Using a Simple Soil Spring Model and Support Vector Machine to Determine Bridge Scour Depth and Bridge Safety

2016 ◽  
Vol 30 (4) ◽  
pp. 04015088 ◽  
Author(s):  
Chung-Wei Feng ◽  
Shen-Haw Ju ◽  
Hsun-Yi Huang
2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaozhong Zhang ◽  
Wenjuan Yao ◽  
Yimin Liu ◽  
Bo Chen

A new damage identification method for bridge scour was proposed, in the case that it was difficult to detect bridge scour depth applying testing equipment. Through integrative application of the eigensystem realization algorithm (ERA) and method of support vector machine (SVM), this method was used to identify the scour depths of bridge under conditions of ambient excitation. The following three steps are necessary for the application of this method to identify bridge scour. Firstly, a sample library about scour depth and upper structure vibration response was established using numerical methods and support vector machine method. Secondly, free response signal of bridge were extracted from random vibration signal of bridge upper structure using random decrement technique. Thirdly, based on above two steps, the bridge scour depth was identified using ERA and SVM. In the process of applying the method to identify bridge scour depth, the key is to find the sensitive points for scour depth of substructure using sample library and to gather the vibration response signal of accelerated velocity under conditions of ambient excitation. It was identified that the method has higher recognition accuracy and better robustness through experiments on a real bridge. The method provided a new way for identifying scour depth of bridges.


2019 ◽  
Vol 23 (6) ◽  
pp. 2503-2513 ◽  
Author(s):  
Abbas Parsaie ◽  
Amir Hamzeh Haghiabi ◽  
Amir Moradinejad

2016 ◽  
Vol 84 (3) ◽  
pp. 2145-2162 ◽  
Author(s):  
Hassan Sharafi ◽  
Isa Ebtehaj ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.


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