scholarly journals High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines

2017 ◽  
Vol 07 (01) ◽  
pp. 68-75 ◽  
Author(s):  
Rguig Mustapha ◽  
EL Aroussi Mohamed
Keyword(s):  
Compressive Strength ◽  
Support Vector Machines ◽  
High Performance ◽  
High Performance Concrete ◽  
Strength Prediction ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Vector Machines

Applying Support Vector Machines in Rebound Hammer Test

2013 ◽  
Vol 853 ◽  
pp. 600-604 ◽  
Author(s):  
Yu Ren Wang ◽  
Wen Ten Kuo ◽  
Shian Shien Lu ◽  
Yi Fan Shih ◽  
Shih Shian Wei
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Support Vector Machines ◽  
Prediction Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Rebound Hammer ◽  
Vector Machines

There are several nondestructive testing techniques available to test the compressive strength of the concrete and the Rebound Hammer Test is among one of the fast and economical methods. Nevertheless, it is found that the prediction results from Rebound Hammer Test are not satisfying (over 20% mean absolute percentage error). In view of this, this research intends to develop a concrete compressive strength prediction model for the SilverSchmidt test hammer, using data collected from 838 lab tests. The Q-values yield from the concrete test hammer SilverSchmidt is set as the input variable and the concrete compressive strength is set as the output variable for the prediction model. For the non-linear relationships, artificial intelligence technique, Support Vector Machines (SVMs), are adopted to develop the prediction models. The results show that the mean absolute percentage errors for SVMs prediction model, 6.76%, improves a lot when comparing to SilverSchmidt predictions. It is recommended that the artificial intelligence prediction models can be applied in the SilverSchmidt tests to improve the prediction accuracy.

scholarly journals INPUTING STUDENTS’ SCORE BASED ON GIST FEATURES, SUPPORT VECTOR MACHINES AND TESSERACT

2020 ◽  
Vol 1 (41) ◽  
pp. 77-85
Author(s):  
Hau Hung Nguyen
Keyword(s):  
Support Vector Machines ◽  
High Performance ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Support Vector ◽  
Vocational College ◽  
Vector Machines ◽  
Handwritten Digit

Handwriting recogination plays an important role in data inputing and processing in the practice. This attracts much attention of many researchers in different fields. In this paper, a new algorithm is proposed by basing on GIST features, Support Vector Machines (SVM) and Tesseract for entering the score on students’ transcript form at Soc Trang Vocational College. The algorithm consists of two main works, i.e., recognizing students’code and recogziing handwritten digit. In the proposed algorithm, all regions of interest are determined and extract their dictint features with using tesseract and GIST. Then, these features are classified by SVM mechanism. Experimental results demonstrated that the proposed algorithm obtained high performance with accuracy up to 96,57% for students’ code and 93,55% for Handwritting scores. Average time was 7,9s per one transcript.

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