Application of support vector regression for the prediction of concrete strength

Lee, Jong-Jae; Kim, Doo Kie; Chang, Seongkyu; Lee, Jang‐Ho

doi:10.12989/cac.2007.4.4.299

Cited by 22 publications

(11 citation statements)

References 9 publications

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“…The problem of finding w and b to reduce the empirical risk with respect to an ε-insensitive loss function is equivalent to the convex optimization problem that minimizes the margin (w) and slack variables ( , ) as: subject to (4) where, the first term ( ) is the margin and is shown in detail in Lee et al (2007); the parameter C is a positive constant. To solve the above optimization problem, one has to find a saddle point of the Lagrange function described as (5) where, , α i , and are Lagrange multipliers.…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…Recently, the Support Vector Machine (SVM) has been proposed for pattern recognition, such as text classification and image recognition (Vapnik, 1995;Ye et al, 2005), and was extended to regression analysis for various applications (Mukherjee et al, 1997;Muller et al, 1997;Samui, 2000;Mita and Hagiwara, 2003;Yu et al, 2006;Zhang et al, 2006;Lee et al, 2007;Samui et al, 2008). In the present study, the support vector machine for regression (support vector regression, SVR) is applied for predicting the stability number of armor blocks of breakwaters.…”

Section: Introductionmentioning

confidence: 99%

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Stability number prediction for breakwater armor blocks using Support Vector Regression

Kim

Chang

et al. 2011

KSCE J Civ Eng

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This paper presents the Support Vector Regression (SVR) to predict the stability number of armor blocks of breakwaters. The experimental data of van der Meer are used as the training and test data for the SVR in this study. Estimated results of SVR are compared with those of the empirical formula and a previous Artificial Neural Network (ANN) model. The comparison of results shows the efficiency of the proposed method in the prediction of the stability numbers. The proposed method proves to be an effective tool for designers of rubble mound breakwaters to support their decision process and to improve design efficiency.

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Section: Support Vector Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability number prediction for breakwater armor blocks using Support Vector Regression

Kim

Chang

et al. 2011

KSCE J Civ Eng

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“…SVMs have been recently applied in civil engineering. Lee et al successfully estimated the concrete strength using the SVR algorithm [30]. Chen et al predicted the exposed temperature for concrete exposed to fire with SVM.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Tanyıldızı

2018

Advances in Civil Engineering

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e artificial neural network and support vector machine were used to estimate the compressive strength and flexural strength of carbon fiber-reinforced lightweight concrete with the silica fume exposed to the high temperature. Cement was replaced with three percentages of silica fumes (0%, 10%, and 20%). e carbon fibers were used in four different proportions (0, 2, 4, and 8 kg/m 3 ). e specimens of each concrete mixture were heated at 20°C, 400°C, 600°C, and 800°C. After this process, the specimens were subjected to the strength tests. e amount of cement, the amount of silica fumes, the amount of carbon fiber, the amount of aggregates, and temperature were selected as the input variables for the prediction models. e compressive and flexural strengths of the lightweight concrete were determined as the output variables. e model results were compared with the experimental results. e best results were achieved from the artificial neural network model. e accuracy of the artificial neural network model was found at 99.02% and 96.80%.

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“…Ranković et al [34] established a nonlinear autoregressive SVR model that yields accurate results for the prediction of the tangential displacement of a concrete dam. Lee et al [35] successfully predicted the strength of concrete based on its mix proportions using the SVR technique and NNs, as indicated by a comparison against experimental results, and concluded that the SVR method can be used to predict the compressive strength of concrete with higher estimation accuracy and within a shorter computation time compared with the NN method. Su et al [36] developed a time-varying identification model for dam behavior before and after structural reinforcement based on the SVR method, and this model was found to yield more accurate fitted and forecasted results compared with classical statistical models.…”

Section: Introductionmentioning

confidence: 99%

Time‐Varying Identification Model for Crack Monitoring Data from Concrete Dams Based on Support Vector Regression and the Bayesian Framework

Chen

Zhong-ru

Liang

et al. 2017

Mathematical Problems in Engineering

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The modeling of cracks and identification of dam behavior changes are difficult issues in dam health monitoring research. In this paper, a time-varying identification model for crack monitoring data is built using support vector regression (SVR) and the Bayesian evidence framework (BEF). First, the SVR method is adopted for better modeling of the nonlinear relationship between the crack opening displacement (COD) and its influencing factors. Second, the BEF approach is applied to determine the optimal SVR modeling parameters, including the penalty coefficient, the loss coefficient, and the width coefficient of the radial kernel function, under the principle that the prediction errors between the monitored and the model forecasted values are as small as possible. Then, considering the predicted COD, the historical maximum COD, and the time-dependent component, forewarning criteria are proposed for identifying the time-varying behavior of cracks and the degree of abnormality of dam health. Finally, an example of modeling and forewarning analysis is presented using two monitoring subsequences from a real structural crack in the Chencun concrete arch-gravity dam. The findings indicate that the proposed time-varying model can provide predicted results that are more accurately nonlinearity fitted and is suitable for use in evaluating the behavior of cracks in dams.

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Application of support vector regression for the prediction of concrete strength

Cited by 22 publications

References 9 publications

Stability number prediction for breakwater armor blocks using Support Vector Regression

Stability number prediction for breakwater armor blocks using Support Vector Regression

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Time‐Varying Identification Model for Crack Monitoring Data from Concrete Dams Based on Support Vector Regression and the Bayesian Framework

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