Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm
International Journal of Concrete Structures and Materials
Modeling the Fresh and Hardened Stage Properties of Self- Compacting Concrete using Random Kitchen Sink Algorithm
Kalpathy Balakrishnan Anand
Aravind Jaya Prakash
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.
self compacting concrete; rheological properties of SCC; hardened properties of SCC; regularized least squares; random kitchen sink
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. This led to the introduction of self
compacting concrete in the late 1980s by
Nagamoto and Ozava
. 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. The
above requirements of SCC can be measured using J ring
test, slump flow test and V funnel test at 5 min respectively.
The hardened stage properties of SCC include its
compressive strength, split tensile strength etc. The mix
proportioning of SCC is done in such a way that it satisfies the
rheological and hardened properties.
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 then
optimizing this equation using regression analysis. These
methods are less efficient in the case of nonlinearly separable
(Chien et al. 2010)
. 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. 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. RKS has been proved as one of such
(Nair et al. 2015)
2. Background Literature
EFNARC guidelines are available for the mix proportioning
of SCC. 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. Large
numbers of trials needed to develop SCC mix with the desired
properties will lead to wastage of materials and time. 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. Using the experimental data generated in
the laboratory, the relationship between the engineering
properties and mix proportions are generated. 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.
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. Highlights of some of the recent works related to
the prediction are included in this section.
Using fuzzy logic and ANN, the strength of high strength
concrete was predicted by Gokmen
Tayfur et al. (2014)
comparative study between the two models was also done. To
compare between the models their root mean square error
(RMSE), mean relative error (MRE) and mean absolute error
(MAE) was found out. Results showed lesser errors for fuzzy
logic compared to ANN for the training data set. Testing data
error is less for ANN which shows the better prediction
capabilities of ANN over fuzzy logic. The insignificant values
of error showed that both the models have good extrapolation
capabilities. The strength of concrete was analyzed by
using neural networks and design of experiments
(DOE). The effect on the late and early compressive strength
when fly ash is used as cement replacement in the range of
0–50% was studied. Water to cementitious material ratio was
also varied in the range of 0.3–0.7. The effect of each
parameter on the compressive strength was studied. The data
collected from DOE were used to develop a high correlation
between the compressive strength and component properties.
Metaheuristic optimized least squares support vector
regression (LSSVR) was used by
Pham et al. (2015)
to predict the
compressive strength of high performance concrete. Least
squares support vector regression is an advanced artificial
intelligence method that can be effectively used for nonlinear
modeling. Using ANN and SVM as bench marks the
performance of the model was verified. The model performance was
measured using RMSE and mean absolute percentage error
(MAPE). LSSVR achieved the lowest MAPE and RMSE
followed by SVM and ANN. The fracture characteristics of
high strength and ultra high strength concrete were modeled
using support vector regression (SVR) by
Yuvaraj et al.
. Using coefficient of determination (r2) the accuracy of
the model was determined. The value of r2 for all the outputs
were found to be closer to 1 indicating that the model has good
Yan and Shi (2010)
predicted the elastic
modulus of normal and high strength concrete by SVM. A
comparison was done between SVM and other methods
including ANN, fuzzy logic, regression, equations of ACI 363
code etc. The MAPE of the model were 3.13 and 8.42% for
high and normal strength concrete in training while it was 3.75
and 9.69% for test data which is better compared to the other
models. Other works in the area of modeling of hardened
properties of mortar and concrete like compressive strength,
flexural strength and torsional strength using different soft
computing methods are also reported
(Yeh 1998; Lee 2003;
Tang 2006; Hossain et al. 2006; Tang et al. 2007; Pala et al.
