1Associate Professor, Department of Civil Engineering, Semnan University, Semnan, Iran
2Assistant Professor, Department of Civil Engineering, Semnan University, Semnan, Iran
3Ph.D. student, Department of Civil Engineering, Semnan University, Semnan, Iran
Spending costs in construction of road pavements has turned this subject into one of the significant points in transportation infrastructure of countries. Concrete slabs consider as a paving method with ability of reducing the rehabilitation needs. Therefore, to manage costs and optimize the thickness of concrete pavements, recognizing the amount of determinative factors’ influence will be required. A study with the aim of determining the influence of traffic parameters, type of subgrade soil and the base layer thickness on the concrete pavement slab thickness can provide the choice of best concrete pavement design. For this purpose, the PCASE software has been used in this paper to construct sufficient number of numerical examples, 288 specimens, with taking into account the number of equivalent single axle, the subgrade modulus of concrete pavement construction place and the base layer thickness. These samples are considered as the basis of training and testing an artificial neural network and the level of pavement design parameters importance is relatively determined on the results of optimal neural network. The method used in this paper for calculating the relative importance of each parameter involved in the concrete pavement thickness indicates that the parameters of base layer thickness and the number of equivalent single axle have the lowest and highest level of influence, with the values of about 21 and 42 percent, respectively. The obtained results are also compatible with concepts and structural features of concrete pavements.
 Fogg, J. A., Baus, R. L., Ray, R. P. (1991). “AASHTO rigid pavement design equation study”, J. Transp. Eng., vol. 117, no. 1, pp. 124–131.
 Guclu, A., Ceylan, H. (2005). “Sensitivity Analysis of Rigid Pavement Systems Using Mechanistic-Empirical Pavement Design Guide”, in Proceedings of the 2005 Mid-Continent Transportation Research Symposium, Ames, Iowa.
 Southgate, H. F. (1988). “Comparison of rigid pavement thickness design systems”, Research Report UKTRP-88-14, Lexington, Kentuchy.
 Jersey, S. R., Bell, H. P. (2011). “Analyses of Structural Capacity of Rigid Airfield Pavement Using Portable Seismic Technology”, Int. J. Pavement Res. Technol., vol. 4, no. 3, pp. 147–153.
 Taghavi Esfandani, M., Mansourian, A., Babaei, A. (2013). “Investigation of Runway Pavement Design Software and Determination of Optimization Software”, J. Basic Appl. Sci. Res., vol. 3, no. 4, pp. 143–150.
 Kannekanti, V., Harvey, J. (2006). “Sensitivity analysis of 2002 design guide distress prediction models for jointed plain concrete pavement”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1947, no. 1, pp. 91–100.
 Hall, K. D., Beam, S. (2005). “Estimating the sensitivity of design input variables for rigid pavement analysis with a mechanistic-empirical design guide”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1919, no. 1, pp. 65–73.
 Khazanovich, L., Darter, M. I., Yu, H. T. (2004). “Mechanistic-empirical model to predict transverse joint faulting”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1896, no. 1, pp. 34–45.
 Mallela, J., Abbas, A., Harman, T., Rao, C., Liu, R., Darter, M. I. (2005). “Measurement and significance of the coefficient of thermal expansion of concrete in rigid pavement design”, Transp. Res. Rec. J. Transp. Res. Board, vol. 1919, no. 1, pp. 38–46.
 Attoh-Okine, N. O., Cooger, K., Mensah, S. (2009). “Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling”, Constr. Build. Mater., vol. 23, no. 9, pp. 3020–3023.
 Bayrak, M. B., Ceylan, H. (2008). “Neural network-based approach for analysis of rigid pavement systems using deflection data”, Transp. Res. Rec. J. Transp. Res. Board, vol. 2068, no. 1, pp. 61–70.
 Ceylan, H., Gopalakrishnan, K., Lytton, R. L. (2010). “Neural networks modeling of stress growth in asphalt overlays due to load and thermal effects during reflection cracking”, J. Mater. Civ. Eng., vol. 23, no. 3, pp. 221–229.
 Ceylan, H., Gopalakrishnan, K. (2007). “Neural Networks Based Models for Mechanistic-Empirical Design of Rubblized Concrete Pavements”, Geotech. Spec. Publ. No. 169, Soil Mater. Inputs Mech. Pavement Des. ASCE, pp. 1–10.
 Gopalakrishnan, K. (2010). “Effect of training algorithms on neural networks aided pavement diagnosis”, Int. J. Eng. Sci. Technol., vol. 2, no. 2, pp. 83–92.
 Sharma, S., Das, A. (2008). “Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network”, Can. J. Civ. Eng., vol. 35, no. 1, pp. 57–66.
