Bayesian Methods for Structural Dynamics and Civil Engineering

By Ka-Veng Yuen. 2010

511 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fb84a4c6d4052dfc012cb41a190fa3921b9b85e2

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. Dianqing Li
2. Kok-Kwang Phoon
3. Z. Cao
4. Yu Wang
5. Mi Tian
6. Yang Liu
7. Oral Büyüköztürk
8. Shuo Zheng

In Statistics and Probability

1. Z. Cao
2. Dianqing Li
3. Kok-Kwang Phoon
4. Yu Wang
5. Shuo Zheng
6. Hadi Salehi
7. Hao Sun
8. Mi Tian

Citations in the corpus

• Emerging artificial intelligence methods in structural engineering (2018, background): “All of the aforementioned problems can be modeled and treated as uncertainties [29].”
• Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling (2019, background): “The input and output are formatted into the shape of [10, 1001, 1] and [10, 1001, 3] for the training datasets.”
• Vision-Only Robot Navigation in a Neural Radiance World (2021, background): “Hessian of a Gaussian loss function and the covariance [32] to yield the posterior covariance,”
• Bayesian identification of random field model using indirect test data (2016, methodology): “Using Bayes' Theorem (e.g., Ang and Tang, 2007; Yuen, 2010a, 2010b; Wang et al., 2016), PðXjξ̂;MÞ is written as: P Xjξ̂;M ¼ K−1P ξ̂jX;M P XjMð Þ ð1Þ in which ξ̂ is a set of values of the logarithm of qmeasured at n different depths D1, D2,…, Dn in a sand layer by CPT, i.e., ½ξ̂ðD1Þ; ξ̂ðD2Þ;…;…”
• Probabilistic updating of building models using incomplete modal data (2016, methodology): “Therefore, we obtain the augmented posterior PDF through hierarchical Bayesian inference [8], viz.”
• Bayesian identification of soil stratigraphy based on soil behaviour type index (2019, methodology): “Recently, some advanced methods, such as transitional Markov Chain Monte Carlo (TMCMC) algorithm (Ching and Chen 2007; Yuen 2010b; Ching and Wang 2016) and BUS with SuS (Straub and Page 14 of 57”