2007; Topcu and Saridemir 2008; Altun et al. 2008; Prasad
et al. 2009; Khatibinia et al. 2016)
Mohebbi et al. (2011)
proposed an ANN model on the effect
chemical and mineral admixtures on the flow properties of self
consolidating cement paste based on 200 training data. The 14
input parameters were the water binder ratio, 4 type mineral
admixture quantity, 5 different superplasticizer quantity and 4
viscosity modifying admixture quantity. Mini slump spread
diameter and flow cone time were the output parameters. The
developed model could predict the optimum quantity of
admixtures which have strong influence on the rheological
properties of the cement paste. Modeling of the fresh stage
properties of concrete using ANN and other soft computing
methods are also reported in other studies
2007, 2008a, 2008b; Ghafari et al. 2015)
Few of the important works related to modeling of fresh as
well as hardened stage properties of SCC using ANN and
SVM methods are reviewed in this section. Fresh stage
properties of SCC mixtures with different dosages of water,
superplasticizer and coarse aggregate were measured and
Sonebi et al. (2016)
using support vector
machine approach. Properties of SCC viz., slump flow, flow
time (T50 and T60), V funnel, orimet time and blocking ratio
(L box) were predicted using the radial basis function and
polynomial kernel. The study showed that RBF kernel was
more accurate than polynomial kernel based support vector
machines. Sensitivity analysis was also done to check the
effects of mixture constituents (including water content,
weight of cement and limestone powder, volumes of
aggregate, amount of superplasticizer) as well as testing time
on the predicted values. The result showed that SVM RBF
Model can predict the fresh stage properties of SCC with
Sonebi et al. (2016)
modeled the fresh stage
properties of SCC using ANN also. In this SCC mix
composition are given as the input parameter and 6 parameters
like blocking ratio (L-box), slump flow, T50, T60, orimet flow
time, V-funnel flow time were the output parameters. Check
on the accuracy of the model to predict the fresh stage
properties of the SCC indicated that ANN model could
accurately predict the flow properties. The rheological and
mechanical performances of SCC mixtures were predicted
Nehdi et al. (2001)
using ANN, which is a non parametric
approach. The network was trained based on relationships
between the proportions of SCC and its performance using
existing data available in published literature. The
rheological properties of SCC like slump flow, filling capacity and
segregation along with compressive strength were modeled
using ANN. The feed forward back propagation algorithm
was adopted in the study. The model developed had good
predictive capability. Few more studies are reported related
to modeling of fresh and hardened stage properties of SCC
(Pathak et al. 2012; Raheman and Modani 2013; Douma
et al. 2014; Malagavelli and Manalel 2014)
Nair et al. (2015)
did a spread sheet implementation for
classification of data using random kitchen sink algorithm
(RKS). Experiments were performed on linear and
non-linear data sets using RKS and regularized least square
estimation techniques. Classification has been done in an
efficient manner by inheriting concepts from linear algebra
and optimization theory. Data was mapped into a higher
dimension using RKS and weight matrix for optimizing the
data generated using regularized least square (RLS). The
accuracy levels attained were 100% in case of linear data and
98% for nonlinear data. Preliminary studies have been made
to predict the passing ability of the SCC mix using RKS by
Prakash et al. (2016)
3. Approach to Modeling and Prediction
3.1 Random Kitchen Sink Algorithm (RKS)
Random kitchen sink algorithm is used for mapping
nonlinearly separated data sets. It is suitable for classifying
very large data sets and follows a non linear kernel method.
In machine learning many methods like SVM, neural
networks, fuzzy logic and gaussian mixture model based
classifiers are popular out of which SVM is the most popular. Its
working is based on the principle of finding a hyper plane
that gives the maximum margin between the classes of the
given data sets. It is not suitable for large data sets as the
kernel matrix is of higher order resulting in large processing
time. This problem was overcome by
Rahimi and Recht
by using random feature mapping. The storage space
and processing time requirement is independent of the
number of data points. An explicit feature mapping is done
/ðxÞ corresponding to radial basis function kernel (RBF).
The kernel function is symmetric and a real gaussian
function. The kernel function is taken as the inner product of the
kðp; qÞ ¼ \/ð pÞ; /ðqÞ [
Where k (p, q) is a positive definite function and /ðpÞ, /ðqÞ
are the mapping functions.
