Advances in Materials Science and Engineering

Volume 2014 (2014), Article ID 647243, 13 pages

http://dx.doi.org/10.1155/2014/647243

Research Article

Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm

Yun-Yong Kim,¹ Byung-Jae Lee,² and Seung-Jun Kwon³

¹Civil Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Republic of Korea

²R&D Center, JNTINC Co., Ltd., No. 9, Hyundaikia-ro 830beon-Gil, Bibong-myeon, Hwaseong, Gyeonggi-do 445-842, Republic of Korea

³Civil and Environmental Engineering, Hannam University, 133 Ojeong-dong, Daedeok-gu, Daejeon 306-791, Republic of Korea

Received 3 June 2014; Accepted 11 August 2014; Published 11 September 2014

Academic Editor: Tao Zhang

Copyright © 2014 Yun-Yong Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Diffusion coefficient from chloride migration test is currently used; however this cannot provide a conventional solution like total chloride contents since it depicts only ion migration velocity in electrical field. This paper proposes a simple analysis technique for chloride behavior using apparent diffusion coefficient from neural network algorithm with time-dependent diffusion phenomena. For this work, thirty mix proportions of high performance concrete are prepared and their diffusion coefficients are obtained after long term-NaCl submerged test. Considering time-dependent diffusion coefficient based on Fick’s 2nd Law and NNA (neural network algorithm), analysis technique for chloride penetration is proposed. The applicability of the proposed technique is verified through the results from accelerated test, long term submerged test, and field investigation results.

1. Introduction

Chloride ions induced into concrete cause a severe corrosion in embedded steel and the durability degradation propagates to structural safety problem [1]. In the earlier researches on deterioration of chloride attack, studies based on field investigations and apparent diffusion have been performed [2–4]. Recently, micromodels based on mass conservation law and behaviors in early aged concrete like porosity, hydration, and saturation have been proposed [5–7]. Furthermore various phenomena like enlarged diffusion and permeation due to cracks and cold joint are considered for quantitative modeling on chloride penetration [8, 9].

Apparent diffusion coefficient based on Fick’s 2nd law is conventionally utilized for evaluating chloride behavior, however migration test and the related techniques are frequently utilized for measuring a resistance to chloride penetration [10, 11] since apparent diffusion coefficient experimentally obtained needs considerably long period. Chloride diffusion coefficients from migration tests indicate ion velocity in electrical field so that they cannot be directly employed to Fick’ 2nd law. For evaluation of chloride behavior using diffusion coefficient from migration test, complicated analysis frame is required [12, 13]. For evaluation of total chloride content, special relationship between free and bound chloride ion, so called isotherm, should be considered in the analysis frame [6, 14, 15]. Apparent diffusion coefficient can provide direct solution-chloride content based on Fick’s 2nd Law to engineers, and this technique has been widely applied for its simple formulation and several familiar-powerful programs like Life 365 [3, 16]. Additionally several engineering advantages like easy employing time effect on diffusion behavior [16] and random variables for stochastic approach [8] are found through treating apparent diffusion coefficient.

NNA (neural network algorithm) is one of the optimization techniques and this has been widely utilized for determination of mix proportions and strength evaluation in concrete research field [17–20]. For analysis of chloride behavior and carbonation, NNA technique is recently adopted [21, 22] but the related analysis technique needs such complicated micromodels and major assumptions that it is not widely used for engineers.

In this paper, apparent diffusion coefficients, which can provide a direct solution (chloride content), are obtained from chloride-submerged condition for 6 months. Major mix components are selected as neurons and training for learning is carried out for optimum apparent diffusion coefficients. The simulated diffusion coefficients are verified with test results. A simple and deterministic analysis technique for chloride behavior is proposed considering time-dependent diffusion characteristics and the apparent diffusion coefficient from NNA. In this paper, diffusion coefficients from various HPC (high performance concrete) mix proportions, NNA application for reasonable selection of diffusion coefficients, and simple technique for chloride penetration prediction are dealt with. In Figure 1, flow chart for the work is shown.

