Infrastructures like bridges and tunnels are significant for the economy and growth of countries, however, the risk of failure increases as they getting aged. Therefore, a systematic monitoring scheme is necessary to check the integrity regularly. Among all the defects, cracks are the most common ones that can be observed directly by camera or mapping system. Meanwhile, cracks are capable and reliable indicators. As a result, crack detection is one of the most broadly researched topic. As the limitation of computing resource vanishing, deep learning methods are developing rapidly and used widely. U-net is one of the latest deep learning methods for image classification and has shown overwhelming adaptability and performance in medical images. It is promising to be capable for crack detection.  In this thesis project, a U-net approach is used to automatically detect road and tunnel cracks. An open-source crack detection dataset is used for training. The model is improved by new parameter settings and fine-tuning and transformed onto the data acquired by the mobile mapping system of TACK team. Image processing techniques such as class imbalance handling and center line are also used for improvement. At last, qualitative and quantitative statistics are used to illustrate superiority of the methods.  This thesis project is a sub-project of project TACK, which is an ongoing research project carried out by KTH - Royal Institute of Technology, Sapienza University of Rome and WSP Sweden company under the InfraSweden2030 program funded by Vinnova. The main objective of TACK is developing a methodology for automatic detection and measurement of cracks on tunnel linings or other infrastructures.

Το αντικείμενο της παρούσας διδακτορικής διατριβής είναι η ανάπτυξη μιας εναλλακτικής μεθόδου μη καταστροφικού ελέγχου (ΜΚΕ) για αγώγιμα υλικά, που συνδυάζει την ηλεκτρομαγνητική διέγερση - επαγωγική θέρμανση του υλικού και επιθεώρηση με μεταβατική υπέρυθρη θερμογραφία. Με ένα μεταβαλλόμενο μαγνητικό πεδίο επάγονται δινορρεύματα εντός του εξεταζόμενου δοκιμίου. Η θερμότητα που παράγεται από τα δινορρεύματα, δημιουργεί θερμοκρασιακές διαφορές οι οποίες τείνουν να εξομαλυνθούν μέσω της θερμικής αγωγής. Κάποια ατέλεια στη δομή του υλικού, όπως είναι μια ρωγμή, θα επηρεάσει άμεσα ή έμμεσα τη ροή της θερμότητας και κατ’ επέκταση τη θερμοκρασιακή κατανομή στην επιφάνεια του υλικού. Χρησιμοποιώντας την υπέρυθρη θερμογραφία μπορούμε να απεικονίσουμε σε δύο διαστάσεις τη θερμοκρασιακή κατανομή της επιφάνειας του επιθεωρούμενου δοκιμίου και να εντοπίσουμε την ατέλεια αυτή. Η παρούσα διατριβή επικεντρώνεται στην υπολογιστική και πειραματική διερεύνηση της αποτελεσματικότητας και της αξιοπιστίας της ηλεκτρομαγνητοθερμικής μεθόδου ως μεθόδου ΜΚΕ σε αγώγιμα υλικά. Αφού πραγματοποιηθεί αναλυτική περιγραφή του μοντέλου με το οποίο προσεγγίζονται τα ηλεκτρομαγνητικά - θερμικά φαινόμενα της ηλεκτρομαγνητικής διέγερσης - επαγωγικής θέρμανσης αγώγιμων υλικών, αναπτύσσεται υπολογιστικός κώδικας για την υλοποίηση του μοντέλου. Με τη χρήση του υπολογιστικού προγράμματος διερευνάται η σημασία και η σπουδαιότητα ενός μεγάλου πλήθους παραμέτρων που επηρεάζουν την αποτελεσματικότητα της ηλεκτρομαγνητοθερμικής μεθόδου με απώτερο στόχο τη βελτιστοποίηση της. Στη συνέχεια ακολουθεί πειραματική επαλήθευση των αριθμητικών αποτελεσμάτων, όπου και αποδεικνύεται η αξιοπιστία των υπολογιστικών μοντέλων που χρησιμοποιήσαμε κατά την αριθμητική διερεύνηση της μεθόδου. Κατ’ αυτόν τον τρόπο επαληθεύεται η αποτελεσματικότητα της μεθόδου στον ΜΚΕ έλεγχο αγώγιμων υλικών. Το γενικό συμπέρασμα που προκύπτει είναι ότι η ηλεκτρομαγνητοθερμική μέθοδος αποτελεί μια αξιόπιστη μέθοδο για τον ΜΚΕ αγώγιμων υλικών. Απομένει πλέον να διερευνηθούν οι δυνατότητες της μεθόδου στο έπακρο, ώστε να αναδειχθεί το εύρος των εφαρμογών αυτής και να χρησιμοποιηθεί ενδεχομένως σε περιπτώσεις όπου μέχρι σήμερα κυριαρχούν άλλες διαγνωστικές μέθοδοι. / The subject matter of the present dissertation is the development of an alternative method for non-destructive inspection of conducting materials, which combines electromagnetic excitation – thermal conduction and inspection with transient infrared thermography. A time-varying magnetic field is used to induce eddy currents inside the conducting material under inspection. The Ohmic power generated in the material by the eddy currents creates temperature