Assessment of SSI effects on stiffness of single and grouped helical piles in dry sand from large shake table tests

2021 ◽  
Author(s):  
A. F. Fayez ◽  
M. H. El Naggar ◽  
A. B. Cerato ◽  
A. Elgamal
Keyword(s):  
Shake Table ◽  
Shake Table Tests ◽  
Helical Piles ◽  
Dry Sand

Data Reduction and Dynamic p-y Curves of Helical Piles from Large-Scale Shake Table Tests

2019 ◽  
Vol 145 (10) ◽  
pp. 04019075 ◽  
Author(s):  
M. K. ElSawy ◽  
M. H. El Naggar ◽  
A. B. Cerato ◽  
A. W. Elgamal
Keyword(s):  
Data Reduction ◽  
Large Scale ◽  
Shake Table ◽  
Shake Table Tests ◽  
Helical Piles

Seismic Behavior of Brick Cave Dwellings: Shake Table Tests

2021 ◽  
pp. 102886
Author(s):  
Jianyang Xue ◽  
Pengchun Hu ◽  
Fengliang Zhang ◽  
Yan Zhuge
Keyword(s):  
Seismic Behavior ◽  
Shake Table ◽  
Shake Table Tests

Comparison of Failure Modes of Piping Systems With Wall Thinning Subjected to In-Plane, Out-of-Plane, and Mixed Mode Bending Under Seismic Load: An Experimental Approach

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Izumi Nakamura ◽  
Akihito Otani ◽  
Masaki Shiratori
Keyword(s):  
Dynamic Behavior ◽  
Failure Modes ◽  
Seismic Load ◽  
Piping System ◽  
Shake Table ◽  
Shake Table Tests ◽  
Wall Thinning ◽  
Piping Systems ◽  
Out Of Plane ◽  
Plane Bending

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.

Shake table tests on prestressed concrete frames

1998 ◽  
Vol 31 (10) ◽  
pp. 676-682 ◽  
Author(s):  
Y. L. Mo ◽  
W. L. Hwang
Keyword(s):  
Prestressed Concrete ◽  
Shake Table ◽  
Concrete Frames ◽  
Shake Table Tests

Shake table tests and analytical simulations of a steel structure with shape memory alloy dampers

2014 ◽  
Vol 23 (12) ◽  
pp. 125002 ◽  
Author(s):  
Y M Parulekar ◽  
A Ravi Kiran ◽  
G R Reddy ◽  
R K Singh ◽  
K K Vaze
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Steel Structure ◽  
Shake Table ◽  
Shake Table Tests

scholarly journals Learning Damage Representations with Sequence-to-Sequence Models

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 452
Author(s):  
Qun Yang ◽  
Dejian Shen
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Research Community ◽  
Regions Of Interest ◽  
Data Driven ◽  
Economic Losses ◽  
Shake Table ◽  
Learning Models ◽  
Shake Table Tests ◽  
Damage States

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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