66 ejptX2 77 66 ejptX2 77
66 ejptX3 77 66 ejptX3 77
/ðpÞ ¼ p1ffiLffiffi 4666666 :::: 5777777 and /ðqÞ ¼ p1ffiLffiffi 6466666 :::: 5777777
To avoid complex calculations the equations can be
converted into their sine and cosine functions.
3.2 Regularised Least Square Algorithm (RLS)
Regularized least square algorithm is used for
classification of data. It trains a classifier and obtains accuracies
which determine the efficiencies with which different data
sets are classified. It is mainly used in the case of over
determined systems. Regularization is a technique used to
capture the real trend of data by use of functions and avoids
Nair et al. (2015)
studied the use of regularized
least square algorithm for multi class learning. If data set
contain l class of objects, the total number of features is x
and total number of objects is y. The data matrix is of size
y x and matrix A is of size y l which holds the label
vectors. Matrix B of size x l map x tupil data vectors
corresponding to label vector. f ðBÞ is the objective function.
¼ arVgymxin jjA
YBjj22 þ kjjBjj22
f ðBÞ ¼ jjA
YBjj22 þ kjjBjj22
f ðBÞ ¼ TrðAT A þ BT Y T YB
þ Tr kBT B
¼ 0 ) B ¼ Y T Y þ kI
1Y T A
Tr BT Y T A
The application of regularized least square algorithm and
random kitchen sink algorithm comes together in a tool bar in
MATLAB called GURLS (Grand Unified Regularized Least
Square). It is a tool bar developed for supervised learning
based on regularized least square algorithm. The tool box
provides a set of basic functionalities which includes various
training strategies and routines to handle computations with
very large matrices by means of both memory mapped storage
and distributed task execution. It consists of a set of tasks each
belonging to a predefined category and a method called
GURLS core, implemented through the GURLS routine that is
responsible for processing the task pipe line.
The data derived from the laboratory testing are first
normalized by norm method and given as the input. By
checking the size of the normalized data matrix, a random
matrix will be generated. Size of the random matrix is
dependent only on the feature size and not the data points.
Being an explicit feature mapping, the number of columns
(feature) in the random matrix can be controlled. RKS maps
the input data matrix to higher dimension by multiplying it
with the generated random matrix. Training of the algorithm
will be carried out using the newly generated higher
dimension input data matrix and an optimized relation
between the output and input variables will be generated
using RLS. Prediction of output data for familiar and
unfamiliar mixes will be done using this optimized relation. Flow
chart of the work flow consisting of 6 stages is shown in
Fig. 1a. Figure 1b shows the details of processing stages 3–6
which is part of GURLS in MATLAB.
4. Experimental Details
The data sets used for training and testing was obtained
experimentally. SCC was tested for its rheological and
hardened stage properties. The rheological properties of
SCC like flowability, passing ability and segregation
resistance were tested. For hardened properties SCC was tested
for its compressive strength. As the mixing process affect the
properties of the concrete, the mixing sequence and time of
mixing were kept the same throughout the preparation of the
training and testing data. The materials were mixed using a
tilting drum mixer. Initially dry mixing of the materials
(cement, fine aggregate and coarse aggregate) was done for
3 min and then 70% of the mixing water is added to the
mixer and mixing is done for 2 min. Superplasticizer along
with remaining (30%) water is added after that and mixed for
The materials which were used for the preparation of the
SCC mix were flyash based portland pozzolana cement
(PPC), coarse aggregate (CA), fine aggregate (FA),
superplasticizer (SP) and water. The cement used was tested for its
consistency, specific gravity, fineness and compressive
strength according to IS 4031 (Part 1-2005a, Part 11-2005b,
Part 4-2005c, Part 6-2005d). The obtained values are 37%,
2.9, 2.95 and 35 N/mm2 respectively which were compared
with the specifications given in IS 1489 (2005) and it
satisfied the requirements.
