An analytical model of cracked composite beams vibrating in coupled bending-torsion is developed. The beam is made of fiber-reinforced composite with fiber angles in each ply aligned in the same direction. The crack is assumed open. The local flexibility concept is implemented to model the open crack and the associated compliance matrix is derived. The crack introduces additional boundary conditions at the crack location and these effects in conjunction with those of material properties are investigated. Free vibration analysis of the cracked composite beam is presented. The results indicate that variation of natural frequencies in the presence of a crack is affected by the crack ratio and location, as well as the fiber orientation. In particular, the variation pattern is different as the magnitude of bending-torsion coupling changes due to different fiber angles. When bending and torsional modes are essentially decoupled at a certain fiber angle if there is no crack, the crack introduces coupling to the initially uncoupled bending and torsion. Based on the crack model, aeroelastic characteristics of an unswept composite wing with an edge crack are investigated. The cracked composite wing is modeled by a cracked composite cantilever and the inertia coupling terms are included in the model. An approximate solution on critical flutter and divergence speeds is obtained by Galerkin's method in which the fundamental mode shapes of the cracked wing model in free vibration are used. It is shown that the critical divergence/flutter speed is affected by the elastic axis location, the inertia axis location, fiber angles, and the crack ratio and location. Moreover, model-based crack detection (size and location) by changes in natural frequencies is addressed. The Cawley-Adams criterion is implemented and a new strategy in grouping frequencies is proposed to reduce the probability of measurement errors. Finally, sensitivity of natural frequencies to model parameter uncertainties is investigated. Uncertainties are modeled by information-gap theory and represented with a collection of nested sets. Five model parameters that may have larger uncertainties are selected in the analysis, and the frequency sensitivities to uncertainties in the five model parameters are compared in terms of two immunity functions. / Ph. D.

This research introduces and applies the concept of digital state models for civil infrastructure condition assessment and structural testing. Digital state models are defined herein as any transient or permanent 3D model of an object (e.g. textured meshes and point clouds) combined with any electromagnetic radiation (e.g., visible light, infrared, X-ray) or other two-dimensional image-like representation. In this study, digital state models are built using visible light and used to document the transient state of a wide variety of structures (ranging from concrete elements to cold-formed steel columns and hot-rolled steel shear-walls) and civil infrastructures (bridges). The accuracy of digital state models was validated in comparison to traditional sensors (e.g., digital caliper, crack microscope, wire potentiometer). Overall, features measured from the 3D point clouds data presented a maximum error of ±0.10 in. (±2.5 mm); and surface features (i.e., crack widths) measured from the texture information in textured polygon meshes had a maximum error of ±0.010 in. (±0.25 mm). Results showed that digital state models have a similar performance between all specimen surface types and between laboratory and field experiments. Also, it is shown that digital state models have great potential for structural assessment by significantly improving data collection, automation, change detection, visualization, and augmented reality, with significant opportunities for commercial development. Algorithms to analyze and extract information from digital state models such as cracks, displacement, and buckling deformation are developed and tested. Finally, the extensive data sets collected in this effort are shared for research development in computer vision-based infrastructure condition assessment, eliminating the major obstacle for advancing in this field, the absence of publicly available data sets. / Ph. D.

Die digitale Nahbereichsphotogrammetrie ermöglicht eine effiziente Erfassung dreidimensionaler Objektoberflächen bei experimentellen Untersuchungen. Besonders für die flächenhafte Erfassung von Verformungen und die Rissdetektion sind photogrammetrische Verfahren – unter Beachtung entsprechender Randbedingungen – prinzipiell geeignet. Der Beitrag geht unter Einbeziehung aktueller Untersuchungen an textilbewehrten Betonproben auf die Problematik der Rissdetektion ein und gibt einen Überblick über den Entwicklungsstand und das erreichbare Genauigkeitspotential. In Bezug auf die praktische Anwendung der vorgestellten Verfahren wird abschließend auf verschiedene Möglichkeiten der Optimierung eingegangen.

