Theoretical development of sampling analysis for the monitoring of pavement quality

Theoretical development of sampling analysis for the monitoring of pavement quality

Noca, Walter (TU Delft Civil Engineering and Geosciences)

Erkens, Sandra (mentor)
Houben, Lambert (mentor)
Verlaan, Jules (mentor)

Delft University of Technology

2018-10-19

Schiphol Airport and Heijmans are working together on the renewal of the Schiphol runways pavements. Currently the top layer is covered with a synthetic antiskid material called ASK. But the strong weather limitations for the installation of this material have opened the door for alternatives. Heijmans has proposed an innovative asphalt mixture that is able to provide similar and in some cases better surface performances compared with the ASK. This asphalt mixture is called Flightflex R and is a stone matrix asphalt. Consequently it is affected by the variability of the construction process. This thesis project focuses on the analysis of the best quality control procedure for this asphalt. The pavement surface needs to meet specific requirements and it is of interest to define a sampling methodology for the evaluation of the Texture Depth (TD). In particular the research aims to define the minimum number of samples that provides the highest reliability for the definition of the Mean Texture Depth (MTD) of the surface. To achieve this goal a theoretical approach is adopted. Starting from the collection of a consistent number of samples, the properties of the surface are analysed. In this process it is of interest to define the influence of the construction process on the surface quality. The information obtained are used to simulate bigger surfaces on which different sampling methodologies are tested. In particular three different methodologies are analysed: the current methodology called CROW, a Uniform methodology and a random methodology called Hammersley methodology. Thorough testing these sampling methodologies on the simulated surfaces it is possible to evaluate the relative error between the MTD of the simulated surfaces and the MTD of the samples taken. A Monte Carlo type of approach helps to define precisely which methodology performs better. The one with the lowest relative error and minimum required number of samples will be considered the most efficient. The simulation of the surfaces and the analysis of the sampling process highlights a correlation between the manufacturing signature and some sampling methodologies. In case of a correlation the reliability of the methodology decreases. In particular the CROW and the Uniform methodology present a form of correlation and thus have a lower reliability. The Hammersley methodology aims to simulate a random selection of samples and for this reason it does not enter in correlation with the surface patterns. The three aforementioned methodologies are in the last part of the research applied on a 500 m long section of the runway Polderbaan at Schiphol Airport. Although the Uniform methodology is less reliable it provides a 1% of relative error with only 70 samples. The Hammersley instead needs 180 samples to reach the same relative error but with a higher reliability. The CROW is the least performing. In fact it has a lower reliability than the Uniform strategy and it needs 170 samples to reach 1% relative error. The research helps highlighting the correlation between the manufacturing signature left by the construction process and the sampling strategy adopted. It also highlights the fact that a random distribution escapes this correlation and provides more reliable results. To conclude, the companies are suggested to use the Uniform methodology in case of short time available for the quality control measurements. This comes with a lowest reliability that has to be accepted. But in case a high reliability is required and sufficient time is available, the Hammersley strategy is considered more appropriate.

pavement engineering
Schiphol
asset management
Texture depth
asphalt

http://resolver.tudelft.nl/uuid:1dbebfc7-93a4-47f1-a9f5-3055879897e2

Student theses

master thesis

© 2018 Walter Noca