Structural Health Monitoring (SHM) techniques are widely used in a number of Non – destructive Evaluation (NDE) applications. There is a need to develop effective techniques for SHM, so that the safety and integrity of the structures can be improved. Two most widely used SHM methods for plates and rods use either the spectrum of the impedances or monitor the propagation of lamb waves. Piezoelectric wafer – active sensors (PWAS) were used for excitation and sensing. In this study, surface response to excitation (SuRE) and Lamb wave propagation was monitored to estimate the integrity of the round objects including the pipes, tubes and cutting tools. SuRE obtained the frequency response by applying sweep sine wave to surface. The envelope of the received signal was used to detect the arrival of lamb waves to the sensor. Both approaches detect the structural defects of the pipes and tubes and the wear of the cutting tool.

Detecção de dano em estruturas de engenharia de grandes dimensões através da análise de suas características dinâmicas envolve diversos campos de estudo. O primeiro deles trata da identificação dos parâmetros modais da estrutura, uma vez que executar testes de vibração livre em tais estruturas não é uma tarefa simples, necessita-se de um método robusto que seja capaz de identificar os parâmetros modais dessa estrutura a ações ambientais, campo esse chamado de análise modal operacional. Este trabalho trata do problema de detecção de dano em estruturas que possam ser representadas através de modelos em pórticos planos e vigas e que estejam submetidos à ação de vibrações ambientais. A localização do dano é determinada através de um algoritmo de otimização conhecido como Backtracking Search Algorithm (BSA) fazendo uso de uma função objetivo que utiliza as frequências naturais e modos de vibração identificados da estrutura. Simulações e testes são feitos a fim de verificar a concordância da metodologia para ambos os casos. Para as simulações, são utilizados casos mais gerais de carregamentos dinâmicos, e dois níveis de ruído (3% e 5%) são adicionados ao sinal de respostas para que esses ensaios se assemelhem aos ensaios experimentais, onde o ruído é inerente do processo. Já nos ensaios experimentais, apenas testes de vibração livre são executados. Diversos cenários de dano são propostos para as estruturas analisadas a fim de se verificar a robustez da rotina de detecção de dano. Os resultados mostram que a etapa de identificação modal estocástica através do método de identificação estocástica de subespaço (SSI) teve ótimos resultados, possibilitando, assim, a localização da região danificada da estrutura em todos os casos analisados. / Damage detection in large dimensions engineering structures through the analysis of their dynamic characteristics involves several fields. The first one deals with the structure modal identification parameter, since running free vibration tests in such structures is not a simple task, robust methods are needed in order to identify the modal parameters of this structure under ambient vibrations, this field is known as operational modal analysis. This work deals with the problem of damage detection in structures under ambient vibrations that can be represented by FEM using frame and beam elements. The damage location is determined through an optimization algorithm know as Backtracking Search Algorithm (BSA). It uses as objective function the identified natural frequencies and modes of vibration of the structure. Numerical and experimental tests are performed to assess the agreement of the methodology for both cases. For the numerical tests, more general cases of dynamic loads are used, and two noise levels (3% and 5%) are added to the response signal to assessing the robustness of the methodology close to the field conditions, in which noise is inherent of the process. In the experimental tests, only free vibration tests are performed. Several damage scenarios are proposed for the analyzed structures to check the robustness of the damage detection routine. The results show that the stochastic modal identification using the stochastic subspace identification (SSI) method had excellent results, thus allowing the location of the damaged region of the structure in all analyzed cases.