Figure 1: Flowchart for this study.

2. Outline of NNA

It is reported that NNA was started by McCulloch and Pitt [23]. A neuron as a unit with process of stimulus and reaction is modeled in the system. The training for learning a data set is conducted with connection strength, transfer function, and biases. The errors between calculated and expected results are reduced with increasing epochs. The training for learning is completed when the error decreases to a target convergence level. In this paper, a back-propagation algorithm is adopted for the neural network. Figure 2 shows an outline of simple neural network architecture [22, 24].

(a) Simple neural network architecture

(b) Activation function

Figure 2: Schematic diagram for simple NNA architecture [22, 24].

In this network, each element of input is connected to each neuron input through the weight matrix. Neurons () and activated values () in the hidden layer are formulated as (1) and (2). Activated value, can be written as (3). Consider

where is input vector, is weight or connection strength, is transfer function, and is bias. In the back-propagation, error () is calculated through (4) considering target value (). Consider

For minimizing the error, connection strength () is modified backward form neurons in output layer like

where and are gradients of the total error, and is the learning rate.

After the modification of connection strength, NNA repeats the process of calculation and modification until the error decreases within the target convergence.

For the data set, each input should have boundary limits from 0.0 to 1.0. Through data process like (6), each value satisfies the boundary limit. Consider

where is input value for training, is actual input data, and and are maximum and minimum values of input data. After calculation, the output value with a range of 0.0~1.0 is obtained and it should be converted to actual value using (6).

3. Test Program for Apparent Diffusion Coefficient

3.1. Outline of Test Program

In this section, tests for learning and training of NNA are explained. Thirty mix proportions for HPC are prepared. Target slump and air content are  mm and %, respectively. Three w/b (water to binder) ratios are set as as 0.37, 0.42, and 0.47. After 28 days of water curing, the specimens were kept in 3.5% of NaCl solution for 6 months. For 1-dimensinal intrusion of chloride ion, sides and bottoms were coated with epoxy except top surface. After 6 months of submerging in NaCl solution, chloride profiles were measured based on AASHTO T 260. Through regression of chloride profile, surface chloride contents and apparent diffusion coefficients are obtained. For binding materials, OPC (ordinary portland cement) was used. GGBFS (ground granulated blast furnace slag), FA (fly ash), and SF (silica fume) were added for mineral admixtures. In Table 1, chemical compositions and physical properties of cement and the used mineral admixtures are listed. The physical properties of aggregates are listed in Table 2. Thirty mix properties which are used for learning and training of NNA are listed in Table 3.

Table 1: Chemical and physical properties of cement and mineral admixtures.

Types
Items

Chemical composition (%)
Physical properties

SiO₂
Al₂O₃
Fe₂O₃
CaO
MgO
SO₃
Ig. loss
Specific gravity (g/cm³)
Blaine (cm²/g)

OPC
21.96
5.27
3.44
63.41
2.13
1.96
0.79
3.16
3,214

GGBFS
32.74
13.23
0.41
44.14
5.62
1.84
0.2
2.89
4,340

FA
55.66
27.76
7.04
2.70
1.14
0.49
4.3
2.19
3,621

SF
93.3
0.5
1.21
0.27
1.03
0.02
1.1
2.21
190,620

Table 2: Physical properties of aggregates.

Types
Items

(mm)
Specific gravity (g/cm³)
Absorption (%)
F.M.

Fine aggregate
—
2.58
1.01
2.90

Coarse aggregate
25
2.64
0.82
6.87

Table 3: Mix proportions for HPC.

