University of Twente Student Theses


Smartphones als meetinstrument voor langsonvlakheid

Lub, Robert-Jan (2016) Smartphones als meetinstrument voor langsonvlakheid.

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Abstract:This research is part of the exploration of the Asset Management Advisory Group of Arcadis towards the added value of Big Data for Asset Management. The objective of this research is to determine to which extent smartphones are able to measure the road roughness and whether they are able to replace existing road roughness measuring methods, considering the requirements of Rijkswaterstaat (RWS), or can be of other added value for the Asset Management process. The usage of RWS of the road roughness data is mapped by conducting expert interviews. It emerged that RWS uses road roughness data in the form of the Half-Car Roughness Index (HRI), along with four other parameters to prepare their Multiple Year Plan for Pavement Maintenance (MJPV). The HRI is measured with the Automatic Road Analyzer (ARAN). Based on the information needs of RWS that were indicated by the experts different criteria are formulated. During a test drive smartphone data is collected, which is processed using RoADS and then is assessed based on the criteria. As a reference framework for this analysis ARAN-data of RWS has been used. The findings of the analysis are listed below per criterion. 1. The location in the width of the road should be determined with an accuracy of which it is possible to determine with 95% certainty on which lane of the road the data has been collected. The distributions of the locating accuracy of the smartphones have been determined by comparing the coordinates of the smartphones with those of the ARAN. The highest certainty with which the collected smartphone data could be assigned to a lane is 83,33% for the OnePlus One and 91,01% for the S4 Mini. Therefore the smartphones do not meet the first criterion. 2. The location in the length of the road should be determined with a maximum standard deviation of 5 meters and must be determined within every 100 meters. The same distributions that were determined for the first criterion have been used here. The OnePlus One has a standard deviation of 2,33m and the S4 Mini 1,62m. The GPS signal of the smartphones have a frequency of 1 Hz, which means that a speed of over 360 km/h should be reached to have a measurement gap of over 100m between two points. Since this is not a realistic speed to be reached on the national RoADS, it is concluded that the second criterion is met. 3. The average HRI should be delivered in 100 meters segments with a maximum standard deviation of 0,1m/km. Prediction functions have been determined that use the classes that RoADS assigns to the collected smartphone data. The predictions based on class 1 have a standard deviation of 0,2362m/km and those based on class 3 of 0,2638m/km. In both cases the criteria is not met. Based on the results it is concluded that smartphones currently do not comply with the use of RWS to measure the road roughness and, therefore, are not able to replace the ARAN. By conducting a brainstorm session with Asset Management experts of Arcadis other possible areas of application were identified. These include the monitoring of road roughness in new construction projects, monitoring of road networks in developing countries, the use of RoADS for day-to-day pavement management, and better analysing the degradation process of road roughness by collecting more data.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Construction Management and Engineering MSc (60337)
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