Existing methods of evaluating the structural system of a building after a seismic event consist of removing architectural elements such as drywall, cladding, insulation, and fireproofing. This method is destructive and costly in terms of downtime and repairs. This research focuses on removing the guesswork by using forced vibration testing (FVT) to experimentally determine the health of a building. The experimental structure is a one-story, steel, bridge-like structure with removable braces. An engaged brace represents a nominal and undamaged condition; a dis-engaged brace represents a brace that has ruptured thus changing the stiffness of the building. By testing a variety of brace configurations, a set of experimental data is collected that represents potential damage to the building after an earthquake. Additionally, several unknown parameters of the building’s substructure, lateral-force-resisting-system, and roof diaphragm are determined through FVT. A suite of computer models with different levels of damage are then developed. A quantitative analysis procedure compares experimental results to the computer models. Models that show high levels of correlation to experimental brace configurations identify the extent of damage in the experimental structure. No testing or instrumentation of the building is necessary before an earthquake to identify if, and where, damage has occurred.

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.

This work is focusing to study the structural behavior of Composite Overwrapped Pressure Vessels (COPVs). These COPVs are found in many engineering applications. In the aerospace field, they are installed onto spaceships and aid the reorientation of the spacecraft in very far and airless, therefore frictionless, orbits to save energy and fuel. The intent of this research is to analyze the difference in performance of both perfectly intact and purposely damaged tanks. Understanding both the source and location of a structural fault will help NASA engineers predict the performance of COPVs subject to similar conditions, which could prevent failures of important missions. The structural behavior of six tanks is investigated by means of experimental modal analysis. Knowledge of statistical signal processing methods allows to sort out and extract meaningful features from the data as to gain understanding of the performance of the structures. Structural identification is carried out using Narrow Band and Broad Band algorithms. A comparison through correlation tables and figures presents the differences in natural frequencies, mode shapes and damping ratios of all structures. A careful analysis displays the deviation of these modal parameters in the damaged tanks, highlighting the evident structural defects.

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A fatigue test has been performed on 7075-T651 aluminum specimens which were bonded with polyimide coated optical fibers with discrete Bragg gratings. These fibers were bonded with AE-10 strain gage adhesive. The results indicate that lower strain amplitudes do not produce cause for concern, but that larger strain amplitudes (on the order of 3500 μ) may cause some sensors to become unreliable. The strain response of acrylate coated optical fiber strain sensors bonded to aluminum specimens with AE-10 and M-Bond 200 strain gage adhesives was investigated with both axial and cantilever beam tests. These results were compared to both the strain response of conventional strain gages and to model predictions. The results indicate that only about 82.6% of the strain in the specimen was transferred through the glue line and fiber coating into the fiber. Thus, multiplying by a strain transfer factor of approximately 1.21 was sufficient to correct the optical fiber strain output. This effect was found to be independent of the adhesive used and independent of the three-dimensional profile of the glue line used to attach the fiber. Finally, this effect did not depend on whether the fiber had a polyimide or an acrylate coating. Further investigation was conducted on the feasibility of using optical fiber strain sensors for monitoring subcritical damage (such as matrix cracks) in fiber reinforced composite materials. These results indicate that an array of optical fibers which monitor the strain profile on both sides of a composite panel may be sufficient for these purposes / Master of Science