Names of mix
Items

w/b
Unit weight (kg/m³)
Binder × %

W
Binder Materials
S
G
Admixture

C
GGBS
FA
SF
SP
AE

o100-37
0.37
168
454
—
—
—
767
952
1.0
0.017

o100-42
0.42
168
400
—
—
—
787
976
0.9
0.015

o100-47
0.47
168
357
—
—
—
838
960
0.85
0.017

g30o70-37
0.37
168
318
136
—
—
762
946
0.8
0.018

g30o70-42
0.42
168
280
120
—
—
783
972
0.75
0.013

g30o70-47
0.47
168
250
107
—
—
835
956
0.65
0.015

g50o50-37
0.37
168
227
227
—
—
760
943
0.75
0.017

g50o50-42
0.42
168
200
200
—
—
780
969
0.7
0.0135

g50o50-47
0.47
168
178
179
—
—
832
853
0.6
0.015

f10o90-37
0.37
168
409
—
45
—
760
943
0.75
0.018

f10o90-42
0.42
168
360
—
40
—
780
969
0.9
0.021

f10o90-47
0.47
168
321
—
36
—
832
952
0.75
0.017

f20o80-37
0.37
168
363
—
91
—
752
934
0.75
0.018

f20o80-42
0.42
168
320
—
80
—
774
961
0.85
0.025

f20o80-47
0.47
168
286
—
71
—
826
946
0.7
0.017

f30o70-37
0.37
168
318
—
136
—
745
952
0.75
0.2

f30o70-42
0.42
168
280
—
120
—
768
953
0.75
0.015

f30o70-47
0.47
168
250
—
107
—
820
939
0.65
0.019

f10s05-37
0.37
168
386
—
45
23
756
938
1.0
0.023

f10s05-42
0.42
168
340
—
40
20
777
965
0.9
0.021

f10s05-47
0.47
168
303
—
36
18
829
950
0.9
0.021

f20s05-37
0.37
168
340
—
91
23
749
929
0.9
0.023

f20s05-42
0.42
168
300
—
80
20
771
957
0.85
0.025

f20s05-47
0.47
168
268
—
71
18
810
927
0.9
0.025

g30s05-37
0.37
168
295
136
—
23
759
942
0.75
0.015

g30s05-42
0.42
168
260
120
—
20
765
949
0.75
0.015

g30s05-47
0.47
168
232
107
—
18
832
952
0.8
0.015

g35f15-37
0.37
168
227
159
68
—
751
932
0.65
0.014

g35f15-42
0.42
168
200
140
60
—
773
959
0.65
0.014

g35f15-47
0.47
168
178
125
54
—
804
921
0.7
0.014

w/b: water to binder ratio.

G: gravel.

AE: air entrainer.

S: sand.

SP: superplasticizer.

3.2. Test Results

3.2.1. Compressive Strength with Ages

Compressive strength is measured at the age of 7, 28, 91, and 270 days. In Figure 3, the results of compressive strength with different ages are shown. The results show typical strength development, higher strength with lower w/b ratio. The smallest strength at the age 7 days is measured in f30o70 (30% replacement of FA) in Figure 3. Compared with the results in OPC, the strength ratio is only 69.9%, however, in the long term (270 days), concrete with mineral admixtures mostly shows higher strength than OPC concrete. It is reported that the ability of a mineral admixture to react with calcium hydroxide present in the hydrated Portland cement paste and to form additional calcium silicate hydrates can lead to significant reduction in porosity of both the matrix and the transition zone. Consequently, considerable improvement in ultimate strength and water-tightness can be achieved by incorporation of mineral admixtures [25]. Silica fume is very effective to strength development both in the short and in the long term. In the case of 270 days, the highest strength is measured in f10s05 (17.5% increase for OPC result, w/b 0.37), g30s05 (16.7% increase for OPC result, w/b 0.42), and g30s05 (31.0% increase for OPC result, w/b 0.47). In many researches, the effect of silica fume is found to be considerable both to strength and to durability [26, 27].

(a) w/b 0.37 series

(b) w/b 0.42 series

(c) w/b 0.47 series

Figure 3: Strength development with admixtures and curing periods.

