Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.722942
Title: Road and soil dynamic characterization from surface measurements
Author: Iodice, Michele
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
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Abstract:
The increased demand for non-destructive evaluation of the shear wave profiling, condition monitoring and performance assessment of soils and roads in a dynamic state has made seismic methods the most desirable and effective non-destructive techniques. Surface wave methods have gained popularity over the last decades since they monitor the propagation of the surface wave with non-invasive transducers working from the surface. Nonetheless, their use is restricted by resolution problems and their ability to assess the actual dispersive behaviour of Rayleigh wave. Non-destructive, in-situ methods for characterizing existing infrastructures require the ability to detect structural damage and features such as cracking and discontinuities. The proper assessment of the location and of the extension of such discontinuities is crucial for the determination of the level of deterioration of an infrastructure and for planning the maintenance interventions. Damage in a pavement structure is usually initiated in the asphalt layers, making the Rayleigh wave ideally suited for the detection of shallow surface defects. Nonetheless, the practical application of crack detection methods in asphalt is hampered due to the heterogeneous and dispersive nature of the material tested. This thesis describes new signal processing methods and the novel application of existing methods to tackle the problems that hinder the non-destructive surface wave methods. The spectral convolution method proposed in this thesis, based on the simultaneous exploitation of the vertical and the horizontal components of a seismic event, improved the resolution and the overall accuracy of the spectral image in the frequency-wavenumber (f-k) domain. Hence, it led to more accurate seismic inversion by reducing the amount of uncertainty coming from a seismic survey. This research investigated the use of this new proposed method in soils and asphalts for the measurement of surface wave dispersion through conceptual analysis and numerical investigation alongside experimental investigation on soil and asphalt. The application of spectral wave methods and the transformation of the wave field into the frequency wave number domain allowed the identification of the position and the extension of vertical defects. The joint use of the Multichannel Analysis of Surface Waves and the Multiple Impact of Surface Waves methods in numerical and experimental investigations presented in this thesis proved to be effective for crack detection and sizing. Moreover, the space-normalised seismogram helped in the interpretation of f-k spectra for cracks detection. Here, the wave decomposition method for crack interrogation consisted of a signal processing algorithm capable of computing the direct and reflected waves’ amplitudes and phase angles from the signals of a deployment of sensors. It solved linear systems with a number of measurements much bigger than the number of unknowns. It tackled the measurement errors naturally present in experimental data by finding a least square approximate solution with the help of the pseudo-inverse matrix for over determined systems. The results coming from numerical simulations and experimental investigations showed the effectiveness of the wave decomposition method for the assessment of the location and of the depth of surface-breaking cracks in the half-space and in layered systems. Contrary to other techniques, it was able to cope with the heterogeneities and the dispersive nature of layered system, thus making possible to detect and assess the depth of surface-breaking cracks in roads.
Supervisor: Rustighi, Emiliano Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.722942  DOI: Not available
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