gradients which tend to be ironed out through thermal conduction. A defect in the material structure, such as a cracking, will affect the heat flow either directly or indirectly and hence the temperature distribution at the surface of the material. By employing infrared thermography, it is then possible to visualize in two-dimensional the temperature distribution over the excited surface of the tested specimen and detect the defect. The present dissertation focuses on computational and experimental investigation of the effectiveness and reliability of electromagnetic-thermal method as a method for non destructive inspection of conductive materials. After have been made a detailed description of the model which describes the electromagnetic-thermal phenomena of electromagnetic excitation - induction heating in conductive materials, it was developed a computer program based on the above model. Using the computer program we investigated the significance and the importance of a large number of parameters affecting the effectiveness of electromagnetic-thermal method, with a view to optimize the method. The experimental verification of numerical results, indicate the reliability of computational model used in the numerical investigation of the method and verifies the method’s effectiveness for non destructive inspection of conducting materials. The general conclusion is that the electromagnetic - thermal method is a reliable method for non destructive inspection of conductive materials. It remains the full potentials of the method to be investigated, in order to extend the range of applications and use the method in cases where today dominate other diagnostic methods.

Recent developments show the applications of smart structure in different engineering fields. Smart structures can be used for shape and vibration control, structural health monitoring etc. Smart materials can be integrated to composite structure to enhance its abilities. Fiber reinforced composites are the advanced materials of choice in aerospace applications due to its high strength and stiffness, light weight and ability to tailor according to the design requirements. Due to complex manufacturing process and varying operating conditions, composites are susceptible to variation in material properties and damages. The present study focuses on the effect of uncertainties in material properties and damages on a smart composite structure. A cantilevered composite plate with surface mounted piezoelectric sensor/ actuator is considered in this study. The sensors and the actuators are connected through a conventional feedback controller and the controller is configured for vibration control application. Matrix cracks are considered as damage in the composite plate. To study the effect of material uncertainty, probabilistic analysis is performed considering composite material properties and piezoelectric coefficients as independent Gaussian random variables. Numerical results show that there is substantial change in dynamic response of the smart composite plate due to material uncertainties and damage. Deviation due to material uncertainty and damage can be compensated by actively tuning the feedback control system. Feedback control parameters can be properly adjusted to match the baseline response. Here baseline case represents the response of the undamaged smart composite plate with deterministic material properties. The change in feedback control parameters are identified as damage indicator. Feedback control based damage detection method is proposed for structural health monitoring in smart composite structure and robustness of the method is studied considering material uncertainties. Fractal dimension based damage detection method is proposed to detect localized matrix cracks in a composite plate with spatially varying material properties. Variation in material properties follows a two dimensional homogeneous Gaussian random field. Fractal dimension is used to extract the damage information from the static response of composite plate with localized matrix cracks. It is found that fractal dimension based approach is capable of detecting the location of the single and multiple damages from the static deflection curve. Robustness of the fractal dimension based damage detection method is studied considering spatial uncertainties in material properties.

