:: Artikel Jurnal :: Kembali

Artikel Jurnal :: Kembali

The data mining applied for the prediction of highway roughness due to overloaded trucks

Sigit P. Hadiwardoyo, Antonio Gomes Correia, Paulo Pereira, Paulo Cortez (Faculty of Engineering, Universitas Indonesia, 2015)

 Abstrak

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 veri?ed 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.

 Metadata

No. Panggil : UI-IJTECH 6:5 (2015)
Entri utama-Nama orang :
Subjek :
Penerbitan : Depok: Faculty of Engineering, Universitas Indonesia, 2015
Sumber Pengatalogan : LibUI eng rda
ISSN : 20869614
Majalah/Jurnal : International Journal of Technology
Volume : Vol. 6, No. 5, December 2015: Hal. 751-761
Tipe Konten : text
Tipe Media : unmediated
Tipe Carrier : volume
Akses Elektronik : https://doi.org/10.14716/ijtech.v6i5.1186
Institusi Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 4 R. Koleksi Jurnal
  • Ketersediaan
  • Ulasan
No. Panggil No. Barkod Ketersediaan
UI-IJTECH 6:5 (2015) 08-23-22071506 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 9999920530503