3.2.2. Apparent Diffusion Coefficient

In Table 4, the results of apparent diffusion coefficient are listed. The maximum and minimum results are measured in o100-47 (7.3 10⁻¹² m²/sec) and g30s05-37 (1.4 10⁻¹² m²/sec), respectively. The lower w/b ratio concrete has, the lower diffusion coefficients are measured. The mix proportions with mineral admixture have lower results than those with only OPC. Since the mix proportions with lower w/b ratio and large amount of binder have more hydrates amount and smaller porosity, penetration of chloride ion is impeded [7, 13, 15]. Concrete with FA can have large amount of hydrates due to pozzolan reaction and this leads low diffusion of chloride ion. In the case of GGBFS, low diffusion coefficients are measured due to the small porosity from latent hydraulic properties and chemical binding of chloride ion [5, 6, 28]. The comparisons of mineral admixture group with OPC series are shown in Figure 4.

Table 4: Results of apparent diffusion coefficient.

Mixture
Diffusion coefficient (m²/sec)

o100-37

o100-42

o100-47

g30o70-37

g30o70-42

g30o70-47

g50o50-37

g50o50-42

g50o50-47

f10o90-37

f10o90-42

f10o90-47

f20o80-37

f20o80-42

f20o80-47

f30o70-37

f30o70-42

f30o70-47

f10s05-37

f10s05-42

f10s05-47

f20s05-37

f20s05-42

f20s05-47

g30s05-37

g30s05-42

g30s05-47

g35f15-37

g35f15-42

g35f15-47

(a) OPC and slag mixture series with various w/b ratios

(b) OPC and fly ash series with various w/b ratios

(c) OPC and combined mixture series with various w/b ratios

Figure 4: Comparison of measured diffusion coefficient with OPC concrete.

In order to evaluate the relationship between strength and diffusion coefficient, linear regression analysis is performed and the results are shown in Figure 5 with test results.

Figure 5: Relationship between compressive strength and diffusion coefficient.

The regression results are listed in (7a)~(7d). Consider

where denotes the compressive strength (MPa) at days, is measured diffusion coefficient (×10¹² m²/sec). It is observed the gradients of (7a), (7b), (7c), and (7d) increase with ages and this shows higher strength is related with lower diffusion coefficient with aging.

3.2.3. NNA Application to Diffusion Coefficient

NNA technique is applied to simulation of diffusion coefficient and the results are compared with those from multiregression analysis. Seven mix components like w/b ratio, unit content of cement, GGBFS, FA, SF, sand, and coarse aggregate are considered as input neurons. Output neuron is fixed as apparent diffusion coefficient. MATLAB program is used for this regression analysis. Back propagation algorithm is adopted and Tan-Sigmoid function is used for transfer function among various functions like linear transfer and log-sigmoid [24]. Training number is set as 2,000 and the error to target convergence is set as 10⁻¹² for learning process. The number of neuron is only 7 so that the simulation is usually completed within 2,000 trials. The decrease in error with increasing epoch is shown in Figure 6.

Figure 6: Decrease in errors with increasing epochs.

In Table 5, the result from multiregression analysis is listed. From the analysis, the average of relative error is 19.8%, which is reasonable; however 70.6% of relative error is calculated in the case of g50o50-47.

Table 5: Result of multiregression analysis.

In Figure 7, the results from multiregression in Table 5 are compared with those from NNA and experiment. The results from NNA show more reasonable prediction with average relative error of 4.1%, which is very close to test results compared with 19.8% of average relative error from multiregression analysis. The comparisons of relative error from each technique are shown in Figure 8.

(a) OPC and GGBFS series

(b) FA series

(c) Combined series

Figure 7: Comparison with results from test, multiregression, and NNA.

Figure 8: Comparison of relative errors from NNA and linear regression analysis with averaged relative errors.