Structural health monitoring and building assessment are crucial to acquire structures’ states and maintain their conditions. Besides human-labor surveys that are subjective, time-consuming, and expensive, autonomous image and video analysis is a faster, more efficient, and non-destructive way. This thesis focuses on crack detection from videos, crack segmentation from images, and building assessment from street view images. For crack detection from videos, three approaches are proposed based on local binary pattern (LBP) and support vector machine (SVM), deep convolution neural network (DCNN), and fully-connected network (FCN). A parametric Naïve Bayes data fusion scheme is introduced that registers video frames in a spatiotemporal coordinate system and fuses information based on Bayesian probability to increase detection precision. For crack segmentation from images, the rotation-invariant property of crack is utilized to enhance the segmentation accuracy. The architectures of several approximately rotation-invariant DCNNs are discussed and compared using several crack datasets. For building assessment from street view images, a framework of multiple DCNNs is proposed to detect buildings and predict their attributes that are crucial for flood risk estimation, including founding heights, foundation types (pier, slab, mobile home, or others), building types (commercial, residential, or mobile home), and building stories. A feature fusion scheme is proposed that combines image feature with meta information to improve the predictions, and a task relation encoding network (TREncNet) is introduced that encodes task relations as network connections to enhance multi-task learning.

Corrosion damage reduces the load-carrying capacity of bridges which poses a threat to passenger safety. The objective of this research was to reduce the resources involved in conventional bridge inspections which are an important tool in the condition assessment of bridges and to help in determining if live load testing is necessary. This research proposes a framework to link semi-automated damage detection on prestressed concrete bridge girders with the estimation of their residual flexural capacity. The framework was implemented on four full-scale corrosion damaged girders from decommissioned bridges in Virginia. 3D point clouds of the girders reconstructed from images using Structure from Motion (SfM) approach were textured with images containing cracks detected at pixel level using a U-Net (Fully Convolutional Network). Spalls were detected by identifying the locations where normals associated with the points in the 3D point cloud deviated from being perpendicular to the reference directions chosen, by an amount greater than a threshold angle. 3D textured mesh models, overlaid with the detected cracks and spalls were used as 3D damage maps to determine reduced cross-sectional areas of prestressing strands to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011). Scaling them to real-world dimensions enabled the measurement of any required dimension, eliminating the need for physical contact. The flexural capacities of a box beam and an I-beam estimated using strain compatibility analysis were validated with the actual capacities at failure sections determined from four destructive tests conducted by Al Rufaydah (2020). Along with the reduction in the cross-sectional areas of strands, limiting the ultimate strain that heavily corroded strands can develop was explored as a possible way to improve the results of the analysis. Strain compatibility analysis was used to estimate the ultimate rupture strain, in the heavily corroded bottommost layer prestressing strands exposed before the box beam was tested. More research is required to associate each level of strand corrosion with an average ultimate strain at which the corroded strands rupture. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework. / Master of Science / Corrosion damage is a major concern for bridges as it reduces their load carrying capacity. Bridge failures in the past have been attributed to corrosion damage. The risk associated with corrosion damage caused failures increases as the infrastructure ages. Many bridges across the world built forty to fifty years ago are now in a deteriorated condition and need to be repaired and retrofitted. Visual inspections to identify damage or deterioration on a bridge are very important to assess the condition of the bridge and determine the need for repairing or for posting weight restrictions for the vehicles that use the bridge. These