Development of a Decision Support System for Road Maintenance Scheduling

Akindele Opeyemi Areegbe; Abiodun Alani Ogunseye; Naheem Olakunle Adesina; Thomas Kokumo Yesufu

doi:https://doi.org/10.14445/22315381/IJETT-V50P223

Research Article | Open Access | Download PDF

Volume 50 | Number 1 | Year 2017 | Article Id. IJETT-V50P223 | DOI : https://doi.org/10.14445/22315381/IJETT-V50P223

Development of a Decision Support System for Road Maintenance Scheduling

Akindele Opeyemi Areegbe, Abiodun Alani Ogunseye, Naheem Olakunle Adesina, Thomas Kokumo Yesufu

Citation :

Akindele Opeyemi Areegbe, Abiodun Alani Ogunseye, Naheem Olakunle Adesina, Thomas Kokumo Yesufu, "Development of a Decision Support System for Road Maintenance Scheduling," International Journal of Engineering Trends and Technology (IJETT), vol. 50, no. 1, pp. 150-154, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V50P223

Abstract

The work developed and evaluated the performance of a vehicle vibration monitoring system for road maintenance scheduling. The developed system consist of an ADXL335 three-axis accelerometer to detect vehicle vibration; an Arduino Uno microcontroller board for data conditioning and storage; and a GPSMAP 78s Global Positioning System (GPS) receiver for obtaining the geographical coordinates of the location and the vehicle velocity. The developed system, attached to a test vehicle, samples the vehicle vibration signal, conditions, stores and sends the sampled data to a personal computer (PC) via a USB connection. The International Roughness Index (IRI) and Road Quality Index (RQI) of the test road sections were calculated from the standard deviation of the acquired vehicle vibration data with a MATLAB program running on the PC. The RQI result showed that the roads can be classified into excellent, smooth and rough road sections per selected road. For a selected road with five (5) classified sections having an overall “smooth” outlook, the highest and lowest value of RQIs were 1.691 and 1.436 respectively; for another selected road having an overall “rough” outlook with ten (10) classified sections, the highest and lowest RQIs were 1.940 and 0.000 respectively. The study concluded by developing a system that can be used to prioritize the maintenance of failing road sections. This can aid road maintenance agencies to schedule road maintenance work appropriately.

Keywords

Road Surface Roughness, Scheduling, Road Quality Index, Roughness index, microcontroller.

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