Development and preliminary application of a plate loading model test system considering stress state

Measurement ◽  
2021 ◽  
pp. 109507
Author(s):  
Changqi Zhu ◽  
Xing Wang ◽  
Mingjian Hu ◽  
Xinzhi Wang ◽  
Jianhua Shen ◽  
...  
IACGE 2018 ◽  
2019 ◽  
Author(s):  
Lei Gao ◽  
Han Long Liu ◽  
Xiangjuan Yu ◽  
Huidong Chen ◽  
Ali H. Mahfouz

2020 ◽  
Vol 101 ◽  
pp. 103404 ◽  
Author(s):  
Liping Li ◽  
Shangqu Sun ◽  
Jing Wang ◽  
Shuguang Song ◽  
Zhongdong Fang ◽  
...  
Keyword(s):  

2011 ◽  
Vol 243-249 ◽  
pp. 2915-2919
Author(s):  
Jian Bin Hao ◽  
Yu Ming Men

Gradually, anchor is in the working state of tension, but its stress becomes rather complicated when influenced by some external factors such as water, ground loads, etc. Aiming at the problems that ground additional load or coupling action of water and the additional load are forced on soil slope, the axial strain distributions of anchor tendons were obtained by model test. The change rules of axial strain at anchor head, near to slip plane, and end of the anchorage were analyzed respectively. The results show that under ground loads, the strain of anchor is biggest at the anchor head, and it gradually decreases from outer to inner. Under combined water and ground load, the stress state of the anchor system changes from tension to bending, and the upper anchors are most urgently affected. The experimental results can supply a reference for design and research of soil anchor with similar conditions.


2017 ◽  
Author(s):  
Alexander C. Reis ◽  
Howard M. Salis

ABSTRACTGene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.


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