Determining the Relative Importance of Parameters Affecting Concrete Pavement Thickness | ||
| Journal of Rehabilitation in Civil Engineering | ||
| مقاله 5، دوره 3، شماره 1 - شماره پیاپی 5، اردیبهشت 2015، صفحه 61-73 اصل مقاله (825.7 K) | ||
| نوع مقاله: Regular Paper | ||
| شناسه دیجیتال (DOI): 10.22075/jrce.2015.358 | ||
| نویسندگان | ||
| Golamali Shafabakhsh* 1؛ Hossein Naderpour2؛ Reza Noroozi3 | ||
| 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 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Concrete pavement؛ pavement design؛ PCASE؛ Neural Network؛ relative importance | ||
| مراجع | ||
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