inspections require close physical access to the hard-to-reach areas of the bridge for physically measuring the damage which involves many resources in the form of experienced engineers, skilled labor, equipment, time, and money. The safety of the personnel involved in the inspections is also a major concern. Nowadays, a lot of research is being done in using Unmanned Aerial Vehicles (UAVs) like drones for bridge inspections and in using artificial intelligence for the detection of cracks on the images of concrete and steel members. Girders or beams in a bridge are the primary longitudinal load carrying members. Concrete inherently is weak in tension. To address this problem, High Strength steel reinforcement (called prestressing steel or prestressing strands) in prestressed concrete beams is pre-loaded with a tensile force before the application of any loads so that the regions which will experience tension under the service loads would be subjected to a pre-compression to improve the performance of the beam and delay cracking. Spalls are a type of corrosion damage on concrete members where portions of concrete fall off (section loss) due to corrosion in the steel reinforcement, exposing the reinforcement to the environment which leads to accelerated corrosion causing a loss of cross-sectional area and ultimately, a rupture in the steel. If the process of detecting the damage (cracks, spalls, exposed or severed reinforcement, etc.) is automated, the next logical step that would add great value would be, to quantify the effect of the damage detected on the load carrying capacity of the bridges. Using a quantified estimate of the remaining capacity of a bridge, determined after accounting for the corrosion damage, informed decisions can be made about the measures to be taken. This research proposes a stepwise framework to forge a link between a semi-automated visual inspection and residual capacity evaluation of actual prestressed concrete bridge girders obtained from two bridges that have been removed from service in Virginia due to extensive deterioration. 3D point clouds represent an object as a set of points on its surface in three dimensional space. These point clouds can be constructed either using laser scanning or using Photogrammetry from images of the girders captured with a digital camera. In this research, 3D point clouds are reconstructed from sequences of overlapping images of the girders using an approach called Structure from Motion (SfM) which locates matched pixels present between consecutive images in the 3D space. Crack-like features were automatically detected and highlighted on the images of the girders that were used to build the 3D point clouds using artificial intelligence (Neural Network). The images with cracks highlighted were applied as texture to the surface mesh on the point cloud to transfer the detail, color, and realism present in the images to the 3D model. Spalls were detected on 3D point clouds based on the orientation of the normals associated with the points with respect to the reference directions. Point clouds and textured meshes of the girders were scaled to real-world dimensions facilitating the measurement of any required dimension on the point clouds, eliminating the need for physical contact in condition assessment. Any cracks or spalls that went unidentified in the damage detection were visible on the textured meshes of the girders improving the performance of the approach. 3D textured mesh models of the girders overlaid with the detected cracks and spalls were used as 3D damage maps in residual strength estimation. Cross-sectional slices were extracted from the dense point clouds at various sections along the length of each girder. The slices were overlaid on the cross-section drawings of the girders, and the prestressing strands affected due to the corrosion damage were identified. They were reduced in cross-sectional area to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011) and were used in the calculation of the ultimate moment capacity of the girders using an approach called strain compatibility analysis. Estimated residual capacities were compared to the actual capacities of the girders found from destructive tests conducted by Al Rufaydah (2020). Comparisons are presented for the failure sections in these tests and the results were analyzed to evaluate the effectiveness of this framework. More research is to be done to determine the factors causing rupture in prestressing strands with different degrees of corrosion. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework.