The chloride profiles based on the diffusion coefficient from NNA are compared with test results which were kept in submerged condition for 6 months in Figure 9. Concrete with lower w/b ratio and larger mineral admixture shows the more reduced chloride penetration. The proposed technique shows reasonable prediction for chloride penetration.

(a) Chloride profile in o100 series

(b) Chloride profile in g30o70 series

(c) Chloride profile in g50o50 series

(d) Chloride profile in f10o90 series

(e) Chloride profile in f20o80 series

(f) Chloride profile in f30o70 series

(g) Chloride profile in f10s05 series

(h) Chloride profile in f20s05 series

(i) Chloride profile in g30s05 series

(j) Chloride profile in g35f15 series

Figure 9: Comparison of chloride profile between NNA and test results.

4. Analysis Technique of Chloride Penetration with Time-Dependent Diffusion

4.1. Time-Dependent Diffusion of Chloride Ion

It is reported that chloride diffusion coefficient based on Fick’s 2nd law decreases with time [3, 8]. The governing equation for chloride penetration is listed in (8) and time-dependent diffusion coefficient is listed in (9) [3, 8, 16]. Consider

where, and are reference time (28 days) and diffusion coefficient at reference time, is time-dependent diffusion coefficient. is time exponent which is changed with type and amount of mineral admixtures [3, 16], which is defined as

where FA and SG denote the replacement ratio of fly ash and slag. For solving (8) with (9), numerical analysis like finite differential method should be employed, however if time term is fixed, averaged diffusion coefficient can be derived as (11a) and (11b) [29]. Consider

where is the time after which diffusion coefficient keeps almost constant and it is usually assumed as 30 years.

4.2. Chloride Penetration Analysis Using NNA and Time-Dependent Diffusion Coefficient

The diffusion coefficients from NNA are the results based on the test data which is obtained from 6 months submerged condition, so that they are converted to diffusion coefficient at the reference time (28 days). In Figure 10, analysis technique for chloride behavior using NNA is depicted.

Figure 10: Prediction of chloride penetration using NNA and time-dependent diffusion.

4.3. Comparison with Previous Test Results

In this section, the results from the proposed technique are compared with the previous test results of chloride profiles. In the previous test [28], two types of concrete (FA and OPC) were kept in 3.5% NaCl solution for 46 weeks. Table 6 shows the mix proportions [28].

Table 6: Mix proportions for verification (long-term submerged condition).

Type
w/b
Binder (kg/m³)
Sand (kg/m³)
Coarse aggregate (kg/m³)

C
FA

OPC 100%
38.1
449
0
616
1,050

OPC 80% and FA 20%
38.1
359
90
616
1,050

Conditions for analysis are listed in Table 7 and the analysis results are shown in Figure 11. From Figure 11, it is found that the obtained diffusion coefficient seems to be small but the results from the analysis reasonably agree with the previous chloride profiles.

Table 7: Analysis conditions for verification (submerged condition).

Type

from NNA

Surface chloride content

(m²/sec)
(10⁻¹² m²/sec)
(% of concrete wt.)

OPC 100%
0.2
2.509
2.912
0.709

OPC 80% and FA 20%
0.36
1.801
2.252
0.709

Figure 11: Chloride profile between results from test and this study.

Another verification is performed using the results from field investigation. In the previous research [28], the chloride profiles were obtained from RC columns after 1 and 10 years in submerged condition. Unfortunately, mix proportions could not be obtained but it was found that it was made up with OPC concrete and w/c (water to cement ratio) was 0.55. Conventional mix proportions are assumed as Table 8 based on the domestic typical mix proportions [30] and analysis conditions are listed in Table 9.

Table 8: Mix proportions for verification (field investigation).

Type
w/c
Cement

(kg/m³)
Sand

(kg/m³)
Coarse aggregate (kg/m³)

OPC 100%
55.0
352
653
1,173

Table 9: Analysis condition for verification (field investigation).

