Compressive Strength Prediction of High Strength Concrete using
Regression and ANN Models.
S Mandal1, Shilpa M2, R Rajeshwari3
Department of Civil Engineering
PES University, Bengaluru -560085 [email protected], [email protected], [email protected]
Abstract: High strength concrete (HSC) is one of the most popular terminologies used in the concrete
technology, which is known for benefits like high workable, durable and high ultimate strength. The
estimation of the compressive strength (CS) using experimental method is too expensive and time
consuming procedure and small error will lead to repetition of the work, to overcome this, alternative
methods are used for prediction of the CS of HSC. In the present study, the experimental data on the
strength of HSC of various mix designs are collected from authenticated journal papers, which are
used to predict the CS using regression analysis (MLR- multi-linear regression) and artificial neural
network (ANN) models. The collected data set are divided into two groups, one for training and other
for testing. The input parameters used in regression and ANN models are cement content,
superplasticizer, coarse aggregate, fly ash, fine aggregate, silica fume, blast furnace slag, water-
cement ratio and the CS of HSC at 28 days is the output parameter. The models are developed using
training dataset and the developed model is validated using testing dataset. The comparison is made
between the CS obtained from the MLR and ANN models. The ANN model yields better correlation
between predicted and actual values of the CS (test correlation for MLR-45.48% and ANN-95.03%)
and the percentage of error also reduces as compared to that of MLR. From this investigation, it is
observed that the ANN model can be used to predict the CS of HSC.
Keywords: High strength concrete, artificial neural network, Multi-linear regression.
Concrete is an excellent building material which carries the compressive stresses from large
infrastructure because of this advantage concrete is used for the construction. Concrete is classified as
different types based on their strength and performance. The concrete which has properties like high
durable, workable and high ultimate-strength is called as HSC. The production of HSC depends upon
the good selection and mix proportioning of the ingredient to achieve a high strength of concrete. The
use of HSC in construction increases the service life of the structure and suffers less damage which
would reduce overall cost.
In construction the estimation of the CS has received a large amount of interest because, the CS is one
of the important mechanical properties of concrete. On specific topic several investigations are carried
to estimate the CS of HSC with the addition of different types of admixtures. The comparison was
made by considering the properties like CS, consumption of cement and economic validation. HSC
saves the consumption of cement about 50% and which results in less usage of raw materials and
reduction in overall costs (Alves et. al 2003). The performance of HSC is studied by variation of
dosage of silicafume (SF) and water-cement ratio (w/c). SF effects negatively on the CS as compared
to w/c ratio (Behnood and Ziari 2007). HSC gives the high workability and concrete strength without
using the special ingredient except good quality of material and proportions of concrete (Rashid et. al
2008). The CS variation at different temperatures ranges are carried out. The study indicates that the
CS decreases with increase in temperatures and structure may damage by fire (Husem 2005). Partial
replacement of cement with other admixture increases the strength of concrete as compared to mixture
made by 100% port land cement (Ibrahim et. al 2013).
The estimation of the CS by experimental method is too expensive and time consuming procedure and
small error will lead to repetition of the work, to overcome this, an alternative method is used for
prediction of the CS of HSC. Many attempts are made to develop the suitable mathematical models
which have ability of find the strength of concrete at various ages and conditions. Sometimes
mathematical model like regression analysis shows poor agreement with experimental data. To deal
these drawbacks, a different method of modelling known as ANN is used. ANN is a soft computation
method based on the structure and functionality of biological neuron in similar way how human brain
works. The massages and information that flow through neural network, depends on the structure of
ANN because ANN has the ability to learn on the basis of data provided for the input and output
variables, and ANN method is trained and tested.
The present study involves the estimation of the strength of HSC. For this requirement, a total of 106
experimental data on HSC is extracted from authenticated journal papers; these data set are divided
into two groups, 70% of data are taken for training and remaining 30% data are taken for testing the
models. Finally the performances in estimating the CS of HSC using MLR and ANN are compared.
2. Modelling techniques
2.1 Multi-linear Regression Model
The linear regression model is a general form of regression models, the linear estimator functions are
used to model the data, and output parameters are determined from the data. Some times for the
regression analysis include more than one input variables which would lead to the formation of
“multiple linear regression” function. MLR evaluates the correlation between two or more input
variables by ?tting a linear equation between them and involves summarization of data as well as
investigation of relationship between variables. Mathematically, a MLR model can be expressed as
y = a? + a? x? +a? x?+… (1)
Where, y is the dependent variable, x?, x? … etc. are the independent input variables to the model,
and a?, a? , a?,… are partial regression coefficients.