Structural Health Monitoring (SHM) with emerging technologies like e.g. fibre optic sensors, lasers, radars, acoustic emission and Micro Electro Mechanical Systems (MEMS) made an entrance into the civil engineering field in last decades. Expansion of new technologies together with development in data communication benefited for rapid development. The author has been doing research as well as working with SHM and related tasks nearly a decade. Both theoretical knowledge and practical experience are gained in this constantly developing field. This doctoral thesis presents lessons learned in SHM and sensory technologies when monitoring civil engineering structures, mostly bridges. Nevertheless, these techniques can also be used in most applications related to civil engineering like dams, high rise buildings, off-shore platforms, pipelines, harbour structures and historical monuments. Emerging and established technologies are presented, discussed and examples are given based on the experience achieved. A special care is given to Fibre Optic Sensor (FOS) technology and its latest approach. Results from crack detection testing, long-term monitoring, and sensor comparison and installation procedure are highlighted. The important subjects around sensory technology and SHM are discussed based on the author's experience and recommendations are given. Applied research with empirical and experimental methods was carried out. A state-of-the art-review of SHM started the process but extensive literature studies were done continuously along the years in order to keep the knowledge up to date. Several SHM cases, both small and large scale, were carried out including sensor selection, installation planning, physical installation, data acquisition set-up, testing, monitoring, documentation and reporting. One case study also included modification and improvement of designed system and physical repair of sensors as well as two Site Acceptance Tests (SATs) and the novel crack detection system testing. Temporary measuring and testing also took place and numerous Structural Health Monitoring Systems (SHMSs) were designed for new bridges. The observed and measured data/phenomena were documented and analysed.  Engineers, researchers and owners of structures are given an essential implement in managing and maintaining structures. Long-term effects like shrinkage and creep in pre-stressed segmental build bridges were studied. Many studies show that existing model codes are not so good to predict these long-term effects. The results gained from the research study with New Årsta Railway Bridge are biased be the fact that our structure is indeed special. Anyhow, the results can be compared to other similar structures and adequately used for the maintenance planning for the case study. A long-term effect like fatigue in steel structures is a serious issue that may lead to structural collapse. Novel crack detection and localisation system, based on development on crack identification algorithm implemented in DiTeSt system and SMARTape delamination mechanism, was developed, tested and implemented. Additionally, new methods and procedures in installing, testing, modifying and improving the installed system were developed. There are no common procedures how to present the existing FOS techniques. It is difficult for an inexperienced person to judge and compare different systems. Experience gained when working with Fibre Optic Sensors (FOS) is collected and presented. The purpose is, firstly to give advice when judging different systems and secondly, to promote for more standardised way to present technical requirements. Furthermore, there is need to regulate the vocabulary in the field. Finally, the general accumulated experience is gathered. It is essential to understand the complexity of the subject in order to make use of it. General trends and development are compared for different applications. As the area of research is wide, some chosen, specific issues are analysed on a more detailed level. Conclusions are drawn and recommendations are given, both specific and more general. SHMS for a complex structure requires numerous parameters to be measured. Combination of several techniques will enable all required measurements to be taken. In addition, experienced specialists need to work in collaboration with structural engineers in order to provide high-quality systems that complete the technical requirement. Smaller amount of sensors with proper data analysis is better than a complicated system with numerous sensors but with poor analysis. Basic education and continuous update for people working with emerging technologies are also obligatory. A lot of capital can be saved if more straightforward communication and international collaboration are established: not only the advances but also the experienced problems and malfunctions need to be highlighted and discussed in order not to be repeated. Quality assurance issues need to be optimized in order to provide high quality SHMSs. Nevertheless, our structures are aging and we can be sure that the future for sensory technologies and SHM is promising. The final conclusion is that an expert in SHM field needs wide education, understanding, experience, practical sense, curiosity and preferably investigational mind in order to solve the problems that are faced out when working with emerging technologies in the real world applications.  The human factor, to be able to bind good relationship with workmanship cannot be neglected either. There is also need to be constantly updated as the field itself is in continuous development. / QC 20111117 / SHMS of the New Årsta Railway Bridge

This study presents an exploration of several machine learning and image processing theories, as well as a literature review of several previous works on concrete crack detection systems. Through the literature review a system is selected and implemented with the Öresund bridge photograph collection. The selected system is a Convolutional Neural Network (CNN) using cropped (256x256x) images for input. The CNN has a total of 13 layers that were implemented as described in the paper. All parts of the implementation such as cropping, code, and testing are described in detail. This study found a final accuracy rate of 77% for the trained net. This is combined with a sliding window technique for handling larger images. An exploration of reasons for this accuracy rate is done at the end of the paper.

