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Utilization of neural networks to expand databases
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U t i l i z a t i o n o f n e u r a l n e t w o r k s t o e x p a n d
d a t a b a s e s
N R C C - 4 9 2 4 8
K h o g a l i , W . E . I . ; Z e g h a l , M .
A version of this document is published in / Une version de ce document se trouve dans: CANCAM 2007, Toronto, Ontario, June 3-7, 2007, pp. 742-743
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Utilization of Artificial Neural Networks to Expand Databases
W.E.I. Khogali1, M. Zeghal21
National Research Council Canada, Walaa.Khogali@nrc.ca
2
National Research Council Canada, Morched.Zeghal@nrc.ca
1. Abstract
This paper examines the potential use of the artificial neural networks (ANN) technique as a tool for expanding databases. The application presented here targets databases that house mechanistic properties of aggregate materials, commonly used in base and subbase road layers. The research work performed involved the development and utilization of an ANN model to populate a resilient modulus database of granular materials from limited laboratory-generated data. Resilient moduli predicted by the ANN model under a variety of test conditions, including density, stress and moisture content demonstrated consistent trends of known effects that these input parameters have on the resilient response (the output). Results obtained from expanding the original database to include conditions that are not covered in laboratory tests confirmed the ability of the ANN model to produce reliable estimates of the required output.
2. Introduction
Mechanistic–empirical pavement design procedures require the provision of specific material inputs as part of the design process. One such input is the resilient modulus of aggregate materials (Mr) commonly used in
base and subbase layers. The Mr parameter is defined as
the ratio of the deviator stress (σd) to the resilient
(recoverable) strain (εr): d r r M σ ε = ...(1) The determination of the Mr parameter involves
elaborate laboratory testing and special training of technical staff, a capability that the majority of road jurisdictions in Canada lack today. One alternative to circumvent such problem and to provide the needed parameter is to perform limited Mr tests at labs that
posess the required testing capability and then use an analytical approach to expand the original database. This paper presents an attempt to utilize the ANN technique to accomplish the stated objective. The laboratory data set used in the current study included Mr entries that were obtained under different conditions
of density (89–98% Modified Proctor density), moisture content (3–7%, representing 2.5% dry of optimum to 1.5% wet of optimum) and applied deviator
stress (35–85 kPa). These factors are known to afftect the resilient behaviour of aggregate materials [1], [2].
3. Features of ANN Model
The ANN model developed in this study has three input parameters, namely, the compaction density, moisture content and deviator stress, and the output was the Mr
parameter. The optimized network, which had a single hidden layer with 12 nodes, was trained using the back propagation technique [3]. The criterion adopted for assessing the network prediction capability was the percentage “Absolute value of the Relative Error” (ARE):
(
)
100% predicted actual actual x x ARE x − = ...(2) Using the ARE criterion, the ANN model produced acceptable predictions that deviated by less than 10% from actual laboratory results.4. Expanding Database
The trained ANN was used to expand the original database, containing 55 test results, to produce more than 1000 data entries covering the ranges of input variables in increments of 1% for compaction density, 0.5% for moisture content and 5 kPa for deviator stress. An example illustrating the population of the database for a fixed moisture content of 5.0% and covering the full range of density (89–98%) under two stress levels (35 and 45 kPa) is displayed in Table 1. The values highlighted in the table are the original laboratory results used in this example.
Predictions made by the ANN model are plotted in Figures 1–3. Figure 1 displays Mr values obtained at
different densities for the material compacted at its optimum moisture content (5.3%) and tested using a stress level of 50 kPa. The trend, which shows an increase in Mr as the density increases, agrees with
known material behaviour as established in the literature [2]. Figure 2 shows the relationship between predicted Mr values and the material moisture content.
A decreasing trend characterizing this relationship is again indicative of known material behaviour. Comparison between Mr values obtained under two
different levels of deviator stress is shown in Figure 3. The rate at which the modulus increases with density 1
was found to be higher for the higher stress level, an observation that is reported in the literature. In addition, increase in density beyond 98% causes insignificant changes in the modulus value. Findings from Figures 1–3 demonstrate the ability of the ANN model to effectively capture known relationships that exist in the original database between input and output.
Table 1. Expanding original database using ANN Stress level (kPa) Compaction Density (% of MMPD*) Resilient modulus (MPa) 35 90 140 91 146 92 151 93 156 94 159 95 163 96 167 97 173 98 178 99 180 35 100 180 90 136 91 138 92 145 93 156 94 170 95 184 96 199 45 97 212 45 98 222 99 224 45 100 226
*MMPD: Maximum modified Proctor density
120 150 180 210 240 270 88 90 92 94 96 98 100 Density (% of maximum modified Proctor density)
Resilient modulus (MPa)
Stresss = 50 kPa M.C = 5.3%
Figure 1.Variation of Mr with density 5. Summary and Conclusions
Mechanistic-empirical pavement design methods require the provision of the resilient modulus as an input to characterize aggregate materials used in road construction. However, due to the elaborate nature of the test procedure used to determine this parameter, an
alternative approach is required. This paper presented the use of ANN technique as a tool to expand Mr
databases that were created from limited laboratory tests. Findings from the research study demonstrated the ability of the developed ANN model to generate reliable estimates of the resilient modulus parameter and to effectively populate the original database.
50 80 110 140 170 200 230 260 2 3 4 5 6 7 8 Moisture content (%)
Resilient modulus (MPa)
Density = 90%MPD Stresss = 50kPa
Figure 2. Variation of Mr with moisture content
120 140 160 180 200 220 240 88 90 92 94 96 98 100 102 Density (% of maximum Proctor density)
Resilient modulus (MPa)
35 kPa 45 kPa
M.C. = 5.0%
Figure 3. Variation of Mr with density and stress References
[1] A.R. Dawson, N.H. Thom and J.L. Paute,
Mechanical Characteristics of Unbound Granular Materials as a Function of Condition, Flexible
Pavements, Proc. Euro. Symp. Euroflex. 199, A. G. Correia, ed., Balkema, Rotterdam, The Netherlands, 35-44, (1996).
[2] W.E.I. Khogali and ElHussein H. Mohamed, Novel
Approach for Characterization of Unbound Materials, In Transportation Research Record No.
1874, pp. 38 – 46, Journal of the Transportation Research Board, (2004). AASHTO (1986). “Guide for Design of Pavement Structures.” American Association of State Highway and Transportation Officials.
[3] I.A. Basheer and M. Hajmeer, Artificial Neural
Networks: Fundamentals, Computing, Design, and Application, Journal of Microbiological Methods,
No. 43, pp. 3–31 (2000).
