On the cross correlation function amplitude vector and its application to structural damage detection

By Zhichun Yang, Z. Yu, Hao Sun. 2007

67 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/155729dfe39ffc749553076a91ed9fb4bfeeed7c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Arman Malekloo
2. Mark A. Girolami
3. Ekin Ozer
4. Mohammad AlHamaydeh

In Mechanics of Materials

1. Arman Malekloo
2. Mark A. Girolami
3. Ekin Ozer
4. Mohammad AlHamaydeh

Citations in the corpus

• Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “Low-order (dimension) time-series modeling, techniques such as autoregressive moving-average model (ARMA), 154 cross-correlation function (CCF), 155 and stochastic subspace identi fi cation (SSI) 156,157 can be proven useful in extracting highly damage-sensitive feature vectors.”