<div>This dissertation broadly focuses on autonomous condition assessment of civil infrastructures using vision-based methods, which present a plausible alternative to existing manual techniques. A region-based convolutional neural network (Faster R-CNN) is exploited for the detection of various earthquake-induced damages in reinforced concrete buildings. Four different damage categories are considered such as surface crack, spalling, spalling with exposed rebars, and severely buckled rebars. The performance of the model is evaluated on image data collected from buildings damaged under several past earthquakes taking place in different parts of the world. The proposed algorithm can be integrated with inspection drones or mobile robotic platforms for quick assessment of damaged buildings leading to expeditious planning of retrofit operations, minimization of damage cost, and timely restoration of essential services. </div><div><br></div><div> </div><div> Besides, a computer vision-based approach is presented to track the evolution of a damage over time by analysing historical visual inspection data. Once a defect is detected in a recent inspection data set, its spatial correspondences in the data collected during previous rounds of inspection are identified leveraging popular computer vision-based techniques. A single reconstructed view is then generated for each inspection round by synthesizing the candidate corresponding images. The chronology of damage thus established facilitates time-based quantification and lucid visual interpretation. This study is likely to enhance the efficiency structural inspection by introducing the time dimension into the autonomous condition assessment pipeline.</div><div><br></div><div> </div><div> Additionally, this dissertation incorporates depth fusion into a CNN-based semantic segmentation model. A 3D animation and visual effect software is exploited to generate a synthetic database of spatially aligned RGB and depth image pairs representing various damage categories which are commonly observed in reinforced concrete buildings. A number of encoding techniques are explored for representing the depth data. Besides, various schemes for fusion of RGB and depth data are investigated to identify the best fusion strategy. It was observed that depth fusion enhances the performance of deep learning-based damage segmentation algorithms significantly. Furthermore, strategies are proposed to manufacture depth information from corresponding RGB frame, which eliminates the need of depth sensing at the time of deployment without compromising on segmentation performance. Overall, the scientific research presented in this dissertation will be a stepping stone towards realizing a fully autonomous structural condition assessment pipeline.</div>

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In the field of structural engineering it is of vital importance that the condition of an ageing structure is monitored to detect damages that could possibly lead to failure of the structure. Over the past few years various methods for monitoring the condition of structures have been proposed. With respect to civil and mechanical structures several methods make use of modal parameters such as, natural frequency, damping ratio and mode shapes. In the present work four methods for modal parameter estimation and two methods for have been evaluated for their application to multi degree of freedom structures. The methods evaluated for modal parameter estimation are: Wavelet transform, Hilbert-Huang transform, parametric system identification and peak picking. Through various numerical simulations the effectiveness of these methods is studied. It is found that the simple peak-picking method performs the best and is able to identify modal parameters most accurately in all the simulation cases that were considered in this study. The identified modal parameters are then used for locating the damage. Herein the flexibility and the rotational flexibility approaches are evaluated for damage detection. The approach based on the rotational flexibility is found to be more effective. / Master of Science

One of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea, the state of the art technique in the field is straight forward - populating a given structure with sensors and extracting information from them. In this regard, instrumenting with too many sensors may be inefficient as this could lead to superfluous data that is expensive to capture and process. This research aims to explore an alternate SHM technique that optimizes the data acquisition process by eliminating the amount of redundant data that is sensed and uses this sufficient data to detect and locate the fault present in the structure. Efficient data acquisition requires a mechanism that senses just the necessary amount of data for detection and location of fault. For this reason Compressive Sensing (CS) is explored as a plausible idea. CS claims that signals can be reconstructed from what was previously believed to be incomplete information by Shannon's theorem, taking only a small amount of random and linear non - adaptive measurements. As responses of many physical systems contain a finite basis, CS exploits this feature and determines the sparse solution instead of the traditional least - squares type solution.As a first step, CS is demonstrated by successfully recovering the frequency components of a simple sinusoid. Next, the question of how CS compares with the conventional Fourier transform is analyzed. For this, recovery of temporal frequencies and signal reconstruction is performed using the same number of samples for both the approaches and the errors are compared. On the other hand, the FT error is gradually minimized to match that of CS by increasing the number of regularly placed samples. Once the advantages are established, feasibility of using CS to detect damage in a single degree of freedom system is tested under unforced and forced conditions. In the former scenario, damage is indicated when there is a change in natural frequency of vibration of the system after an impact. In the latter, the system is excited harmonically and damage is detected by a change in amplitude of the system's vibration. As systems in real world applications are predominantly multi-DOF, CS is tested on a 2-DOF system excited with a harmonic forcing. Here again, damage detection is achieved by observing the change in the amplitude of vibration of the system. In order to employ CS for detecting either a change in frequency or amplitude of vibration of a structure subjected to realistic forcing conditions, it would be prudent to explore the reconstruction of a signal which contains multiple frequencies. This is accomplished using CS on a chirp signal. Damage detection is clearly a spatio-temporal problem. Hence it is important to additionally explore the extension of CS to spatial reconstruction. For this reason, mode shape reconstruction of a beam with standard boundary conditions is performed and validated with standard/analytical results from literature. As the final step, the operation deflection shapes (ODS) are reconstructed for a simply supported beam using CS to establish that it is indeed a plausible approach for a less expensive SHM. While experimenting with the idea of spatio-temporal domain, the mode shape as well as the ODS of the given beam are examined under two conditions - undamaged and damaged. Damage in the beam is simulated as a decrease in the stiffness coefficient over a certain number of elements. Although the range of modes to be examined heavily depends on the structure in question, literature suggests that for most practical applications, lower modes are more dominant in indicating damage. For ODS on the other hand, damage is indicated by observing the shift in the recovered spatial frequencies and it is confirmed by the reconstructed response.

