Modelling the fresh properties of self-compacting concrete using support vector machine approach

Sonebi, Mohammed; Çevik, Abdülkadir; Grünewald, S.; Walraven, J.C.

doi:10.1016/j.conbuildmat.2015.12.035

Cited by 87 publications

(42 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the last decades, usage of artificial intelligence techniques for estimating and modeling a wide range of issues especially in civil engineering because of their advantages [14][15]. The use of artificial intelligence techniques, such as artificial neural network (ANN) [16], adaptive neurofuzzy inference system (ANFIS) [17][18], genetic programming (GP) [19][20][21][22], and support vector machines (SVMs) [23], to model the compressive behavior of concrete has received significant attention. The previous studies and experiences of the researchers have indicated that in addition to different experimental research works, using the various artificial intelligence approaches in evaluating and forecasting the fresh and hardened properties of the concrete has become a importance [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Azizifar

Babajanzadeh

2018

CivileJournal

View full text Add to dashboard Cite

This paper investigates the capability of utilizing Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programing (GEP) methods to estimate the compressive strength of self-compacting concrete (SCC) incorporating Silica Fume (SF) as a supplementary cementitious materials. In this regards, a large experimental test database was assembled from several published literature, and it was applied to train and test the two models proposed in this paper using the mentioned artificial intelligence techniques. The data used in the proposed models are arranged in a format of seven input parameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and one output. To indicate the usefulness of the proposed techniques statistical criteria are checked out. The results testing datasets are compared to experimental results and their comparisons demonstrate that the MARS (R2=0.98 and RMSE= 3.659) and GEP (R2=0.83 and RMSE= 10.362) approaches have a strong potential to predict compressive strength of SCC incorporating silica fume with great precision. Performed sensitivity analysis to assign effective parameters on compressive strength indicates that age of specimen is the most effective variable in the mixture.

show abstract

Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Azizifar

Babajanzadeh

2018

CivileJournal

View full text Add to dashboard Cite

show abstract

“…These techniques include GS, RS, and PSO [38]. The techniques were chosen because they have been frequently used as the optimization tools in machine learning and have provided good solutions in searching the hyperparameters [36,52,53]. This makes these optimization techniques good references.…”

Section: Performance Criteriamentioning

confidence: 99%

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

Hu²,

Liu

2020

SN Appl. Sci.

View full text Add to dashboard Cite

“…On the other hand, a pattern search (also known as a line search or a compass search) starts from the center of the search area and tries steps in both directions for each parameter. The center of search area is then moved to the new point if a better model fit is obtained (Jain and Bhatia 2013;Sonebi et al 2016). The process is repeated until the specified tolerance rate is reached.…”

Section: Svm Modelmentioning

confidence: 99%

Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran

Barzegar

Sattarpour

Nikudel

et al. 2016

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

Uniaxial compressive strength (UCS) is one of the most widely used and important rock mechanical parameters in rock engineering. The main objective of the present study was to evaluate the ability of artificial intelligence models including multi-layer perceptron (MLP), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) to predict the UCS of travertine rocks in the Azarshahr area (NW Iran). To attempt this objective, 85 core samples of travertine rock were collected from the study area and the laboratory tests were performed to determine the P-wave velocity [V p (km/ s)], porosity (n %), Schmidt rebound hardness (R n) and UCS of the rocks at the Rock Mechanics Laboratory in the Tarbiat Modares University. The data set including V p (km/s), n % and R n as the inputs and UCS as the output were divided into training (80 % of dataset) and testing (20 % of dataset) subsets to construct the models. The coefficient of determination (R 2), root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the models performance. The models accuracy followed the order SVM [ ANFIS [ SFL [ MLP [ MFL. The SVM model with RBF kernel function yielded the highest R 2 (0.9516), and the lowest RMSE (2.14 MPa) and MAE (1.351 MPa) in the testing step. Accuracy results indicated that SVM model predictions were better than MLP, SFL, MFL and ANFIS models for prediction of UCS of travertine rocks.

show abstract

Modelling the fresh properties of self-compacting concrete using support vector machine approach

Cited by 87 publications

References 19 publications

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran

Contact Info

Product

Resources

About