Conference PaperPDF Available

Use of Neural Networks for the Evaluation of Concrete Core Strengths

S Tapkin
Civil Eng. Department
Anadolu University
Eskisehir, Turkey
M Tuncan
Civil Eng. Department
Anadolu University
Eskişehir, Turkey
K Ramyar
Civil Eng. Department
Ege University
Izmir, Turkey
Abstract- This paper examines a method to evaluate
concrete core strengths by using artificial neural
networks. Eight different concrete mixtures were
prepared by using two different aggregates of four
different maximum sizes. Beam specimens were cast
by prepared mixtures. Cores with different diameters
and length-to-diameter ratios were drilled from beam
specimens. Compressive strength tests were carried
out on core specimens at different ages. The
parameters influencing the strength of cores were
used as input for neural network architecture and the
core strengths were evaluated. The outputs of the
proposed network were examined by root mean
squared errors (RMSE). The proposed architecture
gave reliable estimates of the concrete core strength.
The RMSE values were found to be highly reliable.
Conclusively, the results revealed that the feed
forward back propagation neural networks can
perform to obtain reasonable evaluation of core
Keywords: Neural networks, Back propagation,
Compressive strength, Core strength
The strength is one of the most important
properties of concrete [1-4]. The quality control of
concrete in structures is generally carried out on
standard test specimens [5,6]. However, it is
difficult to assess the actual strength of concrete in
structures since the compaction and curing received
by the in-situ concrete and those received by the
standard specimens are quite different [7]. This
becomes more pronounced for larger members [8].
On the other hand, it is sometimes necessary to
know the strength of concrete in a structure [9].
Although it is expensive, the core test is one the
O Arioz
Civil Eng. Department
Anadolu University
Eskişehir, Turkey
A Tuncan
Civil Eng. Department
Anadolu University
Eskişehir, Turkey
most reliable methods to determine the strength of
concrete in structures [2]. However, the results of
the core tests should be carefully interpreted since
the strength of cores is influenced by a number of
factors such as diameter, length-to-diameter (l/d)
ratio, and the moisture conditions of the cores
[2,3,11-16]. Moreover, the maximum size of the
aggregate in concrete mixture plays an important
role for the evaluation of the test results [3,17]. This
is strongly emphasized in recently published
Turkish Standard, TS EN 12504-1 [18].
In the present study, the effects of core
diameter, l/d ratio as well as the type and maximum
size of the aggregate and the age of the concrete on
the core strengths were examined by means of
neural networks.
The progressing development of
neurobiology has enabled scientists to develop
mathematical models of neurons for the simulation
of neural behaviour. In the early 1940s, one of the
first abstract models of a neuron was introduced by
McCulloch and Pitts [19]. Hebb proposed a
learning law explaining how a network of neurons
learned [20]. Minsky and Rosenblatt followed this
notion through the next two decades [21, 22]. Later,
Minsky and Papert pointed out theoretical
limitations of single-layer neural network models
[23]. Research on artificial neural networks failed
into an indefinable era for nearly two decades due
to this pessimistic projection. In spite of the
negative atmosphere, some researchers still
continued with their research and produced
valuable results. For example, Anderson and
Grossberg did important studies on psychological
models and Kohonen developed associative
memory models [24-26]. In the early 1980s, the
neural network approach was resurrected. Hopfield
introduced the idea of energy minimization in
physics into neural networks [27]. His influential
paper endowed this technology with renewed
momentum. Feldman and Ballard made the term
“connectionist” popular [28]. Sometimes,
connectionism is also referred to as subsymbolic
process, which have become the study of cognitive
and artificial intelligence systems inspired by neural
networks [29]. Unlike symbolic artificial
intelligence, connectionism emphasized the
capability of learning and discovering
representations. Insidiously, connectionism has
become a common ground between traditional
artificial intelligence and neural network research.
