Literature review brings out earlier studies related to the prediction of concrete properties using ANN, fuzzy logic, support vector machines (SVM), design of experiments (DOE) etc. This generated model will be useful to obtain the properties of SCC mixes avoiding the physical performance of the test in laboratory and also reduce the wastage of material and time. Using the experimental data generated in the laboratory, the relationship between the engineering properties and mix proportions are generated. 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 SCC. Large numbers of trials needed to develop SCC mix with the desired properties will lead to wastage of materials and time. Compared to conventional concrete, the proportioning is complex and no theoretical relationships have been developed between the mixture proportioning and the measured fresh or hardened stage properties of SCC. 2015).ĮFNARC guidelines are available for the mix proportioning of SCC. RKS has been proved as one of such modeling method (Nair et al. Hence a modeling approach which is good for nonlinearly separable data with the advantage of limited data storage space and computation time requirement for analysis has greater utility. Tools like artificial neural networks (ANN), fuzzy logic etc., have been used to model non linearly separable data, but if the data size is big they need large space for storing the data and require lot of computational time. These methods are less efficient in the case of nonlinearly separable data (Chien et al. 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 then optimizing this equation using regression analysis. This brings out the importance of modeling the fresh and hardened stage properties of SCC. The mix proportioning of SCC is done in such a way that it satisfies the rheological and hardened properties.Īs 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.
The hardened stage properties of SCC include its compressive strength, split tensile strength etc. The above requirements of SCC can be measured using J ring test, slump flow test and V funnel test at 5 min respectively. The fresh stage characteristics of SCC include high passing ability to flow through congested reinforcements under its own weight, flowing ability to flow and fill the formwork under self weight and segregation resistance. This led to the introduction of self compacting concrete in the late 1980s by Nagamoto and Ozava ( 1999).
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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. Improper compaction can lead to low quality and poor performance. In the construction of heavily reinforced structural members one of the biggest problems encountered is the compaction of concrete. The model could accurately predict the properties of the SCC within the experimental domain. Accuracy of the model was checked by comparing the predicted and measured values. 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. Out of 40 test results, 32 results were used for training and 8 set results were used for testing the algorithm. Parametric variation in the SCC mixes were the quantities of fine and coarse aggregates, superplasticizer dosage, its family and water content. The database for testing and training the algorithm was prepared by conducting tests on 40 SCC mixes. 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 material and time requirement is more to conduct such large number of trials. 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. 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.