Monitoring the mechanical integrity of critical structures is extremely important, as mechanical defects can potentially have adverse impacts on their safe operability throughout their service life. Structural defects can be detected by using active structural health monitoring (SHM) approaches, in which a given structure is excited with harmonic mechanical waves generated by actuators. The response of the structure is then collected using sensor(s) and is analyzed for possible defects, with various active SHM approaches available for analyzing the response of a structure to single- or multi-frequency harmonic excitations. In order to identify the appropriate excitation frequency, however, the majority of such methods require a priori knowledge of the characteristics of the defects under consideration. This makes the whole enterprise of detecting structural defects logically circular, as there is usually limited a priori information about the characteristics and the locations of defects that are yet to be detected. Furthermore, the majority of SHM techniques rely on sensors for response collection, with the very same sensors also prone to structural damage. The Surface Response to Excitation (SuRE) method is a broadband frequency method that has high sensitivity to different types of defects, but it requires a baseline. In this study, initially, theoretical justification was provided for the validity of the SuRE method and it was implemented for detection of internal and external defects in pipes. Then, the Comprehensive Heterodyne Effect Based Inspection (CHEBI) method was developed based on the SuRE method to eliminate the need for any baseline. Unlike traditional approaches, the CHEBI method requires no a priori knowledge of defect characteristics for the selection of the excitation frequency. In addition, the proposed heterodyne effect-based approach constitutes the very first sensor-less smart monitoring technique, in which the emergence of mechanical defect(s) triggers an audible alarm in the structure with the defect. Finally, a novel compact phased array (CPA) method was developed for locating defects using only three transducers. The CPA approach provides an image of most probable defected areas in the structure in three steps. The techniques developed in this study were used to detect and/or locate different types of mechanical damages in structures with various geometries.

Among various nanofillers, carbon nanotubes (CNTs) have attracted a significant attention due to their excellent physical properties. Incorporation of a very low amount of CNTs in polymer matrices enhances mechanical, thermal and optical properties of conductive polymer nanocomposites (CPNs) tremendously. For mechanical sensors, the piezoresistive property of CNTs/polymer nanocomposites exhibits a great potential for the realization of stable, sensitive, tunable and cost-effective strain sensors. Achieving homogeneous CNTs dispersion within the polymer matrices, understanding their complex piezoresistivity and conduction mechanisms, as well as the response of the nanocomposites under humidity and temperature effects, is highly required for the realization of piezoresistive CNTs/polymer based nanocomposites. This research primarily aims to synthesize and characterize CNTs/polymer based strain sensitive nanocomposites, which are cost-effective, applicable on both rigid and flexible substrates and require a non-complex fabrication process. A comprehensive understanding of the complex conduction and piezoresistive mechanisms of CNTs/polymer nanocomposites and their responses under humidity and temperature effects is another purpose of this thesis. For this purpose, synthesis and complex electromechanical characterization of multiwalled carbon nanotubes (MWCNTs)/epoxy nanocomposites are realized. In order to realize strain sensors for the strain range up to 1 % the use of epoxy is focused due to its good adhesion, dimensional stability, and good mechanical properties. The nanocomposites with up to 1 wt.