In the middle 1980s, Rumelhart and McClelland
generated great impacts on computer, cognitive and
biological sciences [30]. Notably, the
backpropagation learning algorithm developed by
Rumelhart, Hinton and Williams offers a powerful
solution to training a multilayer neural network and
shattered the curse imposed on perceptrons [31].
However, it should be noted that the idea of
backpropagation had been developed by Werbos
and Parker independently [32, 33]. The symbolic
approach which has long dominated the field of
artificial intelligence was recently challenged by the
neural network approach. There have been
speculations about whether one approach should
substitute for another or whether the two
approaches should coexist and combine. More
evidence favours the integration alternative in
which the low-level pattern recognition capability
offered by the neural network approach and the
high-level cognitive reasoning ability provided by
the symbolic approach complement each other. The
optimal architecture of future intelligent systems
may well involve their integration in one way or
In this study, the core strengths were
analyzed by feed forward back propagation neural
networks. The reason of utilizing feed forward back
propagation was that they were used widely in
almost every study concerning neural network
applications. In this study, there are two hidden
layers in the present architecture opposed to the
other studies which have only one hidden layer in
their architectures [34-41]. The training process
time does not differ too much with two layered
architecture and this gives a more flexible approach
to the solution. The gradient descent algorithm was
used in the training process.
There are several studies on the application of
neural networks to predict the compressive strength
of concrete through input parameters such as type
and dosage of the cement, water-cement ratio,
fineness modulus of sand, sand-aggregate ratio,
slump, type and dosage of admixtures, etc. [34-41].
The use of test results in the neural network
approaches is a fairly new concept. In a recent
study by Hola and Schabowicz, non-destructive
assessment of concrete strength using artificial
intelligence has been presented [42]. The core test
results have not been utilised yet in a neural
network approach. In this study, type and maximum
size of the aggregate used in concrete mixture,
diameter, length-to-diameter ratio and the age of the
concrete cores were used as input parameters for
the estimation of concrete core strength by means
of artificial intelligence. Both the architecture of
two hidden layers and the gradient descent
algorithm has been utilised.
In this study, the neural network toolbox of
MATLAB was used. The reason of using this
software was to provide quick and reliable results.
Two main data sets were analysed. One of them
was for cores removed from crushed limestone
aggregate-containing concrete. The other one was
for cores drilled from natural aggregate containing
concrete. Table 1 presents designations, mix
proportions and some properties of the concrete
The cores with 144, 94, 69 and 46 mm in
diameter were obtained and cut to six different l/d
ratios which were selected as 2, 1.75, 1.5, 1.25, 1,
and 0.75. The cores were tested at the ages of 7, 28,
and 90 days and the compressive strength values
were calculated by taking the average of at least six
Table 1.Constituents and some properties of concrete mixtures
Mix Proportions (kg/m
) Some Properties
Type of
MIX-A 696 1043 356 215
MIX-B 729 1094 331 200 15
MIX-C 1034 846 315 190 22
MIX-D 1128 752 315 190 30
MIX-E 507 1259 356 195
MIX-F 833 994 331 181 15
MIX-G 1158 706 315 173 22
MIX-H 1300 565 315 173 30
The problem can be defined as a nonlinear
input-output relation between the influencing
factors (core diameter, l/d ratio, maximum
aggregate size and age of concrete) and
compressive strength values at 7, 28 and 90 days.
Fig.2 illustrates the architecture of the neural
network applied in the present study. There are four
nodes in the input layer corresponding to above
mentioned four factors and one in the output layer
corresponding compressive strength. Lots of trials
were carried out for the determination of hidden
neuron number of the two hidden layers. This
procedure was performed for cores drilled from
both crushed limestone aggregate and natural
aggregate-containing concretes. Different optimum
hidden neuron numbers were obtained for different
cases. In this study, the neurons of neighbouring
layers were fully connected.
Each batch of data was divided into two sets,
one for the network learning called training set, and
the other for testing the network called testing set.
Each set was composed of 144 pairs of input and
output vectors. Each input pair was calculated by
taking the average of at least six specimens. An
input vector consisted of four components and an
output vector had only one component.