2.2 Artificial Neural Networks
The artificial neuron mimics the characteristics of the biological neuron that is human brain structure
and it basically consists of inputs; each input represents the output of another neuron. The input used
to solve the problem is multiplied with corresponding weights and these weighted inputs are summed
with bias value. The summation of the inputs are processed in the hidden layer using transfer
functions like linear, tan-sigmoid and log-sigmoid transfer functions etc. The processed information
by transfer function sends along the output layer as required result. The arrangement of neurons into
layers and the connection pattern between other layers is known as network architecture.
Figure 1: Structure of an Artificial Neuron Model.
Figure 1 shows the structure of an Artificial Neuron Model. Feed forward network (FFN) is generally
used back propagation for training network where the error obtained at the output layer is moved back
to the input and hidden layers for updating weights and decreases the errors to get the best output
from ANN. The main aim of the FFN process is to reduce the overall error between the observed and
estimated values by adjusting the weights, and these weights are combined and processed through an
activation function and released to the output layer.
For the present study, experimental data on HSC are obtained from various authenticated journals
(Alves et. al 2003; Behnood and Ziari 2007; Husem 2005; Ibrahim et. al 2013; Elahia et. al 2010;
Prasad et. al 2009; Biskri et. al 2017; Kumar et. al 2017; Hassan et. al 2000;Ouda 2015;Rashida et. al
2008). A total of 106 datasets collected and the data consists of input and output parameters. The
input parameters include cement content, coarse aggregates, fine aggregates, blast furnace slag, silica
fume, fly-ash content, super plasticizer and water-cement ratio. Output parameter is the 28 days CS of
HSC. All collected data are converted into desirable units. Further, these data sets are normalized
using Eq. 2.
( ) ( )
Where, is the normalized value, is the actual value, is ( ) the maximum value, ( ) is
the minimum value.
Table 1: Range of experimental variables
Cement(kg/m3) 135 – 91O
Coarse aggregate(kg/m3) 814.54 – 1547.06
Fine aggregate(kg/m3) 241.93 – 1164
Fly ash(kg/m3) 0 – 27M
Silica fume(kg/m3) 0 – 91
Blast furnace slag(kg/m3) 0 – 34M
Super plasticizers(kg/m3) 0 – 32.2
W/C(kg/m3) 0.21 – 1.23
Compressive strength(MPaF 55 – 88.9M
Total numbers of data sets collected are 106 and the data is divided into two sets: one is for testing
and another for training purpose. Data considered for training consist of varying values covering
maximum to minimum range. 70% of the data are taken for training purpose, therefore 75 data sets
are used for training the network. 30% of the data are taken for testing purpose; hence 31 data sets are
used for testingK
4. Methodology used for Model Development
4.1 MLR model
The MLR model constructed in the current study has eight input parameters namely cement content,
superplasticizer (SP), coarse aggregate (CA), fly ash(F),fine aggregate(FA), , silica fume(SF), blast
furnace slag(BFS) and w/c to get the 28 days CS as output parameter. Initially, the 75 train data set
containing both the input and output parameters are used to get the regression coefficients for each
variable and obtained CCtrain, thereafter remaining data are utilized to estimate the CS and obtained
CCtest. The estimated and observed CS of testing data sets are compared based on the correlation
between them and performance of MLR in predicting the CS of HSC is assessed based the CC value
of testing data set.
4.2 ANN model
Here, the ANN model with the 8- input nodes, 6-hidden nodes and one output node is developed to
estimate the CS of HSC. The input information is passed along the input layer no computation are
happens in this layer but it pass the information to the next layer called hidden layer in hidden layer
inputs are multiplied by weights and the bias value is added to the each input nodes. These weighted
inputs are processed by the transfer function of the ANN model, finally the processed information are
received from output layer. The ANN model works on the basis of the Levenberg-Marquardt (LM)
5. Results and Discussion
Regression (MLR) and ANN approaches have been used to predict the CS of HSC. The capability of
these models was assessed using statistical measures like Correlation Coefficient (CC), Root Mean
Square Error (RMSE) and Scatter Index (SI), which are defined as,
?( )( ) ??( ) ( )
Where and is the observed and predicted CS of HSC respectively. n is the number of data set
used. ? and is the average observed and predicted CS of HSC respectively.
