Volume 19, Issue 4 (2019)
MCEJ 2019, 19(4): 135-144 |
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.Kavussi A. Prediction model of remaining service life of pavements using effective parameters. MCEJ 2019; 19 (4) :135-144
URL: http://mcej.modares.ac.ir/article-16-37918-en.html
URL: http://mcej.modares.ac.ir/article-16-37918-en.html
A .Kavussi * 
1
1
1- , kavussia@modares.ac.ir
Abstract: (4247 Views)
A number of pavement condition assessment methods are used to perform pavement condition evaluation. Two of the most widely used methods are determination of “International Roughness Index (IRI)” and “Pavement Condition Index (PCI)” parameters. IRI is measured on roads using specific equipment that determine road roughness. In contrast, PCI is based on subjective rating of a number of pavement distresses. Road pavement structures very often could not reach their design service life as a result of several parameters affecting their performance. PCI decreases as a result of increased traffic loading. Aside from the impact of traffic loading, many other factors cause damage to pavements; namely, low construction quality, poor maintenance, flooding and water scouring. As a result of many factors causing damage to roads, road serviceability age uncertainties arise, so that the remaining life of a pavement service life will be difficult to predict.
Determining the structural capacity and surface pavement condition of pavements play important roles in pavement evaluation. With this regard, it is important to find relationship between surface characteristics and structural capacity of pavements. Therefore, finding a reliable model for surface characteristics and structural capacity will be beneficial. Structural evaluation of pavements is carried out by non-destructive testing. FWD is one of the most important non-destructive testing of pavements, although performing that is costly and time-consuming. On the other hand, the remaining service life of pavements is one of the most important parameters for the structural assessment of pavements. This is essential for assessment of requirements, maintenance and rehabilitation, prioritization and budgeting purposes. The lack of precise evaluation of remaining service life of pavements is a crucial issue. In addition, lack of a proper performance prediction model is a major barrier to predict the remaining service life of pavements. The combined analysis of FWD testing results and PCI measurements can be known as an appropriate solution for determining RSL. With selecting a series of independent variables reliable correlation was found between RSL and independent variables. RSL can be used with other indexes and parameters that make their data capturing easier and less costly. In addition, using this model enabled finding correlation and interaction between different variables
Determining the structural capacity and surface pavement condition of pavements play important roles in pavement evaluation. With this regard, it is important to find relationship between surface characteristics and structural capacity of pavements. Therefore, finding a reliable model for surface characteristics and structural capacity will be beneficial. Structural evaluation of pavements is carried out by non-destructive testing. FWD is one of the most important non-destructive testing of pavements, although performing that is costly and time-consuming. On the other hand, the remaining service life of pavements is one of the most important parameters for the structural assessment of pavements. This is essential for assessment of requirements, maintenance and rehabilitation, prioritization and budgeting purposes. The lack of precise evaluation of remaining service life of pavements is a crucial issue. In addition, lack of a proper performance prediction model is a major barrier to predict the remaining service life of pavements. The combined analysis of FWD testing results and PCI measurements can be known as an appropriate solution for determining RSL. With selecting a series of independent variables reliable correlation was found between RSL and independent variables. RSL can be used with other indexes and parameters that make their data capturing easier and less costly. In addition, using this model enabled finding correlation and interaction between different variables
Article Type: Original Research |
Subject:
Geotechnic
Received: 2019/10/31 | Accepted: 2019/10/31 | Published: 2019/10/31
Received: 2019/10/31 | Accepted: 2019/10/31 | Published: 2019/10/31
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