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.
Highlights
In the construction of heavily reinforced structural members one of the biggest problems encountered is the compaction of concrete
This paper demonstrates a nonparametric approach of effectively using regularized least square algorithm along with random kitchen sink algorithm to predict the fresh stage and hardened properties of self compacting concrete (SCC)
Literature review brings out earlier studies related to the prediction of concrete properties using artificial neural networks (ANN), fuzzy logic, support vector machines (SVM), design of experiments (DOE) etc
Summary
In the construction of heavily reinforced structural members one of the biggest problems encountered is the compaction of concrete. Improper compaction can lead to low quality and poor performance In such structures it is difficult to use mechanical vibrators or manual compaction methods and the solution is to develop a mix which does not need compaction. As it is very difficult to establish a general relation between the SCC properties and its ingredients a large number of trials (involving time, material and labour) are generally needed to get an SCC mix with required rheological and hardened properties This brings out the importance of modeling the fresh and hardened stage properties of SCC. The common trend in most of the studies that have been reported is to adopt analytical equation relating the required properties of SCC with its ingredients and optimizing this equation using regression analysis These methods are less efficient in the case of nonlinearly separable data (Chien et al 2010). RKS has been proved as one of such modeling method (Nair et al 2015)
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