In general, the network parameters; number of
training samples for each concrete core sample
property was 144, number of input layer neurons
was 4, number of hidden layer neurons ranged
between 5 to 50, number of output layer neurons
was 1, type of back-propagation learning rule was
gradient descent algorithm, activation functions
were logarithmic sigmoid, learning rate was 0.3 and
number of epochs varied from training to training.
Actually, the number of training samples was more
than 144 and different combinations of the number
of hidden neurons and activation functions for the
training of the neural network architecture were
used to have the optimum number of hidden
The network was tested with 144 pairs. It was
found out that logarithmic sigmoid activation
function served our purpose very well. Therefore,
logarithmic sigmoid activation function was used
throughout the analyses.
Fig.2. Neural network architecture
Fig.3. Sample training performed through the analyses
l/d ratio
aggregate size
Hidden la
Input layer
Output layer
Fig.3 shows a typical sample training session
performed in this study. As the data set was
representative of the test data, the learning process
terminated after approximately 200 epochs. As
analyses proceeded, it was seen that the epoch number
rose to maximum 600. The testing set was employed
to evaluate the confidence in the performance of the
trained network. One hundred and forty four testing
vectors of the batch of data were used to test the
neural network model. The training was conducted on
by the 10 and 15 mm maximum aggregate sizes and
the testing was carried out by the 22 and 30 mm
maximum aggregate sizes.
The target outputs of the output neurode are
supposed as the actual compressive strength obtained
from the results of the core tests. The training data set
was normalised before the analyses and the predictive
capabilities of the feed forward back-propagation
neural network were examined. The basis of this
discussion was to demonstrate the prediction
performance of these models by comparing their
levels of prediction rather than to illustrate how well
the models predict a given set of data. The prediction
performances were compared with the Root Mean
Squared Error (RMSE) values. The lesser the Root
Mean Squared Error, the better the estimates were.
RMSE values can be obtained by the following
standard formula:
= number of observations,
= predicted values, and
= Observed values
In other word, the correspondence of the data set
has been ensured. The behaviour of all of the system,
rather data set can be monitored by this way.
Therefore, it is much easier to decide the number of
hidden neurons that can be utilised in the hidden
layers. This is solely done on a root mean squared
error minimisation basis. This means that when the
value of the root mean squared error for the whole set
of data is minimum, the optimum number of hidden
neurons is determined. Many trials were carried out to
determine the optimum number of hidden neurons. It
was found that the optimum number of hidden
neurons was 40 and 35 for cores obtained from
crushed aggregate-containing and natural aggregate-
containing concrete, respectively. After obtaining the
number of hidden neurons, some further analyses
were also carried out to determine the optimum
learning rate. Fig.4 shows the RMSE values for
different hidden neuron numbers. It can be seen that
the smallest RMSE value was obtained by 40 hidden
neurons. The learning rates were found to be 0.3 and
0.5 for cores drilled from crushed limestone and
natural aggregate-bearing concretes, respectively.
When the simulation results for the optimum
hidden neuron numbers were further analyzed, it can
be seen that the modelling results are reasonably good
for such a big data set. RMSE values of 0.0708 and
0.1006 are fairly representative for crushed limestone
and natural aggregate-bearing cores, respectively. It is
not surprising to observe some fluctuations in the root
mean squared errors due to the nature of the back
propagation algorithm. However, it was observed that
the modelling results were very close to the real
compressive strength test results.
As the data set is extremely big, the analyses
gave fairly reasonable results and show the behaviour
of the whole system. As the data sets are composed of
mainly four elements acting together as a whole unit,
there were no means to show the effect of each of
these parameters on concrete strength individually.
Therefore, the above given root mean squared values
show the most correct and realistic representation of
the analysis results.
