RETRACTED: Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies

Motamedi, Shervin; Shamshirband, Shahaboddin; Hashim, Roslan; Petković, Dalibor; Roy, Chandrabhushan

doi:10.1016/j.engstruct.2015.03.070

Cited by 38 publications

(18 citation statements)

References 40 publications

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“…Recently, many researchers have worked toward the development of kernel-based models in different areas of engineering-health monitoring [15], concrete research [9,10,16,17], hydrology [18], remote sensing [19,20] and fracture mechanics [21]. In this paper, kernel-based models which are widely used-support vector regression (SVR) [22], relevance vector machine (RVM) [23] and Gaussian process regression (GPR) [24,25]-are selected to predict cement compressive strength.…”

Section: Introductionmentioning

confidence: 99%

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Kernel-based models for prediction of cement compressive strength

Verma

Thirumalaiselvi

Rajasankar

2016

Neural Comput & Applic

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This paper employs three different kernel-based models-support vector regression (SVR), relevance vector machine (RVM) and Gaussian process regression (GPR)-for the prediction of cement compressive strength. The input variables for the model are taken as C 3 S (%), SO 3 (%), Alkali (%) and Blaine (cm 2 /g), while the output is 28-day cement compressive strength (N/mm 2 ) of the cement. The hyperparameters of the SVR are obtained using two different metaheuristic optimization algorithms-particle swarm optimization (PSO) and symbiotic organism search (SOS). Trial-and-error-based approach is used for arriving at the hyperparameters of RVM and GPR. The compressive strength predicted using different kernelbased models is also compared with that obtained from ANN and fuzzy logic models reported in the literature. The performance of the different kernel-based models is benchmarked using six different error indices and residual analysis. The performance of the kernel-based models is found to be at par with ANN. The better generalization capability and excellent empirical performance of the kernel-based models overcome the disadvantages associated with ANN and provide a good tool for the prediction of the cement compressive strength.

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Section: Introductionmentioning

confidence: 99%

“…The studies involved the use of mathematical modeling and regression analysis [6][7][8]. Some researchers explored artificial neural network (ANN) models to predict the compressive strength [9][10][11]. A model based on fuzzy logic was developed by Fa-Liang [12] for prediction of cement compressive strength.…”

Section: Introductionmentioning

confidence: 99%

Kernel-based models for prediction of cement compressive strength

Verma

Thirumalaiselvi

Rajasankar

2016

Neural Comput & Applic

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“…(22) (iii) The parameters k and λ of the coupled model need optimization using the mean square residual error (MSRE) as criterion. For each CEM type, different parameters are used, the set of (k BM , λ BM ) for CEM II B-M 32.5, and the set (k AL , λ AL ) for CEM II A-L 42.5.…”

Section: F I G 3 -Example Of Data Set For T D = 180 Daysmentioning

confidence: 99%

“…Gene expression programming (GEP) and neural networks were used by Baykasoglu et al 20 and Thamma et al 21 to predict the strength of Portland composite cement. Motamedi et al 22 predicted the compressive strength of cockle shell-cement-sand mixtures using and comparing the support vector regression (SVR) and adaptive -neuro-fuzzy inference (ANFIS) techniques. Firstly, the ANFIS network was used to find the parameters having a stronger impact on strength.…”

Section: Introductionmentioning

confidence: 99%

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

Tsamatsoulis¹

2016

Chem.Biochem.Eng.Q.

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The dynamic approach of two well-known techniques has been used to predict a cement's 28-day compressive strength: Multiple linear regression (MLR), and artificial neural networks (ANN). The modeling is based on Portland cement data and utilizes daily physical, chemical analyses, and early strength results at days 1 and 7. Two kinds of models have been built, containing the 1-day strength as an independent variable, or both 1-and 7-day strength. The models are dynamic because they are applied to a movable past period of T D days to calculate the parameters, and then used for a future period of T F days. The comparison is based on the residual error of the testing period, and T D , T F have been optimized. Eight ANNs of different complexity have been developed, but some of them are suffering from over-fitting. A third model has also been created with the coupling of the initial two. The time parameters as well as the filtering and weighting coefficients of the coupled model have been optimized. The simple ANNs with one node in the hidden layer, sigmoid or hyperbolic functions and bias, show better performance. The combination of the coupled model with these two best ANN techniques provides an improved prediction of 28-day strength compared with the initial model containing the 1-day strength. The sensitivity also of T F parameter is lower providing certain benefit in daily industrial application. The implementation of these methods in cement process control can contribute to quality improvement by maintaining a low variance of typical strength.

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“…Previous studies also showed that shell husk is a good source of calcium oxide (CaO) and calcium carbonate (CaCO3), which provides the opportunity to reinforce the soil or bind the material construction (Park, 2014;Motamedi et al, 2015). The utilisation of shell husk as recycled material has the aim of resolving several problems such as preservation of limited natural resources, saving disposal costs and environmental conservation.…”

Section: Introductionmentioning

confidence: 99%

Ground improvement using waste shell for farm roads and embankments

Rachmawati

Hossain

Shiau

2018

J Agricult Engineer

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The bearing capacity of subgrade soil plays an important role in the performance of agricultural earth structures such as farm roads or earth embankments. It also has a strong link to the budgeting efficiency of those agricultural earth structural projects. One way to improve the subgrade of soil is by using stabilised material in the subgrade layer. Shell husk waste is one of the construction materials that can be used as a substitute for the subgrade layer. The utilisation of shell husk waste has several purposes such as protecting natural aggregate and preventing problems that arise from the untreated abandoned shell husk waste. In this research, shell husk waste was used as farm road subgrade material. To understand the bearing capacity of soil specimens, a series of California bearing ratio (CBR) tests were carried out by using several types of subgrade layers containing different percentages of shell husk in the soil-shell husk mixture. Three types of subgrade layers were used: subgrade upper layer, subgrade bottom layer, and subgrade double layer, containing 0 (control), 10, 20, and 30% shell husk in soil-shell husk mixture. The experimental results showed that the addition of shell husk in soil leads to improvement in the CBR values of the ground as compared to control specimens. It was further revealed that by increasing the percentage of shell husk in the soil-shell husk mixture, the CBR value was improved significantly. The highest CBR value was achieved by using subgrade upper layer with a 30% shell husk in soil-shell husk mixture. The results obtained are encouraging with regard to the utilisation of the waste shell husks for improving the bearing capacity of agricultural earth structures such as farm roads and earth embankments.

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RETRACTED: Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies

Cited by 38 publications

References 40 publications

Kernel-based models for prediction of cement compressive strength

Kernel-based models for prediction of cement compressive strength

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

Ground improvement using waste shell for farm roads and embankments

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