Vol 6, No 5 (2015) > Civil Engineering >

The Data Mining Applied for the Prediction of Highway Roughness due to Overloaded Trucks

Andri Irfan Rifai, Sigit P. Hadiwardoyo, Antonio Gomes Correia, Paulo Pereira, Paulo Cortez

 

Abstract:

Currently, the quality
of the Indonesian national road network is inadequate due to several constraints,
including overcapacity and overloaded trucks. The high deterioration rate of
the road infrastructure in developing countries along with major budgetary
restrictions and high growth in traffic have led to an emerging need for
improving the performance of the highway maintenance system. However, the high
number of intervening factors and their complex effects require advanced tools
to successfully solve this problem. The high learning capabilities of Data
Mining (DM) are a powerful solution to this problem. In the past, these tools
have been successfully applied to solve complex and multi-dimensional problems
in various scientific fields. Therefore, it is expected that DM can be used to
analyze the large amount of data regarding the pavement and traffic, identify
the relationship between variables, and provide information regarding the
prediction of the data. In this paper, we present a new approach to predict the
International Roughness Index (IRI) of pavement based on DM techniques. DM was
used to analyze the initial IRI data, including age, Equivalent Single Axle
Load (ESAL), crack, potholes, rutting, and long cracks. This model was
developed and verified using data from an Integrated Indonesia Road Management
System (IIRMS) that was measured with the National Association of Australian
State Road Authorities (NAASRA) roughness meter. The results of the proposed
approach are compared with the IIRMS analytical model adapted to the IRI, and
the advantages of the new approach are highlighted. We show that the novel
data-driven model is able to learn (with high accuracy) the complex
relationships between the IRI and the contributing factors of overloaded trucks.

Keywords: Data mining; Overload; Pavement maintenance; Pavement roughness; Prediction

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