5.1 Regression Analysis:
The MLR model is trained using normalized training data set. After training the model, the MLR
coefficients are used to predict the CS of test data set. Both train and test data sets are shown in
Figures 2 and 3.
Eq. 6 is developed using training data:
Y = – 0.5009 + (0.7885*x1) + (0.7421*x2) + (0.5846*x3) + (0.1693*x4) + (0.0115*x5) +
(0.6027*x6) – (0.1595*x7) – (0.1018*x8) (6)
Figure 2: Comparison between predicted and observed CS for training and testing by MLR
Figure 2 shows variation of the predicted and observed CS of trained and tested data set with the CC
of 0.6757 and 0.4548. This shows that prediction of CS by MLR is poor, as could be observed, when
data are scattered and not along or near by the (y=mx) line. If the predicted data are mostly lying
along a nearby (y=mx) line, the prediction is fairly, accurate.
5.2 Artificial Neural Network:
The ANN model is trained and tested using LM algorithm for a given input and output parameter. The
network is trained for different number of hidden layer nodes; initially 3 hidden nodes are used to
model. Further numbers of hidden layer nodes are increased to arrive at better results. The results
obtained during training and testing processes showing the CC, RMSE and SI values are shown in
Table 2: Statistical results obtained for ANN model (8-X-1).
NETWORK CC RMSE SI
CCs between the observed output and predicted output are calculated using Eq. 3. RMSE and SI
between the observed output and predicted output are calculated using Eq. 4 and Eq. 5 respectively.
From Table 2, it is observed that CC obtained for the network 8-6-1 with 15 epochs has achieved the
best value. The CC value obtained for trained and tested data is 0.9589 and 0.9503 respectively. The
RMSE value is found to be 6.8668 and 7.1643 for training and testing respectively. The SI value is
found to be 0.1695 and 0.1826 for training and testing respectively. Hence the ANN model with
network 8-6-1(Figure 4) yields the best performance to predict the CS of HSC.
Figure 3: Correlation between predicted and observed CSs for training and testing by ANN (8-6-1).
Figure 3 shows the comparison between predicted and observed values of CS by ANN (8-6-1) with
CC of 0.9589 and 0.9503 for trained and tested data respectively.
Figure 4: Structures of ANN (8-6-1).
Once the network is trained, the weight and bias values are fixed for that model. The ANN structure
constructed for predicting the CS of HSC is shown in Figure.4. The structure consists of 8- input
nodes, 6-hidden layer nodes, and one output node.
5.3 Comparison between MLR and ANN models
Table 3 shows the comparison between CC values for regression (MLR) and ANN models. The test
data for CC of 45.48% by MLR shows that it gives poor correlation; whereas CC of 95.03% by ANN
shows that the ANN model predicts the CS of HSC with very good correlation.
Table 3: CCs for MLR and ANN Models
Regression and ANN models have been trained and tested with about 70:30 of the total data sets.
Based on the present study, the following conclusions are drawn:
? The regression model yields CC of 0.6757and 0.4548 for training and testing data
respectively; this shows that estimation of the CS by MLR is poor.
? For better prediction of the CS of HSC, a soft computing model such as ANN is used and the
results are compared in terms of statistical measures such as CC, RMSE and SI.
? ANN model yields a good correlation between the input parameters and compressive strength
of HSC with 15 epochs. The statistical parameters obtained, CC- 0.9589 and 0.9503, RMSE –
6.86 and 7.18and SI- 0.16 and 0.18 for training and testing respectively, demonstrate that the
predicted output values are very close to the actual output values.
? With comparison to regression model, the performance of ANN models show good results in
terms of statistical measures like CC, RMSE and SI for the observed and predicted CS of
HSC. Therefore, the ANN model can be used to predict the CS of HSC.
1. Ali Behnood andHasanZiari, E?ects of silica fume addition and water to cement ratio on the
properties of high-strength concrete after exposure to high temperatures, Cement & Concrete
Composites – Elsevier 30 (2008) 106–112.
2. Ahmed S. Ouda, Development of high-performance heavy density concrete using different
aggregates for gamma-rays shielding, Housing and Building National Research Center-
Elsevier, HBRC Journal (2015)11, 328–338.
3. Ahmed Ibrahim, Hassan El-Chabib, Ahmed Eisa, Ultra strength Foldable Concrete Made
with High Volumes of Supplementary Cementitious Materials, Journal of material in civil
engineering-ASCE 25(12), (2013) 1830-1839.