Preliminary test were done on both coarse aggregate
(maximum size of 12.5 mm) and fine aggregate according to
IS 2386 (Part I-2002a, Part III-2002b). The aggregate were
tested for their specific gravity, water absorption, bulk
density and fineness modulus. The values were compared
with IS 383 (2002) and it satisfied the requirements. The
results are given in Table 1.
The superplasticizers used were from four different
families. They are Sulphonated Naphthalene Formaldehyde-SNF
(SP1), Lignosulphates-LS (SP2), Polycarboxylic Ether-PCE
(SP3) and Sulphonated Melamine Formaldehyde-SMF (SP4).
Superplasticizers were tested for their densities and solid
content according to IS 9103 (2004). The test results are
given in Table 2. Based on the solid content and density of
the superplasticizer the necessary water correction was done
on the mix for specific superplasticizer dosage. As the
incompatibility between cement and superplasticizer can
lead to excessive bleeding, segregation and other undesirable
properties (excessive setting time, air entrainment), it is
essential to check the compatibility between the two before
using it in SCC mixes. In this work the compatible
combination approach based on saturation dosage of
(Sathyan et al. 2016)
were used. Mini slump and Marsh
cone tests were performed on the superplasticized cement
paste mix to find the saturation dosages. Bleeding and
segregation were observed in some SCC mixes (especially with
PCE superplasticizer) when the superplasticizer dosage is
considerably on the higher side of saturation dosage (but
within the manufacturers recommendation). Such mixes
were eliminated and superplasticizer quantity was kept in a
range close to saturation dosage.
The trials were performed by varying the quantity of fine
aggregate, coarse aggregate, type of superplasticizer and its
dosage. Fresh stage properties (flowability, filling ability and
segregation resistance) of the mixes were checked and only
those mixes which satisfied all requirements were taken for
The fine aggregate to total aggregate percentages was
varied as 45, 50 and 55. This range was selected based on
bulk density tests i.e., the range yielding higher bulk density
with lesser voids. The ranges of ingredients used in SCC
mixes are given in Table 3. As the water cement ratio is
maintained constant for all the mixes, there is no change in
the quantity of cement used.
SCC was tested for both its fresh and hardened properties.
The flowability of the SCC mix was found out by doing
slump flow test according to UNI 11041 (2003) and the
spread diameter of the concrete was measured. The passing
ability of the SCC mix was found out by doing J ring test
according to UNI 11045 (2003) specification. The height of
concrete just inside and outside J ring bars was measured
and their difference was taken. To find the segregation
resistance of the SCC mix V funnel test was carried out
according to UNI 11042 (2003). The time required for
emptying the funnel after 5 min (T5) was noted. The results
obtained from all the three tests were compared with the
specifications given in UNI 11040 (2003). A mix which
satisfies all the three rheological characteristics was accepted
as a SCC mix. For finding out the hardened properties, SCC
was tested for its compressive strength. For finding the
Water absorption (%)
Bulk density (kg/m3)
compressive strength of concrete, cubes were cast of
dimension 10 9 10 9 10 cm and tested in accordance with
IS 516 (2004). The experimental results obtained for slump
flow, V funnel, J-ring and compressive strength was used for
both training and testing of the model.
5. Database Preparation and Modeling
The accuracy of prediction of the rheological and
hardened properties of SCC depends to a great extent on how
accurate the training data is. If a larger data set is used,
predictions will be more accurate as the model will be able to
understand the correlation between the mixture components
and the measured engineering properties. The training data
consists of 32 data sets. The input parameters consist of
weights of mixture ingredients like cement, coarse
aggregate, fine aggregate, four families of superplasticizer dosages
and water. The input parameters have been chosen based on
their effect on the mix. The respective sources of cement,
coarse aggregate and fine aggregate used for all the mixtures
were not varied and hence they were all treated as separate
single input parameters. As superplasticizers were selected
from four different families, their dosages were treated as
four separate input parameters. This was necessary as the
effect of different family superplasticizers on the mix
characteristics were different for the same dosage. More input
parameters can be given if we have a large database adopting
varieties of materials. The output vector consists of the
measured value of slump flow, J ring and V funnel of the
SCC mix for fresh stage properties. For the prediction of
hardened properties, the output vector consists of the
measured values of compressive strength. To measure the
accuracy of model, 8 set of test data is used. All the input
and output parameters are normalized using norm method.
