Date of Award

2018-01-01

Degree Name

Doctor of Philosophy

Department

Geological Sciences

Advisor(s)

Vladik Kreinovich

Second Advisor

Laura Serpa

Abstract

For national economy, it is very important to have a reliable infrastructure. Because of this, all over the world, new roads are constantly being built and old roads are being maintained and, if necessary, expanded and/or repaired. Building a good quality road is very expensive, it costs several million dollars per kilometer. It is therefore crucial to make sure that the newly built and newly repaired roads are sufficiently stiff - so that they can withstand the predicted volume of traffic for a sufficient number of years. Current methods of estimating the stiffness are time-consuming and labor-consuming. The most accurate technique is to take a sample from the compacted subgrade or base, bring it to the lab, and measure the mechanical parameters that characterize the corresponding stiffness - this takes days. Another possibility is to measure the road stiffness on-site. There are several different measuring techniques for such measurements, but they are all very labor-intensive and often take days to acquire and process the data. The main idea of intelligent compaction is to measure the roadâ??s mechanical properties while the road is being compacted, by placing accelerometers on the rollers and/or geophones (sensors for detecting ground movement) at different depths at several locations. The main challenge that prevents intelligent compaction from being a widely accepted road building technique is that the relation between the mechanical properties of the soil and the resulting accelerations is very complex, it is described by a system of dynamic non-linear partial differential equations. It is therefore desirable to determine the desired characteristic in real time, without the need to solve the corresponding system of partial differential equations. This is the main task that we perform in this study. In the process of implementing this task, we also solve several auxiliary tasks which can be used in a more general data processing setting.

Language

en

Provenance

Received from ProQuest

File Size

120 pages

File Format

application/pdf

Rights Holder

Afshin Gholamy

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