Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm

Abstract

High performance concrete especially self compacting concrete (SCC) has got wide popularity in construction industry because of its ability to flow through congested reinforcement without segregation and bleeding. Even though European Federation of National Associations Representing for Concrete (EFNARC) guidelines are available for the mix design of SCC, large number of trials are required for obtaining an SCC mix with the desired engineering properties. The material and time requirement is more to conduct such large number of trials. The main objective of the study presented in this paper is to demonstrate use of regularized least square algorithm (RLS) along with random kitchen sink algorithm (RKS) to effectively predict the fresh and hardened stage properties of SCC. The database for testing and training the algorithm was prepared by conducting tests on 40 SCC mixes. Parametric variation in the SCC mixes were the quantities of fine and coarse aggregates, superplasticizer dosage, its family and water content. Out of 40 test results, 32 results were used for training and 8 set results were used for testing the algorithm. Modelling of both fresh state properties viz., flowing ability (Slump Flow), passing ability (J Ring), segregation resistance (V funnel at 5 min) as well as hardened stage property (compressive strength) of the SCC mix was carried out using RLS and RKS algorithm. Accuracy of the model was checked by comparing the predicted and measured values. The model could accurately predict the properties of the SCC within the experimental domain.

Prediction of Compressive Strengths for Rice Husks Ash incorporated concrete, Using Neural Network and Reviews

“…Sathyan et al [2] used random kitchen sink algorithm and regularized least square to predict concrete rheological and hardened properties, with 32 data sets (80%) used for training and 8 datasets (20%) for testing. Revathy et al [3] used various types of network functions, one of the types using algorithm BFGS quasi-Newton back propagation, indicated using 34 datasets for training, 8 for validation and 8 for testing.…”

“…Chandwani et al [1] study on ready mix concrete modeling, the artificial neural network (ANN) model showed promising results in comparison to first-order and second-order regression techniques, for modeling unknown complex relationship exhibited by design mix proportion and concrete slump. Sathyan et al [2] used random kitchen sink algorithm and regularized least square to predict concrete rheological and hardened properties, results indicated RMSEs and mean absolute errors(MSEs) of less than 0.05. According to Revathy et al [3] the MAPE represent model performance and RMSE -Equation (3)-represent the error between the experimental and predicted results.…”

“…According to Revathy et al [3] the MAPE represent model performance and RMSE -Equation (3)-represent the error between the experimental and predicted results. Sathyan et al [2] study on prediction of concrete rheological and hardened properties, input parameters enclosed weights of ingredients including cement, coarse aggregates, fine aggregates, four superplasticizers dosage and water. Islam et al [4], ANOVA analysis indicated water to binder ratio and contents of:cement, fine aggregate content, coarse aggregate, rice husk ash and superplasticizer, had direct effect on strength of RHA incorporated high performance concrete.…”

Prediction of Compressive Strengths of Concrete with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

“…Zhang et al [25] examined the deviation between predicted and experimental values using RMSE, mean absolute percentage error (MAPE) and absolute factor of variance (R 2 ), the equations (3) and (4). Chandwani et al [26] used six different statistical performance for evaluation that included mean absolute error (MAE) and mean absolute percentage error (MAPE) as shown in equations ( 5) and (6). Ngandu [10]…”

“…Revathy et al [5] carried out a study, where mean absolute percentage error (MAPE) represented the model performance and root mean square error (RMSE) represented the error between experimental and predicted results, for neural network models generated to predict compressive strength and fresh properties of flowable concrete, using MATLAB; they used 34 data set for training, 8 for validation and 8 for testing, using BFGS quasi-newton back propagation training algorithm for neural network. Sathyan et al [6] used random kitchen sink algorithm and regularized least square algorithm; the two applications come together in the grand unified regularized least square (GURLS) tool bar in MATLAB; the training data had 32 datasets and to measure model accuracy 8 test dataset were used; hardened stages of self-compacting concrete were used for modeling. Algorithms of HIV/AIDS model were developed under free GNU octave (5.1.0 version); results indicated the usefulness of computing systems like GNU octave as indicated by Campos et al [34].…”