-of-the-art review on Bayesian inference in structural system identification and damage assessment

Diagnosis and accuracy enhancement of compressive-sensing signal reconstruction in structural health monitoring using multi-task sparse Bayesian learning