This study evaluates the effectiveness of actively controlled piezoceramics with positive position feedback (PPF) for reducing structural vibrations. A comparison is made between active control with PPF and a parallel resistor-inductor (RLC) shunt technique. The primary objectives of this study are to: 1. Explore the feasibility of using smart materials and fiber optics for simultaneous health monitoring and active damping of a representative aircraft panel. 2. Determine how optical fiber sensors may be used to detect vibration modes of an aircraft panel by investigating their use on a representative test article. 3. Determine how piezoelectric patches may be used to detect and counteract fundamental resonances of a representative test article. 4. Determine a control algorithm and hardware system to increase substantially the damping in the fundamental mode of the representative test article over a wide temperature range. 5. Develop a health-monitoring algorithm based on fiber optic sensors to detect impedance changes in a representative test article. 6. Make a comparison between active control with PPF and an RLC shunt technique. To achieve the objectives of this study, a special test rig was used to evaluate the performance of piezoelectric materials (PZTs) for vibration suppression. The test rig was used to rigidly clamp a flat 20-guage steel plate, and then excite the plate in various frequency ranges with an electromagnetic shaker. For each test, a data acquisition system was used to acquire the data to evaluate the performance of each PPF controller. Once the data was obtained, a comparison was made between active damping with PPF and passive damping with the RLC shunt technique. The active damping technique used for this study combined piezoelectric actuators with fiber optic sensors to achieve simultaneous active control and health monitoring of a test plate. The results of the active damping tests show that piezoelectric materials can provide substantial narrowband and broadband frequency reductions, while at the same time detecting damage on the test plate. More specifically, the test results indicate that smart damping materials can decrease the fundamental mode of vibration of the test plate by 23 dB and detect damage such as a loose bolt in the clamping frame, with the addition of only 0.04 lb of PZT on the test plate. The active damping technique reduced the plate vibrations at each mode within the frequency range of interest, with only one-third the amount of piezoelectric material needed for an RLC shunt circuit technique. / Master of Science

[pt] O monitoramento da integridade estrutural aumenta de importância dentro do campo de estudo de engenharia civil. Grande parte das cidades dependem de elementos de sua infraestrutura como pontes, barragens e prédios para prover uma série de benefícios para a sociedade moderna. Por outro lado, mesmo o projeto mais conservador não resiste aos efeitos do tempo. Uma boa rotina de manutenção preventiva não exime a necessidade de se ter uma constante verificação e busca de falhas pois em alguns casos isto poderia permitir em catástrofes de grande escala envolvendo grande perda material e até mesmo vidas. Graças ao desenvolvimento tecnológico das últimas décadas foi possível pesquisar e criar ferramentas poderosas que podem ajudar problemas deste tipo. O objetivo desta dissertação é avaliar a aplicação de métodos de Inteligência Artificial na detecção de danos em vigas. A metodologia utiliza parâmetros modais de elementos estruturais para verificar a presença de danos relacionados a redução de rigidez de uma seção transversal. Mais especificamente, os métodos apresentados neste estudo são orientados por dados, então primeiramente o banco de dados para treino e validação dos métodos de IA foi gerado por um programa em Python dentro do software de elementos finitos Abaqus. Os parâmetrosd modais analisados foram as cinco primeiras frequências naturais das vigas. Foi possível avaliar a performance dos métodos de IA para classificação da presença ou não de danos em diferentes métricas de análise. Por fim, uma comparação paramétrica foi feita entre os modelos de Inteligência Artificial. / [en] Monitoring structural integrity has become increasingly important in the field of civil engineering. A huge part of cities depend of civil engineer infrastructures such as bridges, dams and buildings to provide several benefits to modern society. On the other hand, even the most conservative design cannot resist the power of time. A good preventive maintenance routine don’t let go of the need in constant verification for faults because in some cases that could lead to large scale catastrophes involving big material and life costs. Thanks to technology development over the last decades it was possible to search and create many powerful tools that could help those kind of problems. The objective of this thesis is to assess on the application of Artificial Intelligence Methods to detect damage on beams. The formulation uses modal parameters of a structure to verify the presence of damage related to the reduction of stiffness of a section. More specifically, the methods presented on this study are data-driven, so first a database for training and validating the AI methods were generated in a Python program within the finite element software Abaqus. The modal parameters analyzed were the first five natural frequencies of a beam. It was possible to evaluate the performance of the AI methods when classifying a beam with or without damage on different metrics. Finally, a parametric comparison was made between the Artificial Intelligence methods.

