An Exploratory Study on the Effects of Liquefaction-Induced Sand Ejecta on Foundation Settlements Based on Moderate-Scale Shake Table Tests

2021 ◽  
Author(s):  
Marie Buhl ◽  
Milad Jahed Orang ◽  
Ramin Motamed
2021 ◽  
pp. 102886
Author(s):  
Jianyang Xue ◽  
Pengchun Hu ◽  
Fengliang Zhang ◽  
Yan Zhuge

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Izumi Nakamura ◽  
Akihito Otani ◽  
Masaki Shiratori

Pressurized piping systems used for an extended period may develop degradations such as wall thinning or cracks due to aging. It is important to estimate the effects of degradation on the dynamic behavior and to ascertain the failure modes and remaining strength of the piping systems with degradation through experiments and analyses to ensure the seismic safety of degraded piping systems under destructive seismic events. In order to investigate the influence of degradation on the dynamic behavior and failure modes of piping systems with local wall thinning, shake table tests using 3D piping system models were conducted. About 50% full circumferential wall thinning at elbows was considered in the test. Three types of models were used in the shake table tests. The difference of the models was the applied bending direction to the thinned-wall elbow. The bending direction considered in the tests was either of the in-plane bending, out-of-plane bending, or mixed bending of the in-plane and out-of-plane. These models were excited under the same input acceleration until failure occurred. Through these tests, the vibration characteristic and failure modes of the piping models with wall thinning under seismic load were obtained. The test results showed that the out-of-plane bending is not significant for a sound elbow, but should be considered for a thinned-wall elbow, because the life of the piping models with wall thinning subjected to out-of-plane bending may reduce significantly.


1998 ◽  
Vol 31 (10) ◽  
pp. 676-682 ◽  
Author(s):  
Y. L. Mo ◽  
W. L. Hwang

2014 ◽  
Vol 23 (12) ◽  
pp. 125002 ◽  
Author(s):  
Y M Parulekar ◽  
A Ravi Kiran ◽  
G R Reddy ◽  
R K Singh ◽  
K K Vaze

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 452
Author(s):  
Qun Yang ◽  
Dejian Shen

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.


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