According to Fig.4, the RMSE values range
between 0.07 and 0.13. There was a regular pattern of
spread in the RMSE values as the graph was
analyzed. Since the minimum RMSE value was
important, the optimum hidden neuron number for
cores drilled from crushed aggregate-containing
concrete was forty. Further analyses were carried on
the forty hidden neuron neural network architecture
and it was found out that the optimum learning rate
was 0.3. Similar analyses were carried out on results
obtained from natural aggregate-bearing concrete and
the optimum hidden neuron number was found to be
thirty five. Further analyses were carried on the thirty
five hidden neurons network architecture (Fig.3). It
was found out that the optimum learning rate was 0.5.
This type of error presentation is more realistic
and meaningful. In this way, a more visual insight to
the whole data set’s performance can be obtained. A
new point of view to the neural network training and
testing can be drawn by the help of the RMSE and
learning rate graphs.
RMSE vs HN Number
25 30 35 40 45 50
Hidden Neuron
Fig.4. RMSE values vs. hidden neuron number for crushed
aggregate-containing cores
RMSE vs LR Value
0.1 0.2 0.3 0.4 0.5 0.6
LR Value
Fig.5. Different learning rate values for forty hidden
neurons for crushed aggregate-containing cores
RMSE vs HN Number
25 30 35 40 45 50
Hidden Neuron Number
Fig.6. RMSE values vs. hidden neuron number for natural
aggregate-containing cores
RMSE vs LR Value
0.1 0.2 0.3 0.4 0.5 0.6
LR Value
Fig.7. Different learning rate values for forty hidden
neurons for crushed aggregate-containing cores
The core strength test results were analyzed by
means of multi layer feed forward back propagation neural
network model. In this analysis, gradient descent algorithm
and two hidden layers were employed. The following
conclusions can be drawn from this study;
1. The results obtained from the analyses show that the
prediction of the compressive strength of concrete core
specimens by artificial neural networks particularly by
the gradient descent algorithm and two hidden layers
architecture was a viable method. This was mainly
evidenced by the calculated RMSEs for the gradient
descent network. Moreover, by the differences between
the RMSEs enabled to determine the optimum hidden
neuron numbers and the learning rates that make easier
estimations of the core strengths.
2. The average compressive strengths of concrete cores
determined by the artificial neural networks and by
destructive tests during the investigation were very
similar to each other. It was highly significant that the
calculated RMSEs were definitely low therefore it
indicates that the estimations were representative of the
real results.
3. The responsible person on the site can neurally identify
the compressive strength of similar concretes
incorporated in building structures without needing to
determine correlations or to fit hypothetical scaling
curves. Required optimum hidden neuron number and
learning rate values for better predictions can be
obtained by means of RMSE values. A neural network
model can be constructed to provide a quick and
dependable mean of predicting the core strengths. This
model may convert the strength of non standard core to
that of a standard core recommended by relevant
standards and specifications. Neural networks will be
useful to civil engineers especially dealing with material
engineering to evaluate core strength and will provide a
sound basis for these and similar types of analyses.
The authors would like to acknowledge the
financial and technical supports supplied by Scientific
Research Projects (03 02 23) Commission of Anadolu
University, Turkey. The authors also thank to Research
Assistant Kadir Kilinc for his great efforts for the
preparation of the manuscript.
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... Therefore, the network will begin to provide accurate responses after presenting a significant number of samples its strength have reached 99% in 28 days, still concrete continues to gain strength after that period, but that rate of gain in compressive strength is very less compared to that before 28 days. In carrying out these tests we intend to find out the effect of hydration periods on the compressive strength so as to understand the method of strength development of the concrete mixes [26,44,45]. The target characteristic strength of 35 N/mm 2 with a cement content of 290 N/mm 2 , a coarse aggregate content of 1198.65 kg/m 3 , a fine aggregate content of 766.35 kg/ m 3 and a water-cement ratio of 0.45. ...
