Analysis of Traffic Speed Deflectometer (TSD) data on State Highways in Southland, New Zealand

Ford, Albie (2022) Analysis of Traffic Speed Deflectometer (TSD) data on State Highways in Southland, New Zealand. [USQ Project]

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The aim of this study was to determine using Traffic Speed Deflectometer data, sites on the Southland State Highway network that are in very poor condition and may require intervention in the form of pavement rehabilitation. These sites were to be compared with the latest forward works programme to see if the proposed sites aligned with calculated sites.

A literature review was undertaken on the subject to understand the previous research on: • The Southland province and State Highway network • Deflection Theory • Pavement deflection methods • Post processing of TSD data • Analysis of deflection data to inform condition

TSD data was sourced from the asset owner’s database and verified and validated to ensure the data could be relied upon. The data was then analysed using the computer software Mathworks MATLAB.

There was a large quantity of TSD data collected in 2015, 2016 and 2017. Only parts of the network were tested each year so the initial analysis aimed to compare the results in each year as well as comparing the results where there were multiple years of data which would provide an indication of the repeatability and therefore reliability of the testing. The analysis showed that the testing is repeatable.

The data was then converted to FWD equivalent data by using the methodology prescribed by the Road Controlling Authority Waka Kotahi. A tool was developed to convert the data using a MATLAB script rather than the provided excel spreadsheet which had limitations. The data was then converted into several pavement indices which were representative of condition within the various pavement layers. This was to determine if the network had underlying issues with various layers within the pavement. The analysis concluded that the upper pavement layers were the worst with pavement condition ratings improving with depth.

The surface deflection results were then modified using a rolling average algorithm to identify lengths of road in very poor condition that should have further investigation and exclude short, isolated lengths that could be remedied with heavy duty maintenance.

The TSD data was older than initially expected, so rather than comparing the results from the analysis with the forward works programme they were able to be compared with the actual pavement rehabilitations constructed since the testing was undertaken. The results indicated that only 59% of sites had poor deflections and that most of the sites weren’t in the locations of interest identified. There was 34km identified as very poor of which only 1.4km was rehabilitated.

The benefit of this research is the two key findings. Firstly, the current annual length of pavement rehabilitations is less than what is required for the network long term. There is a wave of potential rehabilitations in the short to medium term that exacerbates this issue. This requires confirmation with further TSD testing and investigation.

Secondly, there is an output of locations of interest that can be further investigated to determine if pavement rehabilitation is required. The tool created to calculate these locations of interest can be used with future TSD data collected in subsequent years. If appears that the sites completed are not the sites being identified by the algorithm. This further increases the risk of large lengths of road reaching their intervention level for pavement renewal in the form of rehabilitation.

This analysis also confirmed that the locations of interest were not spread out evenly over the network but that certain State Highways were overrepresented and should be investigated.

In addition, there are several MATLAB scripts and functions that provide methods of TSD analysis for any asset manager or practitioner in New Zealand.

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Item Type: USQ Project
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -)
Supervisors: Nataatmadja, Andreas
Qualification: Bachelor of Engineering (Civil)
Date Deposited: 19 Jun 2023 00:12
Last Modified: 20 Jun 2023 01:03
Uncontrolled Keywords: Traffic Speed Deflectometer; pavement rehabilitation

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