4. A. Camões, B. Aguiar, S. Jalali, Durability of Low Cost High Performance Fly Ash Concrete,
International Ash Utilization Symposium, Center for Applied Energy Research, University of
Kentucky, Paper #43, 2003.
5. A. Elahia, P.A.M. Basheerb, S.V. Nanukuttanb, Q.U.Z. Khana, Mechanical and durability
properties of high performance concretes containing supplementary cementitious materials,
Construction and Building Materials-Elsevier 24 (2010) 292–299.
6. B.K Raghu Prasad , Hamid Eskandari, V. K VenkataramaRaddy , Prediction of compressive
strength of SCC and HPC with high volume fly ash using ANN , Construction and Building
Materials- Elsevier, 23 (2009), 117–128.
7. B.M. Vinay Kumar, H. Ananthan , K.V.A. Balaji, Experimental studies on utilization of
recycled coarse and ?ne aggregates in high performance concrete mixes, Alexandria
8. K.E. Hassan, J.G. Cabrera, R.S. Maliehe, The effect of mineral admixtures on the properties
of high-performance concrete, Cement & Concrete Composites- Elsevier, 22 (2000), 267-271.
9. M.F. Alves, R.A. Cremonini, D.C.C. Dal Molin, A comparison of mix proportioning methods
forhigh-strength concrete, Cement & Concrete Composites- Elsevier 26 (2004) 613–621.
10. Mohammad AbdurRashida and Mohammad AbulMansurb, Considerations in producing high
strength concrete, Journal of Civil Engineering (IEB), 37(1) (2009) 53-63.
11. M. Husem, The effect of high temperature on compressive and flexural strengths of ordinary
and high performance concrete, Fire Safety Journal – Elsevier, 41 (2006), 155–163.
12. YasminaBiskri, DjamelAchoura, NourredineChelghoum, Michel Mouret, Mechanical and
durability characteristics of High Performance Concrete containing steel slag and crystalized
slag as aggregates, Construction and Building Materials- Elsevier, 150 (2017), 167–178.
13. FaezehossadatKhademi, Mahmoud akbar, SayedMohammadmehdi, Mehdi nikoo, Multiple
linear regression, arti?cial neural network, and fuzzy logic prediction of 28 days compressive
strength of concrete, Front. Struct. Civ. Eng. 2017, 11(1): 90–99.
14. FatihAltun ,Ozgur Kisi, KamilAydin, Predicting the compressive strength of steel ?ber added
lightweight concrete using neural network, Computational Materials Science- Elsevier , 42
15. FatihÖzcana, Cengiz D. Atis, OkanKarahanb, ErdalUncuog ?luc, HarunTanyildizid,
Comparison of arti?cial neural network and fuzzy logic models for prediction of long-term
compressive strength of silica fume concrete, Advances in Engineering Software- Elsevier, 40
16. Jui-Sheng Chou, Chih-Fong Tsai, Concrete compressive strength analysis using a combined
classi?cation and regression technique, Automation in Construction Elsevier 24 (2012) 52 –
17. MarekSlonski, A comparison of model selection methods for compressive strength prediction
of high-performance concrete using neural networks, Computers and Structures 88 (2010) 12-
18. Mohammad Iqbal Khan, Predicting properties of High Performance Concrete containing
composite cementitious materials using Arti?cial Neural Networks, Automation in
Construction-Elsevier, 22 (2012) 516–524.
19. Mustafa Sar?demir, Predicting the compressive strength of mortars containing metkaolin by
arti?cial neural networks and fuzzy logic, Advances in Engineering Software-Elsevier, 40
20. NeelaDeshpande, ShreenivasLondhe, SushmaKulkarni, Modeling compressive strength of
recycled aggregate concrete by Arti?cial Neural Network, Model Tree and Non-linear
Regression, International Journal of Sustainable Built Environment (2014) 3, 187–198.
21. Pinar Akpinara, Adnan Khashmanb, Intelligent classification system for concrete compressive
strength, Procedia Computer Science- Elsevier, 120 (2017) 712–718.
22. R. Parichatprecha, P. Nimityongskul, Analysis of durability of high performance concrete
using arti?cial neural networks, Construction and Building Materials- Elsevier, 23 (2009)
23. Seung-Chang Lee, Prediction of concrete strength using arti?cial neural networks,
Engineering Structures 25 (2003) 849–857.
24. Z.H.Duan, S.C.Kou, C.S.Poon , Prediction of compressive strength of recycled aggregate
concrete using arti?cial neural networks, Construction and Building Materials- Elsevier, 40