Full-text available
The use of aluminium waste (AW) and sawdust ash (SDA) in concrete was evaluated in this study where the cement ratio was partially replaced by fractions of AW and SDA introduced as a supplementary cementitious material. Artificial neural network (ANN) was adapted as the modelling tool for this study and was developed with a two-layer feed-forward network, hidden neurons with sigmoid activation function and linear output neurons for the simulation of the network. The setting time and concrete compressive strength at varying curing days were predicted using the neural network model with vari-ations of constituents of the cement content consisting of OPC, SDA and AW as the input of the network. Three input and seven output data set were used for the model development using the following algorithms; Data Division: Random, Train-ing: Levenberg–Marquardt and Calculation: MATLAB. The data sets are set aside for validation, training and testing; 70% of the samples are used for training, 15% for validation and 15% are also used for testing. The performance of the networks was evaluated using linear regression, RMSE and R-values. The model performance scored 0.91 and 0.07 for R2 and RMSE, respectively, and performed better than the linear regression model, the results indicate the efficiency, reliability and useful-ness of ANN for predicting concrete mechanical properties where AW and SDA are used to replace cement ratio accurately.
... In-situ strength is the strength of the concrete as it exists in the element at the time of sampling and is the end result of the quality materials characteristics, construction techniques, workmanship and exposure. Tapkin et al. [6] were made a study to examine a method to evaluate concrete core strengths by using artificial neural networks. Eight different concrete mixtures were prepared by using two different aggregates of four different maximum sizes. ...
Conference Paper
Full-text available
Assessment of in-situ concrete strength by means of cores cut from hardened concrete is accepted as the most common in-situ nondestructive method, however the assessing of the concrete in the existing buildings, particularly in the troubleshooting of problems with new construction, If the strength of standard compression test specimens found to be below the specified 28 days value, frequently, cores tests are undertaken at later ages exceeding the 28 days. This study includes an attempt to find the influence of the long-term concrete age and strength level on the compressive strength development for the standard concrete core. This study involves laboratory investigation were number of specimens including concrete panels and cubes with specified compressive strength ranging from 25-55 MPa were prepared and tested at concrete age of 28, 60, 90, 120, 180,and 270 days by in-situ nondestructive tests (cores) and destructive tests (cubes). The test results obtained from core specimen were compared with those of standard specimens. The test results showed that the core compressive strength increases as the age of concrete increase, but the core strength is somewhat higher than 28-day cube compressive strength even up to the age 270 days in moderate concrete, while the core compressive strength remains lower than 28-day cube compressive strength in the higher strength level even up to the age 270 days.
Investigates the influence of concrete strength on the measured core compressive and tensile strengths of three large columns. The lower strength concrete had larger variability in strength than the higher strength concretes. Measured compressive and tensile strengths from the core specimens were found to be between those strengths measured on cylinders cured in air and those cured in water. The permeability was found to decrease significantly with the increased matrix densities of the higher strength concretes.
Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
The relationship between the in-place compressive strength of concrete in structures and specified strength fc′ is investigated through the use of factors F1 and F2. Factor F1, the ratio of the average strength of standard 28-day-old cylinder specimens to the specified strength, is evaluated using data from 3756 cylinder tests representing 108 concrete mixes produced in Alberta, Canada, between 1988 and 1993. Factor F2, the ratio of average in-place strength to average cylinder strength, is evaluated using core and cylinder data representing 108 concrete mixes with strengths less than 55 MPa that were investigated by others. A statistical description of the compressive strength of concrete in structures is derived that accounts for the inherent randomness of Factors F1 and F2 and also the typical strength variation within a specific structure. The probability of the in-place compressive strength of concrete in a 28-day-old column being less than fc′ is approximately 13 percent. It is likely that a recalibration of the load and resistance factors for the design of new structures in Canada based on these findings would yield greater factored concrete strengths than are currently in use.