The formulae is given by
ym ¼ jjyy1jj ð7Þ
jjyjj ¼ y21 þ y22 þ y23 þ . . .. . .. . . þ yn2 ð8Þ
Here ym is the normalized value and y1 is the original value.
The values are normalized to one so they will be in the range
from 0 to 1. The normalized value of training data is given in
6. Analysis and Discussion of Result
The success of the model depends on its ability to
understand the correlation between the mixture components
and the measured engineering properties. The accuracy of
the model is determined by its ability to predict the
properties of SCC mixture which are familiar and unfamiliar to
the model but similar to the mixes used in training.
Prediction accuracy of the model for familiar and unfamiliar mixes
was tested in this study. Accuracy of prediction for familiar
mixes were checked using six sets of data selected randomly
from training data (Table 5). RMSE values of predicted
slump flow, J-Ring, V-funnel and compressive strength of
the familiar mixes are 0.012, 0.025, 0.023 and 0.025
respectively and their mean absolute error (MAE) values are
0.009, 0.019, 0.017 and 0.021 respectively. For testing the
prediction accuracy of the unfamiliar mixes, eight separate
sets of data (not utilized for training) were used. Predicted
and measured values of unfamiliar mixes are given in
Table 6. RMSE values of predicted slump flow, J-Ring,
V-funnel and compressive strength of the unfamiliar mixes
are 0.013, 0.038, 0.049 and 0.046 respectively and their
MAE values are 0.010, 0.029, 0.032 and 0.036 respectively.
Both RMSE and MAE values of the unfamiliar mixes are
slightly higher than that of familiar mixes for all the
predicted properties. Graphical comparison of the predicted and
measured value is shown in Figs. 2, 3, 4, 5.
The prediction accuracy of the model largely depends on
number and nature of data available for training. With
limited amount of training data available (only 32) in this study,
the accuracy of the model could be lower as it captures the
influence of all mixture components on the SCC mix in a
limited way within the experimental domain (as indicated
earlier in Table 3). However the preliminary study (Prakash
et al. 2018) on comparison of prediction models with respect
to split tensile strength showed marginally higher accuracy
for RKS model over that of ANN model for the same
experimental data set. Enhanced prediction accuracy of the
RKS model is also established by comparing the mean
absolute error in the predicted values obtained using the
proposed RKS model with those of the ANN model
et al. 2001)
, in which the average absolute value of the
predicted filling capacity, slump value, segregation
resistance and compressive strength were 0.05, 0.04, 0.07 and
7. Summary and Conclusion
This study showed that the rheological and hardened
properties of SCC can be predicted using regularized least
square approach along with random kitchen sink algorithm.
The RKS model was able to predict the rheological
properties (slump flow, passing ability and segregation
resistance) and compressive strength of test mixes which were
familiar and unfamiliar to the model. Mean absolute error
and root mean square error of all predicted properties are less
than 0.05. Thus the model is able to understand the
relationship amongst the ingredients, capture the effect of the
considered mixture variables and satisfactorily predict the
fresh and hardened stage properties of SCC. There is
potential to improve the model subsequently by
incorporating the effect of the change in site conditions, mixing
methods, placing methods etc. in the training data. Such an
improved model generated with an extensive data base will
be useful for industries to limit the number of trials and thus
minimize wastage of materials and labour.
Conflicts of interest
The authors declare that they have no conflicts of interest.
provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made.
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