doktorska disertacija
Patricia Cotič (Author), Zvonko Jagličić (Mentor), Matjaž Mikoš (Thesis defence commission member), Janez Dolinšek (Thesis defence commission member), Ernst Niederleithinger (Thesis defence commission member), Mitja Lakner (Thesis defence commission member), Goran Turk (Thesis defence commission member), Vlatko Bosiljkov (Co-mentor)

Abstract

V disertaciji predstavimo uporabo postopka za združevanje slik večsenzorskih neporušnih preiskav, ki temelji na metodah gručenja. Za združevanje na nivoju posamezne slikovne točke in z uporabo značilnic analiziramo algoritme trdega in mehkega gručenja. Sintezo georadarskih in termografskih podatkov opravimo na rezultatih preiskav betonskih preizkušancev z vgrajenimi anomalijami ter na rezultatih preiskav zidovja s prisotnimi razpokami in odstopanjem ometa zaradi delovanja strižne obremenitve. Na betonskih preizkušancih najboljše deluje algoritem gručenja GK, ker prepoznava razrede gručenja poljubne oblike in ne le sferične kot algoritma FCM in PFCM. V primerih s še posebno omejenim vedenjem o materialnih lastnostih in globini anomalij združevanje z uporabo metod gručenja deluje bolje kot metode nadzorovanega združevanja podatkov. Na večslojnih kamnitih zidovih lahko z uporabo georadarja in infrardeče termografije zaznamo morfologijo in teksturo zidov ter odstopanje ometa in nastanek razpok. Za izboljšano zaznavanje odstopanja ometa in razpok predlagamo združevanje podatkov na nivoju posamezne slikovne točke za segmentacijo slik. Z georadarsko, ultrazvočno in geoelekrično tomografijo opravimo na zidovju tudi raziskavo vpliva stopnje vlažnosti zidovja na občuljivost neporušnih metod. Metode gručenja uporabimo tudi za združevanje neporušnih podatkov navigacijskega večsenzorskega robotnega sistema. Poleg tega izvedemo tudi klasifikacijo spektroskopskih podatkov betonskih preizkušancev. V obeh primerih metode gručenja uporabimo za segmentacijo podatkov.

Keywords

Grajeno okolje;gradbeništvo;disertacije;neporušne preiskave;georadar;infrardeča termografija;ultrazvočna metoda;kompleksno-uporovna metoda;beton;zidovje;združevanje podatkov;združevanje slik;metode gručenja;

Data

Language: Slovenian
Year of publishing:
Source: Ljubljana
Typology: 2.08 - Doctoral Dissertation
Organization: UL FGG - Faculty of Civil and Geodetic Engineering
Publisher: [P. Cotič]
UDC: 551.509.313:624.012.82:624.012.4:(043.3)
COBISS: 6614113 Link will open in a new window
Views: 1877
Downloads: 464
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Other data