% MWCNTs are synthesized by a non-complex direct mixing method and the final nanocomposites are deposited on flexible Kapton and rigid FR4 substrates and their corresponding morphological, electrical, electromechanical, as well as the response of the nanocomposite under humidity and temperature influences, are examined. The deformation over the sensor area is tested by digital image correlation (DIC) under quasi-static uniaxial tension. Quantitative piezoresistive characterization is performed by electrochemical impedance spectroscopy (EIS) over a wide range of frequencies. Further, dispersion quality of MWCNTs in the epoxy polymer matrix is monitored by scanning electron microscopy (SEM). Additionally, in order to tailor the piezoresistivity of the strain sensor, an R-C equivalent circuit is derived based on the impedance responses and the corresponding parameters are extracted from the applied strain. Obtained SEM images confirm that MWCNTs/epoxy nanocomposites with different MWCNTs concentrations have a good homogeneity and dispersion. Atomic force microscopy (AFM) analysis show that the samples have relatively good surface topography and fairly homogeneous CNTs networks. Higher sensitivity is achieved in particular at the concentrations close to the percolation threshold. A non-linear piezoresistive behavior is observed at low MWCNTs concentrations due to the dominance of tunneling effect. The strain sensitive nanocomposites deposited on FR4 substrates present high-performance strain sensing properties, including high sensitivity, good stability, and durability after cyclic loading and unloading. In addition, MWCNTs/epoxy nanocomposites show quite a small creep, low hysteresis under cyclic tensile and compressive loadings and fast response and recovery times. Nanocomposites provide an opportunity to measure 2-D strain in one position including amplitude and direction for complex configuration of structures in real-time systems or products. In contrast to present solutions for multi-directional strain sensing, MWCNTs/epoxy based nanocomposites give promising results in terms of durability, easy-processability, and tunable piezoresistivity. Unlike commercially-available approaches for crack/damage identification, MWCNTs/epoxy nanocomposites are capable of detecting the applied crack directly over a certain area. From the humidity influence, it has been found that resistance of nanocomposites increases with the increase of humidity exposure due to swelling of the polymer. Temperature investigations show that MWCNTs/epoxy nanocomposites give negative temperature coefficient (NTC) response due to thermal activation of charge carriers and the temperature sensitivity increases with the increase of filler concentration. The proposed approach can be further developed by combining differently fabricated sensors for realizing a compact structural health monitoring system or multi-functional sensor, where pressure, strain, temperature, and humidity can be monitored simultaneously. / Unter den verschiedenen Nanofillern haben CNTs aufgrund ihrer hervorragenden physikalischen Eigenschaften eine bedeutende Aufmerksamkeit erregt. Die Einarbeitung einer sehr geringen Menge an CNTs in Polymermatrizen verbessert die mechanischen, thermischen und optischen Eigenschaften von CPNs enorm. Für mechanische Sensoren bietet die piezoresistive Eigenschaft von CNTs/Polymer-Nanokompositen ein großes Potenzial zur Realisierung stabiler, empfindlicher, abstimmbarer und kostengünstiger Dehnungssensoren. Die Erzielung einer homogenen CNT-Dispersion innerhalb der Polymermatrizen, das Verständnis ihrer komplexen Piezoresistivitäts- und Leitungsmechanismen sowie die Reaktion der Nanokomposite unter Feuchte- und Temperatureinflüssen ist für die Realisierung piezoresistiver CNTs/Polymer-basierter Nanokomposite unerlässlich. Diese Arbeit zielt darauf ab, CNTs/polymerbasierte dehnungsempfindliche Nanokomposite herzustellen und zu charakterisieren. Diese Nanokompositen sollen kostengünstig, sowohl auf starren als auch auf flexiblen Substraten anwendbar sein und ein nicht komplexes Herstellungsverfahren erfordern. Ein umfassendes Verständnis der komplexen leitungs- und piezoresistive Mechanismen von CNTs/ Polymer-Nanokompositen und deren Reaktionen unter Feuchtigkeits- und Temperatureinflüssen ist ein weiteres Ziel dieser Arbeit. Zu diesem Zweck werden Synthese und komplexe elektromechanische Charakterisierung von MWCNTs/epoxy nanocomposites realisiert. Um Dehnungssensoren für den Dehnungsbereich bis zu 1 % realisieren zu können, wird der Einsatz von Epoxy aufgrund seiner guten Haftung, Dimensionsstabilität und guten mechanischen Eigenschaften fokussiert. Zufällig verteilte MWCNTs mit bis zu 1 wt.