We model the nonlinear dynamics of a cantilever beam with tip mass system subjected to different excitation and exploit the nonlinear behavior to perform sensitivity analysis and propose a parameter identification scheme for nonlinear piezoelectric coefficients. First, the distributed parameter governing equations taking into consideration the nonlinear boundary conditions of a cantilever beam with a tip mass subjected to principal parametric excitation are developed using generalized Hamilton's principle. Using a Galerkin's discretization scheme, the discretized equation for the first mode is developed for simpler representation assuming linear and nonlinear boundary conditions. We solve the distributed parameter and discretized equations separately using the method of multiple scales. We determine that the cantilever beam tip mass system subjected to parametric excitation is highly sensitive to the detuning. Finally, we show that assuming linearized boundary conditions yields the wrong type of bifurcation. Noting the highly sensitive nature of a cantilever beam with tip mass system subjected to parametric excitation to detuning, we perform sensitivity of the response to small variations in elasticity (stiffness), and the tip mass. The governing equation of the first mode is derived, and the method of multiple scales is used to determine the approximate solution based on the order of the expected variations. We demonstrate that the system can be designed so that small variations in either stiffness or tip mass can alter the type of bifurcation. Notably, we show that the response of a system designed for a supercritical bifurcation can change to yield a subcritical bifurcation with small variations in the parameters. Although such a trend is usually undesired, we argue that it can be used to detect small variations induced by fatigue or small mass depositions in sensing applications. Finally, we consider a cantilever beam with tip mass and piezoelectric layer and propose a parameter identification scheme that exploits the vibration response to estimate the nonlinear piezoelectric coefficients. We develop the governing equations of a cantilever beam with tip mass and piezoelectric layer by considering an enthalpy that accounts for quadratic and cubic material nonlinearities. We then use the method of multiple scales to determine the approximate solution of the response to direct excitation. We show that approximate solution and amplitude and phase modulation equations obtained from the method of multiple scales analysis can be matched with numerical simulation of the response to estimate the nonlinear piezoelectric coefficients. / Master of Science / The domain of structural dynamics involves the evaluation of the structures response when subjected to time-varying loads. This field has many applications. For instance, by observing specific variations in the response of a structure such as bridge or a structural element such as a beam, one can diagnose the state of the structure or one of its elements. At much smaller scales, one can use a device to observe small variations in the response of a beam to detect the presence of bio-materials or gas particles in air. Additionally, one can use the response of a structure to harvest energy of ambient vibrations that are freely available. In this thesis, we develop a mathematical framework for evaluating the response of a cantilever beam with a tip mass to small variations in material properties caused by fatigue and to small variations in the tip mass caused by additional mass that gets bound to the structure. We also exploit the response of the beam to evaluate nonlinear material properties of piezoelectric materials that have been suggested for use in charging micro sensors, vibration control, load sensing and for high power energy transfer applications.