 Kisi, O. (2005). “Daily river flow forecasting using artificial neural networks and auto-regressive models”, Turkish J. Eng. Environ. Sci., vol. 29, no. 1, pp. 9–20.
 Olden, J., Jackson, D. (2002). “Illuminating the ‘black box’: a randomization approach for understanding variable contributions in artificial neural networks”, Ecol. Modell., vol. 154, no. 1–2, pp. 135–150.
 Dimopoulos, Y., Bourret, P., Lek, S. (1995). “Use of some sensitivity criteria for choosing networks with good generalization ability”, Neural Process. Lett., vol. 2, no. 6, pp. 1–4.
 Gevrey, M., Dimopoulos, I., Lek, S. (2003). “Review and comparison of methods to study the contribution of variables in artificial neural network models”, Ecol. Modell., vol. 160, no. 3, pp. 249–264.
 Scardi, M., Harding Jr, L. W. (1999). “Developing an empirical model of phytoplankton primary production: a neural network case study”, Ecol. Modell., vol. 120, no. 2, pp. 213–223.
 Garson, G. D. (1991). “Interpreting neural-network connection weights”, AI Expert, vol. 6, no. 4, pp. 46–51.
 Flood, I., Kartam, N. (1994). “Neural networks in civil engineering. I: Principles and understanding”, J. Comput. Civ. Eng., vol. 8, no. 2, pp. 131–148, 1994.
 Adeli, H. (2001). “Neural Networks in Civil Engineering: 1989-2000”, Comput. Civ. Infrastruct. Eng., vol. 16, no. 2, pp. 126–142, Mar. 2001.
 McCulloch, W. S., Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity”, Bull. Math. Biophys., vol. 5, no. 4, pp. 115–133, 1943.
 Chen, D. G., Ware, D. M. (1999). “A neural network model for forecasting fish stock recruitment”, Can. J. Fish. Aquat. Sci., vol. 56, no. 12, pp. 2385–2396.
 Manel, S. S. , Dias, J.-M., Ormerod, S. J. (1999). “Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird”, Ecol. Modell., vol. 120, no. 2, pp. 337–347.
 Özesmi, S. L., Özesmi, U. (1999). “An artificial neural network approach to spatial habitat modelling with interspecific interaction”, Ecol. Modell., vol. 116, no. 1, pp. 15–31.
 Paruelo, J., Tomasel, F. (1997). “Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models”, Ecol. Modell., vol. 98, no. 2, pp. 173–186.
 Spitz, F., Lek, S. (1999). “Environmental impact prediction using neural network modelling. An example in wildlife damage”, J. Appl. Ecol., vol. 36, no. 2, pp. 317–326.
 Mural, R. V., Puri, A. B., Prabhakaran, G. (2010). “Artificial neural networks based predictive model for worker assignment into virtual cells”, Int. J. Eng. Sci. Technol., vol. 2, no. 1, pp. 163–174.
 Haykin, S. (1999). “Neural networks: a comprehensive foundation 2nd edition”, Up. Saddle River, NJ, US Prentice Hall.
 Melesse, A. M., Hanley, R. S. (2005). “Artificial neural network application for multi-ecosystem carbon flux simulation”, Ecol. Modell., vol. 189, no. 3, pp. 305–314.
 Bilgili, M., Sahin, B., Yasar, A. (2007). “Application of artificial neural networks for the wind speed prediction of target station using reference stations data”, Renew. Energy, vol. 32, no. 14, pp. 2350–2360.
 Bishop, C. M. (1995). "Neural networks for pattern recognition", vol. 92, no. 440. Clarendon press Oxford, p. 498.
 Dimopoulos, I., Chronopoulos, J., Chronopoulou-Sereli, A., Lek, S. (1999). “Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece)”, Ecol. Modell., vol. 120, no. 2, pp. 157–165.
 Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., Aulagnier, S. (1996). “Application of neural networks to modelling nonlinear relationships in ecology”, Ecol. Modell., vol. 90, no. 1, pp. 39–52.
 Abrahart, R. J., See, L., Kneale, P. E. (2001). “Investigating the role of saliency analysis with a neural network rainfall-runoff model”, Comput. Geosci., vol. 27, no. 8, pp. 921–928.
 Makarynskyy, O., Makarynska, D., Kuhn, M., Featherstone, W. E. (2004). “Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia”, Estuar. Coast. Shelf Sci., vol. 61, no. 2, pp. 351–360.
 Montano, J., Palmer, A. (2003). “Numeric sensitivity analysis applied to feedforward neural networks”, Neural Comput. Appl., vol. 12, no. 2, pp. 119–125.
 Elmolla, E. S., Chaudhuri, M., Eltoukhy, M. M. (2010). “The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process”, J. Hazard. Mater., vol. 179, no. 1, pp. 127–134.