Secondary language: English
Secondary title: ǂThe ǂsynthesis of multisensor non-destructive testing of civil engineering structural elements with the use of clustering methods
Secondary abstract: In the thesis, clustering-based image fusion of multi-sensor non-destructive (NDT) data is studied. Several hard and fuzzy clustering algorithms are analysed and implemented both at the pixel and feature level fusion. Image fusion of ground penetrating radar (GPR) and infrared thermography (IRT) data is applied on concrete specimens with inbuilt artificial defects, as well as on masonry specimens where defects such as plaster delamination and structural cracking were generated through a shear test. We show that on concrete, the GK clustering algorithm exhibits the best performance since it is not limited to the detection of spherical clusters as are the FCM and PFCM algorithms. We also prove that clustering-based fusion outperforms supervised fusion, especially in situations with very limited knowledge about the material properties and depths of the defects. Complementary use of GPR and IRT on multi-leaf masonry walls enabled the detection of the walls’ morphology, texture, as well as plaster delamination and structural cracking. For improved detection of the latter two, we propose using data fusion at the pixel level for data segmentation. In addition to defect detection, the effect of moisture is analysed on masonry using GPR, ultrasonic and complex resistivity tomographies. Within the thesis, clustering is also successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system. Besides, the classification of spectroscopic spatial data from concrete is taken under consideration. In both applications, clustering is used for unsupervised segmentation of data.
Secondary keywords: Built Environment;civil engineering;doctoral thesis;non-destructive testing;ground penetrating radar;infrared thermography;ultrasonic;complex resistivity;concrete;masonry;data fusion;image fusion;clustering methods;
URN: URN:NBN:SI
File type: application/pdf
Type (COBISS): Doctoral dissertation
Thesis comment: Univ. v Ljubljani, Fak. za gradbeništvo in geodezijo
Pages: XXX, 100 str., [53] str. pril.
Type (ePrints): thesis
Title (ePrints): Sinteza večsenzorskih neporušnih preiskav gradbenih konstrukcijskih elementov z uporabo metod gručenja
Keywords (ePrints): neporušne preiskave;gradbeništvo;georadar;infrardeča termografija;ultrazvočna metoda;kompleksno-uporovna metoda;beton;zidovje;združevanje podatkov;združevanje slik;metode gručenja
Keywords (ePrints, secondary language): non-destructive testing;civil engineering;ground penetrating radar;infrared thermography;ultrasonic;complex resistivity;concrete;masonry;data fusion;image fusion;clustering methods
Abstract (ePrints): V disertaciji predstavimo uporabo postopka za združevanje slik večsenzorskih neporušnih preiskav, ki temelji na metodah gručenja. Za združevanje na nivoju posamezne slikovne točke in z uporabo značilnic analiziramo algoritme trdega in mehkega gručenja. Sintezo georadarskih in termografskih podatkov opravimo na rezultatih preiskav betonskih preizkušancev z vgrajenimi anomalijami ter na rezultatih preiskav zidovja s prisotnimi razpokami in odstopanjem ometa zaradi delovanja strižne obremenitve. Na betonskih preizkušancih najboljše deluje algoritem gručenja GK, ker prepoznava razrede gručenja poljubne oblike in ne le sferične kot algoritma FCM in PFCM. V primerih s še posebno omejenim vedenjem o materialnih lastnostih in globini anomalij združevanje z uporabo metod gručenja deluje bolje kot metode nadzorovanega združevanja podatkov. Na večslojnih kamnitih zidovih lahko z uporabo georadarja in infrardeče termografije zaznamo morfologijo in teksturo zidov ter odstopanje ometa in nastanek razpok. Za izboljšano zaznavanje odstopanja ometa in razpok predlagamo združevanje podatkov na nivoju posamezne slikovne točke za segmentacijo slik. Z georadarsko, ultrazvočno in geoelekrično tomografijo opravimo na zidovju tudi raziskavo vpliva stopnje vlažnosti zidovja na občuljivost neporušnih metod. Metode gručenja uporabimo tudi za združevanje neporušnih podatkov navigacijskega večsenzorskega robotnega sistema. Poleg tega izvedemo tudi klasifikacijo spektroskopskih podatkov betonskih preizkušancev. V obeh primerih metode gručenja uporabimo za segmentacijo podatkov.
Abstract (ePrints, secondary language): In the thesis, clustering-based image fusion of multi-sensor non-destructive (NDT) data is studied. Several hard and fuzzy clustering algorithms are analysed and implemented both at the pixel and feature level fusion. Image fusion of ground penetrating radar (GPR) and infrared thermography (IRT) data is applied on concrete specimens with inbuilt artificial defects, as well as on masonry specimens where defects such as plaster delamination and structural cracking were generated through a shear test. We show that on concrete, the GK clustering algorithm exhibits the best performance since it is not limited to the detection of spherical clusters as are the FCM and PFCM algorithms. We also prove that clustering-based fusion outperforms supervised fusion, especially in situations with very limited knowledge about the material properties and depths of the defects. Complementary use of GPR and IRT on multi-leaf masonry walls enabled the detection of the walls’ morphology, texture, as well as plaster delamination and structural cracking. For improved detection of the latter two, we propose using data fusion at the pixel level for data segmentation. In addition to defect detection, the effect of moisture is analysed on masonry using GPR, ultrasonic and complex resistivity tomographies. Within the thesis, clustering is also successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system. Besides, the classification of spectroscopic spatial data from concrete is taken under consideration. In both applications, clustering is used for unsupervised segmentation of data.
Keywords (ePrints, secondary language): non-destructive testing;civil engineering;ground penetrating radar;infrared thermography;ultrasonic;complex resistivity;concrete;masonry;data fusion;image fusion;clustering methods
ID: 8327204