Composite structures are extremely useful for aerospace, automotive, marine and civil applications due to their very high specific structural properties. These structures are subjected to severe dynamic loading in their service life. Repeated exposure to these severe loading conditions can induce structural damage which ultimately may precipitate a catastrophic failure. Therefore, an interest in the continuous inspection and maintenance of engineering structures has grown tremendously in recent years. Sensitive aerospace applications can have small design margins and any inadequacy in knowledge of the system may cause design failure. Structures made from composite materials posses complicated failure mechanism as compared to those made from conventional metallic materials. In composite structural design, it is hence very important to properly model geometric intricacies and various imperfections such as delaminations and cracks. Two important issues are addressed in this thesis: (1) structural modeling of nonlinear delamination and uncertainty propagation in nonlinear characteristics of composite plate structures and (2) development of a model based damage detection system to handle uncertainty issues. An earlier proposed shear deformable C0 composite plate finite element is modified to alleviate modeling uncertainty issues associated with a damage detection problem. Parabolic variation of transverse shear stresses across the plate thickness is incorporated into the modified formulation using mixed shear interpolation technique. Validity of the proposed modification is established through available literature. Correction of the transverse shear stress term in the formulation results in about 2 percent higher solution accuracy than the earlier model. It is found that the transverse shear effect increases with higher modes of the plate deformation. Transverse shear effects are more prominent in sandwich plates. This refined composite plate finite element is used for large deformation dynamic analysis of delaminated composite plates. The inter-laminar contact at the delaminated region in composite plates is modeled with the augmented Lagrangian approach. Numerical simulations are carried out to investigate the effect of delamination on the nonlinear transient behavior of composite plates. Results obtained from these studies show that widely used unconditionally stable β-Newmark method presents numerical instability problems in the transient simulation of delaminated composite plate structures with large deformation. To overcome this instability issue, an energy and momentum conserving composite implicit time integration scheme presented by Bathe and Baig is used for the nonlinear dynamic analysis. It is also found that a proper selection of the penalty parameter is very crucial in the simulation of contact condition. It is shown that an improper selection of penalty parameter in the augmented Lagrangian formulation may lead to erroneous prediction of dynamic response of composite delaminated plates. Uncertainties associated with the mathematical characterization of a structure can lead to unreliable damage detection. Composite structures also show considerable scatter in their structural response due to large uncertainties associated with their material properties. Probabilistic analysis is carried out to estimate material uncertainty effects in the nonlinear frequencies of composite plates. Monte Carlo Simulation with Latin Hypercube Sampling technique is used to obtain the variance of linear and nonlinear natural frequencies of the plate due to randomness in its material properties. Numerical results are obtained for composite plates with different aspect ratio, stacking sequence and oscillation amplitude ratio. It is found that the nonlinear frequencies show increasing non-Gaussian probability density function with increasing amplitude of vibration and show dual peaks at high amplitude ratios. This chaotic nature of the dispersion of nonlinear eigenvalues is also revealed in eigenvalue sensitivity analysis. For fault isolation, variations in natural frequencies, modal curvatures and curvature damage factors due to damage are investigated. Effects of various physical uncertainties like, material and geometric uncertainties on the success of damage detection is studied. A robust structural damage detection system is developed based on the statistical information available from the probabilistic analysis carried out on beam type structures. A new fault isolation technique called sliding window defuzzifier is proposed to maximize the success rate of a Fuzzy Logic System (FLS) in damage detection. Using the changes in structural measurements between the damaged and undamaged state, a fuzzy system is generated and the rule-base and membership functions are generated using probabilistic informations. The FLS is demonstrated using frequency and mode shape based measurements for various beam type structures such as uniform cantilever beam, tapered beam in single as well as in multiple damage conditions. The robustness of the FLS is demonstrated with respect to the highly uncertain input information called measurement deltas (MDs). It is said, if uncertainty level is larger than or close to the changes in damage indicator due to damage, the true information would be submerged in the noise. Then the actual damaged members may not be identified accurately and/or the healthy members may be wrongly detected as damaged giving false warning. However, this being the case, the proposed FLS with new fault isolation technique tested with these noisy data having large variation and overlaps shows excellent robustness. It is observed that the FLS accurately predicts and isolates the damage levels up-to considerable uncertainty and noise levels in single as well as multiple damage conditions. The robustness of the FLS is also demonstrated for delamination detection in composite plates having very high material property uncertainty. Effects of epistemic uncertainty on damage detection in composite plates is addressed. The effectiveness of the proposed refined Reddy type shear deformable composite plate element is demonstrated for reducing the modeling or epistemic uncertainty in delamination detection.

The great social and economic impact of earthquakes has made necessary the development of novel structural health monitoring (SHM) solutions for increasing the level of structural safety and assessment. SHM is the process of comparing the current state of a structure’s condition relative to a healthy baseline state to detect the existence, location, and degree of likely damage during or after a damaging input, such as an earthquake. Many SHM algorithms have been proposed in the literature. However, a large majority of these algorithms cannot be implemented in real time. Therefore, their results would not be available during or immediately after a major event for urgent post-event response and decision making. Further, these off-line techniques are not capable of providing the input information required for structural control systems for damage mitigation. The small number of real-time SHM (RT-SHM) methods proposed in the past, resolve these issues. However, these approaches have significant computational complexity and typically do not manage nonlinear cases directly associated with relevant damage metrics. Finally, many available SHM methods require full structural response measurement, including velocities and displacements, which are typically difficult to measure. All these issues make implementation of many existing SHM algorithms very difficult if not impossible. This thesis proposes simpler, more suitable algorithms utilising a nonlinear Bouc-Wen hysteretic baseline model for RT-SHM of a large class of nonlinear hysteretic structures. The RT-SHM algorithms are devised so that they can accommodate different levels of the availability of design data or measured structural responses, and therefore, are applicable to both existing and new structures. The second focus of the thesis is on developing a high-speed, high-resolution, seismic structural displacement measurement sensor to enable these methods and many other SHM approaches by using line-scan cameras as a low-cost and powerful means of measuring structural displacements at high sampling rates and high resolution. Overall, the results presented are thus significant steps towards developing smart, damage-free structures and providing more reliable information for post-event decision making.