Type

from NNA

Surface chloride content

(m²/sec)
(m²/sec)
(% of concrete wt.)

OPC

100%
0.2
3.647
4.233
0.709

In Figure 12, chloride profiles from field investigation are compared with the results from this study. With elapsed time, chloride profile moves to inside of concrete and the proposed technique is evaluated to reasonably predict the chloride penetration.

Figure 12: Chloride profile between results from field investigation and this study.

This study extends the applicability of NNA which is limitedly utilized for concrete strength and mix proportions to the research on durability. Through learning and training of diffusion coefficient, target value (diffusion coefficient) can be simulated in a given mix proportions. However this technique has still limitation since NNA technique closely depends on data set for training. The data in this paper has limitary material properties like w/b (0.37~0.47) and diffusion coefficient (1.4~7.3 × 10⁻¹² m/sec²) so that it is necessary to extend the range for enhancing application. Various mix proportions with mineral admixtures and variability of surface chloride content will be considered for future study.

5. Conclusions

The conclusions evaluation technique of chloride penetration using apparent diffusion coefficient and neural network algorithm are as follows.

(1)Thirty mix proportions for HPC containing GGBFS, FA, and SF are prepared and apparent diffusion coefficients are obtained after 6-month submerged condition of NaCl 3.5%. Seven mix components (w/b, unit content of cement, GGBFS, FA, SF, and fine/coarse aggregate) are selected as neurons and NNA is applied to simulation of diffusion coefficient. The simulated data shows only 4.1% of relative error, which is very accurate compared with the results from multiregression analysis showing 19.8%.(2)Utilizing diffusion coefficient from NNA and time-dependent diffusion, chloride profiles are evaluated. From the comparison with results of long term submerging test and field investigation, the proposed technique is evaluated to reasonably predict the induced chloride profile.(3)The proposed technique is closely dependent on quantitative data set for training and learning. With more extended mix proportions and the related diffusion coefficients, this technique can be modified and more applicable to evaluation of chloride penetration.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This research was supported by Grant (Code 11-Technology Innovation-F04) from Construction Technology Research Program (CTIP) funded by Ministry of Land, Infrastructure, and Transport.

References

J. P. Broomfield, Corrosion of Steel in Concrete: Understanding, Investigation and Repair, EE & FN Spon, London, UK, 1997.

RILEM, “Durability design of concrete structures,” Report of RILEM technical committee 130-CSL, E&FN, 1994.

M. D. A. Thomas and E. C. Bentz, Computer Program for Predicting the Service Life and Life-Cycle Costs of Reinforced Concrete Exposed to Chlorides, Life 365 Manual, SFA, 2002.

CEB-FIP, “Model code for service life design,” International Federation for Structural Concrete (fib), Task Group 5.6, 2006

H.-W. Song, S.-W. Pack, C. H. Lee, and S. -J. Kwon, “Service life prediction of concrete structures under marine environment considering coupled deterioration,” Restoration of Buildings and Monuments, vol. 12, pp. 265–284, 2006.

K. Maekawa, T. Ishida, and T. Kishi, “Multi-scale modeling of concrete performance,” Journal of Advanced Concrete Technology, vol. 1, no. 2, pp. 91–126, 2003.

H.-W. Song, S.-J. Kwon, K.-J. Byun, and C.-K. Park, “A study on analytical technique of chloride diffusion considering characteristics of mixture design for high performance concrete using mineral admixture,” Journal of Korean Society of Civil Engineers, vol. 25, no. 1A, pp. 213–223, 2005.

S. J. Kwon, U. J. Na, S. S. Park, and S. H. Jung, “Service life prediction of concrete wharves with early-aged crack: probabilistic approach for chloride diffusion,” Structural Safety, vol. 31, no. 1, pp. 75–83, 2009.