In accordance with the provisions of ASTM C 42-90 and ACI 318-89, it is current practice to either dry concrete core specimens in air for 7 days or soak them in lime-saturated water for at least 40 hr before they are tested. In this paper, the effect of moisture condition on the strengths of mature cores obtained from well-cured elements is investigated by reviewing available literature and performing regression analyses of data from tests of 727 core specimens. It is shown that the compressive strength of a concrete specimen is influenced both by moisture content changes that are uniform throughout the specimen volume and moisture content gradients between the surface of the specimen and interior. The air-drying and soaking periods specified in ASTM C 42-90 and ACI 318-89 are too short to cause a uniform change of moisture content throughout the volume of the core. The effect of these treatments is to create a moisture gradient that artificially biases the test result. The strength of air-dried cores is on average 14 percent larger than the strength of soaked cores. The strength of cores with a negligible moisture gradient is on average 9 percent larger than the strength of soaked cores. These general average values are constant for concretes with strengths ranging from 2200 to 13,400 psi. However, the strength ratios for any particular mix may differ appreciably from these general average values.
Different procedures for material and structural investigations are adopted in British practice when assessing a) noncompliance of concrete under construction and b) the current strength of concrete in structures. This paper presents these procedures and describes the various factors that have to be taken into account depending on whether the potential strength, the actual strength, or the in situ cube strength is required in the investigation. Emphasis is placed on the usefulness of performing nondestructive tests on site to optimize the manner of extraction of cores and combining these results with those derived from cores. The author stresses the importance of taking the utmost care in sampling, extraction of cores, preparation, and testing. Because the extraction of cores is such an expensive exercise, it is recommended that the greatest number of nondestructive tests are also carried out in the laboratory on the cores before finally testing them in compression. By so doing, the results of the nondestructive tests increase the confidence level of the ultimate strength results.
Data are analyzed to determine the effect of the specimen length-to-diameter ratio (l/d) on the magnitude and precision of the compressive strength of concrete cores. The data represent strength tests of 758 core specimens, all 4 in. in diameter, obtained from 10 different elements cast from ordinary portland cement concretes with strengths between 2000 and 14,000 psi. Strength correction factors are determined for converting the strength of a core with an l/d between 1 and 2 to the strength of an equivalent standard specimen with an l/d of 2. The data indicate that both the core moisture condition and the core strength significantly affect the strength correction factors. The proposed strength correction factors differ only slightly from those currently recommended in ASTM C42-90. The single-operator coefficient of variation is found to be independent of the core length-to-diameter ratio. Typical values are 4 percent if the effect of spatial variation of the in situ strength is accounted for, or 5 percent otherwise.
A full-scale seven-storey in-situ advanced reinforced concrete building frame was constructed in the Building Research Establishment's Cardington laboratory encompassing a range of different concrete mixes and construction techniques. This provided an opportunity to use in-situ non-destructive test methods, namely Lok and CAPO tests, on a systematic basis during the construction of the building. They were used in conjunction with both standard and temperature-matched cube specimens to assess their practicality and their individual capabilities under field conditions. Results have been analyzed and presented to enable comparisons of the performance of the individual test methods employed.
The purpose of this paper is to develop the I-PreConS (Intelligent PREdiction system of CONcrete Strength) that provides inplace strength information of the concrete to facilitate concrete form removal and scheduling for construction. For this purpose, the system is developed with artificial neural networks (ANN) that can learn cylinder test results as training patterns. ANN does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. However the system is initially developed by the single architecture of ANN. The initial system has a problem, which it cannot appropriately predict the concrete strength when the curing temperature of a specific curing day is changed. This is because it uses the single architecture, which all nodes are fully connected, and thus it could show too plastic response. As a trial to solve this problem, modular ANN is proposed, which has multiple architectures composed of five ANNs (ANN-Isimilar toV). ANN-I predicts the early strength within 24 hours after pouring. From ANN-II to ANN-V predict the concrete strength at 2nd to 28th day after pouring. Through simulation study, the optimum architectures for individual five ANNs are determined and the best nodes are investigated for inter-connection between ANNs. Two major techniques are applied to increase the accuracy and to more precisely predict concrete strength development. One is to use parameter condensation technique in the determination of input neurons. The other is to apply the weighting technique of input neurons for more prediction accuracy. This study shows that I-PreConS using ANN is very efficient for predicting the compressive strength development of concrete.