Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns

Support vector machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. A SVM model is presented to predict compressive strength of concrete at 28 days in this paper. A total of 20 data sets were used to train, whereas the remaining 10 data sets were used to test the created model. Radial basis function based on support vector machines was used to model the compressive strength and results were compared with a generalized regression neural network approach. The results of this study showed that the SVM approach has the potential to be a practical tool for predicting compressive strength of concrete at 28 days.

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A Self-Adaptive Fuzzy Inference Model Based on Least Squares SVM for Estimating Compressive Strength of Rubberized Concrete

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622016500140 ◽

2016 ◽

Vol 15 (03) ◽

pp. 603-619 ◽

Cited By ~ 4

Author(s):

Min-Yuan Cheng ◽

Nhat-Duc Hoang

Keyword(s):

Compressive Strength ◽

Support Vector Machines ◽

Least Squares ◽

Fuzzy Inference ◽

Support Vector ◽

Rubberized Concrete ◽

Inference Model ◽

Adaptive Fuzzy ◽

Vector Machines ◽

Self Adaptive

This paper presents an AI approach named as self-Adaptive fuzzy least squares support vector machines inference model (SFLSIM) for predicting compressive strength of rubberized concrete. The SFLSIM consists of a fuzzification process for converting crisp input data into membership grades and an inference engine which is constructed based on least squares support vector machines (LS-SVM). Moreover, the proposed inference model integrates differential evolution (DE) to adaptively search for the most appropriate profiles of fuzzy membership functions (MFs) as well as the LS-SVM’s tuning parameters. In this study, 70 concrete mix samples are utilized to train and test the SFLSIM. According to experimental results, the SFLSIM can achieve a comparatively low MAPE which is less than 2%.

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A data mining approach for Jet Grouting Uniaxial Compressive Strength prediction

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) ◽

10.1109/nabic.2009.5393401 ◽

2009 ◽

Cited By ~ 5

Author(s):

Joaquim Tinoco ◽

Antonio Gomes Correia ◽

Paulo Cortez

Keyword(s):

Data Mining ◽

Compressive Strength ◽

Uniaxial Compressive Strength ◽

Strength Prediction ◽

Data Mining Approach ◽

Jet Grouting

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Applying Support Vector Machines in Rebound Hammer Test

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.853.600 ◽

2013 ◽

Vol 853 ◽

pp. 600-604 ◽

Cited By ~ 3

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.

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