Die digitale Nahbereichsphotogrammetrie ermöglicht eine effiziente Erfassung dreidimensionaler Objektoberflächen bei experimentellen Untersuchungen. Besonders für die flächenhafte Erfassung von Verformungen und die Rissdetektion sind photogrammetrische Verfahren – unter Beachtung entsprechender Randbedingungen – prinzipiell geeignet. Der Beitrag geht unter Einbeziehung aktueller Untersuchungen an textilbewehrten Betonproben auf die Problematik der Rissdetektion ein und gibt einen Überblick über den Entwicklungsstand und das erreichbare Genauigkeitspotential. In Bezug auf die praktische Anwendung der vorgestellten Verfahren wird abschließend auf verschiedene Möglichkeiten der Optimierung eingegangen.

Pavement crack detection is an important procedure in road maintenanceand traffic safety. Traditionally, the road inventory was performed by field inspection, now it is replaced by the evaluation of mobile mapping system images. The acquired images are still a significant source of temporal condition of thepavement surface. The automatisation of crack detection is highly necessarybecause it could decrease workload, and therefore, maintenance costs. Two methods for automatic crack detection from mobile mapping imageswere tested: step by step pixel based image intensity analysis, and deep learning. The objective of this thesis is to develop and test the workflow for the streetview image crack detection and reduce image database by detecting no-cracksurfaces. To examine the performance of the methods, their classification precisionwas compared. The best-acquired precision with the trained deep learningmodel was 98% that is 3% better than with the other method and it suggeststhat the deep learning is the most appropriate for the application. Furthermore, there is a need for faster and more precise detection methods, and deep learningholds promise for the further implementation. However, future studies areneeded and they should focus on full-scale image crack detection, disturbingobject elimination and crack severity classification.

Crack, a typical term most people know, is a common form of distress or damage in road pavements and railway sleepers. It poses significant challenges to their structural integrity, safety, and longevity. Over the years, researchers have developed various data-driven technologies for image-based crack detection in road and sleeper applications. The image-based crack detection has become a promising field.  Many researchers use ensemble learning to win the Road Damage Detection Challenge. The challenge provides a street view dataset from several countries from different perspectives. The version of the dataset is 2020, which contains images from Japan, India, and Czech. Thus, the dataset inherits a domain shift problem. Current solutions use ensemble learning to deal with such a problem. Those solutions require much computational power and challenge adaptability in real-time applications. To mitigate the problem, the thesis experiments with various data augmentation techniques that could improve the base model performance. The main focuses are erasing a crack from an image using generative AI (Erase), implementing road segmentation by using the Panoptic Segmentation (RS) and injecting a perspective-aware synthetic crack (InjectPa) into the segmented road surface in the image. The results show that compared to the base model, the Erase + RS techniques improve the model's F1 score when trained only on Japan in the dataset rather than when trained on three countries simultaneously. Moreover, the InjectPa technique does not help improve the base model in both scenarios. Then, the experiment moved to the SBB dataset containing close-up images of sleepers from cameras mounted in front of the diagnostic vehicle. This section follows the same techniques but changes the segmentation model to the Segment Anything Model (SAM) because the previous segmentation model was trained on a street view dataset, making it vulnerable to close-up images. The Erase + SAM techniques show improvement in bbox/AP and validation loss. Nevertheless, it does not improve the F1 score significantly compared to the base model.  This thesis also applies the explainable AI name D-RISE to determine which feature most influences the model decision. D-RISE shows that the augmentation model can pay attention to the damage type pothole for road pavements and defect type spalling for sleepers than other types. Finally, the thesis discusses the results and suggests a strategy for future study. / Sprickor, en typisk term som de flesta känner till, är en vänlig form av skador i vägbeläggningar och järnvägsslipers. Det innebär betydande utmaningar för strukturella integritet, säkerhet och livslängd. Under årens lopp har olika datadrivna tekniker utvecklats för bildbaserade sprickdetektering i vägbeläggningar och järnvägsslipers applikationer. Den bildbaserade sprickdetekteringen har blivit ett lovande område. Många forskare använder ensembleinlärningsmodeller för att vinna den Road Damage Detection Challenge (Vägbeläggningar Detektering Utmaning). Utmaningen ger en Gatuvy dataset från flera länder från olika perspektiv. Versionen av datasetet är 2020 som innehåller bilder från Japan, Indien och Tjeckien. Därför ärver datasetet  ett domänskiftproblem. Nuvarande lösningar använder ensembleinlärning för att hantera ett sådant problem. Dessa lösningar kräver mycket datorkraft och utmanar anpassningsförmågan i realtidsapplikationer. För att mildra problemet, denna avhandling prover många tekniker för dataökningar som kan förbättra basmodellens prestanda. Huvudfokusen är att radera en spricka från en bild via en generativ AI (Erase), implementera vägyta segmentering via den Panoptic Segmentation (RS), lägga en persective-aware syntetik spricka (InjectPa) till segmenterade vögytan in bilden. Resultaten visar att den Erase + RS ökningsteknikerna förbättrar modellens F1 score när den tränas på Japan i datasetet i stället för att tränas alla länder samtidigt. Dessutom förbättrar den InjectPa tekniken inte basmodellen på båda fallen.  Därefter flyttades experimentet till SBB-datasetet som innehåller närbilder av järnvägsslipers från kameror monterades framför ett diagnosfordon. Denna section följer de samma teknikerna men ändra segmentering modellen till den Segment Anything Model (SAM) eftersom förra segmentering modellen tränades på en Gatuvy dataset vilket gör den sårbar för närbilder. Den Erase + SAM ökningsteknikerna visar förbättringar på bbox/AP och validering. Ändå förbättrade den inte F1 score avsevört jämfört med basmodellen.  Denna avhandling tillämpar också Förklarbar AI-namnet D-RISE för att avgöra vilken funktion som mest påverkar modellbeslutet. D-RISE visar att modellen som har dataökning kan uppmärksamma skadetypen potthål för vägbeläggningar och defekttypen spjälkning för järnvägsslipers än andra typer. Slutligen diskuterar avhandlingen resultaten och föreslår en strategi för framtida arbetsinsatser.