% MWCNTs-Konzentration ist durch ein direktes Mischen synthetisiert und die Nanokomposite werden auf flexiblen Kapton und starren FR4 Substraten durch Siebdruck appliziert und anschließend deren morphologische, elektrische, elektromechanische sowie die Reaktion des Nanocomposits unter Feuchtigkeits- und Temperatureinflüssen untersucht. Die Verformung über den Sensorbereich wird duch die Digital Image Correlation (DIC) Methode unter quasi-statischer uniaxialer Spannung getestet. Die quantitative piezoresistive Charakterisierung wird mit elektrische Impedanzspektroskopie (EIS) in einem breitem Frquenzspektrum durchgeführt. Ferner wird die Dispersionsqualität von MWCNTs in der Epoxidepolymermatrix durch Scanning Electron Microscopy (SEM) überprüft. Zusätzlich ist, um die Piezoresistivität des Dehnungssensors abzustimmen, eine RC-Äquivalenzschaltung auf der Grundlage der Impedanzantworten abgeleitet und die entsprechenden Parameter unter Belastung extrahiert. Erhaltene SEM-Bilder bestätigen, dass MWCNTs/Epoxide-Nanokomposite mit unterschiedlichen MWCNTs-Konzentrationen eine gute Homogenität und Dispersion aufweisen. Die atomic force microscopy (AFM) Untersuchung zeigt, dass die Proben relativ gute Oberflächentopographie und ziemlich homogene CNT-Netzwerke aufweisen. Eine höhere Empfindlichkeit wird insbesondere bei den Konzentrationen nahe der Perkolationsschwelle erreicht. Eine nichtlineare Piezoresistivität wird bei niedrigen MWCNTs Konzentrationen aufgrund der Dominanz des Tunnelwirkungseffekts beobachtet. Die auf FR4-Substraten applizierten dehnungsempfindlichen Nanokomposite weisen ausgezeichnete Dehnungsmessungseigenschaften einschließlich hohe Empfindlichkeit, gute Stabilität und Haltbarkeit nach zyklischer Be- und Entlastung auf. Darüber hinaus zeigen MWCNTs/Epoxide-Nanokomposite ein geringes Kriechen, eine kleine Hysterese unter zyklischen Zug- und Druckbelastungen, sowie schnelle Reaktionsund Wiederherstellungszeiten. Nanokomposite bieten die Möglichkeit, 2-D-Dehnungen in einer Position einschließlich Amplitude und Richtung innerhalb einer Materialstruktur in Echtzeitsystemen oder Produkten zu messen. Im Gegensatz zu aktuellen Lösungen für die multi-direktionale Dehnungsmessung, bieten die MWCNTs/Epoxide-Nanokomposite vielversprechende Ergebnisse in Bezug auf Langlebigkeit, leichte Verarbeitung und einstellbare Piezoresistivität. Im Unterschied zu kommerziell verfügbaren Ansätzen wird festgestellt, dassMWCNTs/Epoxide-Nanokomposite zur Riss-/Schadenserkennung in der Lage sind, den angelegten Riss direkt über einen bestimmten Bereich zu detektieren. Aus dem Einfluss der Feuchtigkeit hat sich herausgestellt, dass die Resistenz von Nanokompositen mit zunehmender Feuchtigkeitsbelastung durch Quellung des Polymers zunimmt. Temperaturuntersuchungen zeigen, dass MWCNTs/Epoxide-Nanokomposite aufgrund der thermischen Aktivierung von Ladungsträgern auf Temperatureinflüsse reagieren und die Temperaturempfindlichkeit mit der Erhöhung der Füllstoffkonzentration zunimmt. Der vorgeschlagene Ansatz kann durch die Kombination unterschiedlich hergestellte Sensoren zur Realisierung eines kompakten zur Überwachung des Zustands von Strukturen oder von multifunktionalen Sensoren weiterentwickelt werden, bei denen gleichzeitig Druck, Dehnung, Temperatur und Feuchtigkeit überwacht werden können.

The main goal of this thesis is to get an insight into a field of non-destructive testing using potential drop techniques that have nowadays become the standard not only in the fatigue and loading tests but also in the industry. These methods can serve to non-destructively and continuously measure material specimens, thickness, corrosion losses, deformations, spectroscopy and detection and analysis of crack geometry. They can help to identify materials and measure material changes over time. The second part of this thesis deals with designing the aperture for detection of cracks in steam and product piping using potential drop technique modified by Ing. Ladislav Korec, CSc. Last part deals with extensive testing, experimenting and evaluation of the aperture.

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