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Electrical impedance tomography (EIT) is a non-destructive, non-invasive, and non-radioactive imaging technique used for reconstructing the internal conductivity distribution of a sensing domain. Performing EIT often requires large, stationary benchtop equipment that can be expensive and impractical. Other researchers have attempted to make portable EIT systems, but they all rely on external computation for image reconstruction/data analysis. This study outlines the development of a low-cost, portable, and wireless EIT data acquisition (DAQ) system that is capable of independently performing image reconstructions on-board. With the proposed system, EIT can be performed on carbon fiber reinforced polymers to spatially locate damages. Since EIT reconstruction algorithms can be extremely computationally intensive, this study has also developed an alternative deep-learning algorithm that leverages the compressed-sensing technique to strategically train a neural network. The proposed neural network has not only achieved comparable results to traditional iterative algorithms, but it can do so in a fraction of the time.

Master of Science / Department of Civil Engineering / Hani G. Melhem / The objective of this research is to use structure ambient random vibration response to detect damage level and location. The use of ambient vibration is advantageous because excitation is caused by service conditions such as normal vehicle traffic on a highway bridge, train passage on a railroad bridge, or wind loads on a tall building. This eliminates the need to apply a special impact or dynamic load, or interrupt traffic on a bridge in regular service. This research developed an approach in which free vibration of a structure is extracted from the response of this structure to a random excitation in the time domain (acceleration versus time) by averaging out the random component of the response. The result is the free vibration that includes all modes based on the sampling rate on time. Then this free vibration is transferred to the frequency domain using a Fast Fourier Transform (FFT). Variations in frequency response are a function of structural stiffness and member end-conditions. Such variations are used as a measure to identify the change in the structural dynamic properties, and ultimately detect damage. A physical model consisting of a 20 × 20 × 1670 -mm long steel square tube was used to validate this approach. The beam was tested under difference supports conditions varying from a single- to three-span continuous configuration. Random excitation was applied to the beam, and the dynamic response was measured by an accelerometer placed at various locations on the span. A numerical model was constructed in ABAQUS and the dynamic response was obtained from the finite element model subjected to similar excitation as in the physical model. Numerical results were correlated against results from the physical model, and comparison was made between the different span/support configurations. A subsequent step would be to induce damage that simulates loss of stiffness or cracking condition of the beam cross section, and that would be reflected as a change in the frequency and other dynamic properties of the structure. The approach achieved good results for a structure with a limited number of degrees of freedom. Further research is needed for structures with a larger number of degrees of freedom and structures with damage in symmetrical locations relative to the accelerometer position.