Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine

Aiyer, Bhairevi G.; Kim, Dookie; Karingattikkal, Nithin; Samui, Pijush; Rao, P.R. Sampat

doi:10.1007/s12205-014-0524-0

Cited by 78 publications

(28 citation statements)

References 13 publications

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“…Statistical modeling has its limitations in estimating the underlying relationships between the inputs and outputs of forecasting models in more complicated cases (Zhang 1998). As a result, recent studies have shown an increasing trend toward the application of machine learning techniques in predicting concrete compressive strength (Topçu and Saridemir, 2007;Saridemir et al 2009;Atici 2011;Aiyer et al 2014;Akande et al 2014;Omran et al 2014). The results from these studies demonstrate a great potential of this approach, which warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Omran

Chen

Jin

2016

J. Comput. Civ. Eng.

124

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With its growing emphasis on sustainability, the construction industry is increasingly interested in environmentally friendly concrete produced using alternative and/or recycled waste materials. However, the wide application of such concrete is hindered by lack of understanding of the impacts of these materials on concrete properties. This research investigates and compares the performance of nine data mining models in predicting the compressive strength of a new type of concrete containing three alternative materials as fly ash, Haydite® lightweight aggregate, and Portland limestone cement. These models include three advanced predictive models (multilayer perceptron, support vector machines, and Gaussian processes regression), four regression tree models (M5P, REPTree, M5-Rules, and decision stump), and two ensemble methods (additive regression and bagging) with each of the seven individual models used as base classifier. The analytical results show that with appropriate parameter settings all of these models except for decision stump achieved acceptable prediction performance. The ensemble methods improved the prediction accuracy of the four regression tree models, but had less success on the other three advance predictive models. The individual Gaussian processes regression model as well as its related ensemble models reached the highest prediction accuracy in comparison groups. The results of this paper offer valuable insights to improving the use of these models for property prediction of concrete.

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Section: Introductionmentioning

confidence: 99%

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Omran

Chen

Jin

2016

J. Comput. Civ. Eng.

124

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“…However, Cheng et al [6] illustrated that this parameter setting method would result in a poor performance. Aiyer et al [29] determined the parameters of LSSVM by trial and error procedure. Since and 2 can have any positive real values, the trial and error procedure would be very time-consuming and often would not result in the best parameters.…”

Section: Coupled Simulated Annealing Based Leastmentioning

confidence: 99%

Comparison of artificial neural network and coupled simulated annealing based least square support vector regression models for prediction of compressive strength of high-performance concrete

Rad

Ayubirad²

2017

Scientia Iranica

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Abstract. High-Performance Concrete (HPC) is a complex composite material with highly nonlinear mechanical behavior. Concrete compressive strength, as one of the most essential qualities of concrete, is also a highly nonlinear function of ingredients. In this paper, Least Square Support Vector Regression (LSSVR) model based on Coupled Simulated Annealing (CSA) has been successfully used to nd the nonlinear relationship between the concrete compressive strength and eight input factors (the cement, the blast furnace slags, the y ashes, the water, the superplasticizer, the coarse aggregates, the ne aggregates, age of testing). To evaluate the performance of the CSA-LSSVR model, the results of the hybrid model were compared with those obtained by Arti cial Neural Network (ANN) model. A comparison study is made using the coe cient of determination R 2 and Root Mean Squared Error (RMSE) as evaluation criteria. The accuracy, the computational time, the advantages and shortcomings of these modeling methods are also discussed. The training and testing results have shown that ANNs and CSA-LSSVR models have strong potential for predicting the compressive strength of HPC.

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“…Another example of the prediction of SCC properties is the work of Aiyer et al (2014) who examined the capability of least squares support vector machines (LS-SVM) for predicting the compressive strength of self-compacting concrete. The results indicated that the LS-SVM model performed better than ANN.…”

Section: Applications In Modeling Self-compacting Concrete (Scc) Propmentioning

confidence: 99%

Support Vector Machines in Structural Engineering: A Review

Çevik

Kurtoğlu

Bilgehan

et al. 2015

Journal of Civil Engineering and Management

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Recent development in data processing systems had directed study and research of engineering towards the creation of intelligent systems to evolve models for a wide range of engineering problems. In this respect, several modeling techniques have been created to simulate various civil engineering systems. This study aims to review the studies on support vector machines (SVM) in structural engineering and investigate the usability of this machine learning based approach by providing three case studies focusing on structural engineering problems. Firstly, the concept of SVM is explained and then, the recent studies on the application of SVM in structural engineering are summarized and discussed. Next, we performed three case studies using the experimental studies provided. Applicability of SVM in structural engineering is confirmed by these case studies. The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model.

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Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine

Cited by 78 publications

References 13 publications

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

Comparison of artificial neural network and coupled simulated annealing based least square support vector regression models for prediction of compressive strength of high-performance concrete

Support Vector Machines in Structural Engineering: A Review

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