Concrete has for decades been one of the main materials used to construct important structuressuch as dams and bridges. However, after years of service, the concrete structuresstart deteriorating and signs of damages start showing on the structure. The inspectionof such structures is compulsory to assess their state and plan repairing operations ifnecessary. The main inspection method has for long been field inspection, however, thismethod presents several problems, namely, the complexity to access some parts of thestructures and the subjectivity of the decisions. These difficulties make the operationtime-consuming and prone to error, thus a need for a new methodology that would benumerical and more automated.One of the damages that affect the carrying capacity the most in concrete structuresare surface-cracks. In this work, the focus is brought on this type of damage and a solutionfor the inspection methodology is presented and applied to a case study.The methodology first consists in using the photogrammetry technology, which allows theproduction of a 3D point cloud model of the structure by taking multiple pictures of it usingUAVs to facilitate access to its complex parts. From this point cloud, surface-openingcracks could be visualized and an orthoimage featuring the damages can be produced.Then, an image-based crack detection program will be used on the orthoimage to extractthe crack. From this, a program is developed to find the coordinates of the crack in thestructure and to process the model of the crack. Then a program is made to importthe crack model to a finite element software, and from there the extended finite elementmethod (XFEM) combined with the concrete damaged plasticity (CDP) method will beused to assess the carrying capacity of the structure and to study the evolution of itscrack pattern.The methodology was tested in a case study. In this case study, a steel-reinforced concretebeam was damaged and all of the methodology steps were applied. An adaptationto some steps was needed due to challenges raised by the test set-up.A three-point bending experience was carried out on the beam while recording the forceand displacement data and the crack pattern evolution. Numerically, this experience wasreproduced and relevant results were extracted and compared to the experimental ones.Differences in the carrying capacity were observed and multiple numerical analyses werecarried out to test the taken assumptions and detect the source of error. On the otherhand, for the crack pattern, satisfying results were achieved. Moreover, the degree of thedetailing in the crack model and its effect on the results is discussed.Globally, it can be concluded that the methodology can effectively in a numericalsemi-automated way capture the surface cracks in concrete structures and import theirmodel to a finite element software to apply analyses to assess the capacity of the damagedstructure and study the evolution of the cracks.This methodology could be further developed in the future by including more technologiessuch as lasergrammetry to make it go from a 2D surface-based damage analysis to a 3Danalysis. Moreover, criteria specialized for inspection and repairing purposes could becreated and implemented in the methodology.

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