S.-S. Park, S.-J. Kwon, and S.-H. Jung, “Analysis technique for chloride penetration in cracked concrete using equivalent diffusion and permeation,” Construction and Building Materials, vol. 29, pp. 183–192, 2012.

NORDTEST, “Chloride migration coefficient from non-steady-state migration experiments,” NT BUILD 492, 1999

L. Tang, Chloride Transport in Concrete, Publication P-96:6, Division of Building Materials, Chalmers University of Technology, Sweden, 1996.

S. Park, S. Kwon, S. H. Jung, and S. Lee, “Modeling of water permeability in early aged concrete with cracks based on micro pore structure,” Construction and Building Materials, vol. 27, no. 1, pp. 597–604, 2012.

K. Maekawa, T. Ishida, and T. Kishi, Multi-Scale Modeling of Structural Concrete, Tylor&Francis, London, UK, 1st edition, 2009.

L. Tang, “Electrically accelerated methods for determining chloride diffusivity in concrete-current development,” Magazine of Concrete Research, vol. 48, no. 176, pp. 173–179, 1996.

C. Arya, N. R. Buenfeld, and J. B. Newman, “Factors influencing chloride-binding in concrete,” Cement and Concrete Research, vol. 20, no. 2, pp. 291–300, 1990.

M. D. A. Thomas and P. B. Bamforth, “Modelling chloride diffusion in concrete effect of fly ash and slag,” Cement and Concrete Research, vol. 29, no. 4, pp. 487–495, 1999.

J. Wang, H. Ni, and J. He, “The application of automatic acquisition of knowledge to mix design of concrete,” Cement and Concrete Research, vol. 29, no. 12, pp. 1875–1880, 1999.

I.-C. Yeh, “Modeling of strength of high-performance concrete using artificial neural networks,” Cement and Concrete Research, vol. 28, no. 12, pp. 1797–1808, 1998.

J. A. Stegemann and N. R. Buenfeld, “Prediction of unconfined compressive strength of cement paste with pure metal compound additions,” Cement and Concrete Research, vol. 32, no. 6, pp. 903–913, 2002.

K.-B. Park, T. Noguchi, and J. Plawsky, “Modeling of hydration reactions using neural networks to predict the average properties of cement paste,” Cement and Concrete Research, vol. 35, no. 9, pp. 1676–1684, 2005.

H.-W. Song and S.-J. Kwon, “Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure,” Cement and Concrete Research, vol. 39, no. 9, pp. 814–824, 2009.

S. Kwon and H. Song, “Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling,” Cement and Concrete Research, vol. 40, no. 1, pp. 119–127, 2010.

W. McCulloch and W. Pitt, “A logical calculus of the ideas immanent,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, 1943.

H. Demuth and M. Beale, Neural Network Toolbox: User's Guide, The MathWorks, 1997.

A. M. Neville, Properties of Concrete, Longman, 4th and final edition, 1996.

H.-W. Song, J.-C. Jang, V. Saraswathy, and K.-J. Byun, “An estimation of the diffusivity of silica fume concrete,” Building and Environment, vol. 42, no. 3, pp. 1358–1367, 2007.

S. A. Khedr and M. N. Abou-Zeid, “Characteristics of silica-fume concrete,” Journal of Materials in Civil Engineering, vol. 6, no. 3, pp. 357–375, 1994.

S. Y. Jang, Modeling of chloride transport and carbonation in concrete and prediction of service life of concrete structures considering corrosion of steel reinforcement [Ph.D. thesis], Department of Civil Engineering, Seoul National University, Seoul, Republic of Korea, 2003.

E. Poulsen, “On a model of chloride ingress into concrete,” in Proceedings of the Nordic Mini-Seminar on Chloride Transport, pp. 1–8, Department of Building Materials, Chalmers University of Technology, Gothenburg, Sweden, 1993.

KREA-Korean Remicon Engineering Association, “Concrete Mix Proportions,” 2005, (Korean)