Following the studies of natural frequency based damage detection methods, an advanced technique for damage detection and localization in beam-type structures using a vibration characteristic tuning procedure is developed by an optimal design of piezoelectric materials. Piezoelectric sensors and actuators are mounted on the surface of the host beam to generate excitations for the tuning via a feedback process. The excitations induced by the piezoelectric effect are used to magnify the effect of the damage on the change of the natural frequencies of the damaged structure to realize the high detection sensitivity. Based on the vibration characteristic tuning procedure, a scan-tuning methodology for damage detection and localization is proposed. From analytical simulations, both crack and delamination damage in the beams are detected and located with over 20% change in the natural frequencies. Finite element method (FEM) simulations are conducted to verify the effectiveness of the proposed methodology. / October 2016

Structural Health Monitoring (SHM) is a means of identifying damage from the structural response to environmental loads. Real-time SHM offers rapid assessment of structural safety by owners and civil defense authorities enabling more optimal response to major events. This research presents an real-time, convex, integral-based SHM methods for seismic events that use only acceleration measurements and infrequently measured displacements, and a non-linear baseline model including hysteretic dynamics and permanent deformation. The method thus identifies time-varying pre-yield and post-yield stiffness, elastic and plastic components of displacement and final residual displacement. For a linear baseline model it identifies only timevarying stiffness. Thus, the algorithm identifies all key measures of structural damage affecting the immediate safety or use of the structure, and the long-term cost of repair and retrofit. The algorithm is tested with simulated and measured El Centro earthquake response data from a four storey non-linear steel frame structure and simulated data from a two storey non-linear hybrid rocking structure. The steel frame and rocking structures exhibit contrasting dynamic response and are thus used to highlight the impact of baseline model selection in SHM. In simulation, the algorithm identifies stiffness to within 3.5% with 90% confidence, and permanent displacement to within 7.5% with 90% confidence. Using measured data for the frame structure, the algorithm identifies final residual deformation to within 1.5% and identifies realistic stiffness values in comparison to values predicted from pushover analysis. For the rocking structure, the algorithm accurately identifies the different regimes of motion and linear stiffness comparable to estimates from previous research. Overall, the method is seen to be accurate, effective and realtime capable, with the non-linear baseline model more accurately identifying damage in both of the disparate structures examined.

The focus of this research is on the development of vibration response-based damage detection in civil engineering structures. Modal parameter-based and model identification-based approaches have been considered. In the modal parameter-based approach, the flexibility and curvature flexibility matrices of the structure are used to identify the damage. It is shown that changes in these matrices can be related to changes in stiffness values of individual structural members. Using this relationship, a method is proposed to solve for the change in stiffness values. The application of this approach is demonstrated on the benchmark problem developed by the joint International Association of Structural Control and American Society of Civil Engineers Structural Health Monitoring task group. The proposed approach is found to be effective in identifying various damage scenarios of this benchmark problem. The effect of missing modes on the damage identification scheme is also studied. The second method for damage identification aims at identifying sudden changes in stiffness for real time applications. It is shown that the high-frequency content of the response acceleration can be used to identify the instant at which a structure suffers a sudden reduction in its stiffness value. Using the Gibb's phenomenon, it is shown why a high-pass filter can be used for identifying such damages. The application of high-pass filters is then shown in identifying sudden stiffness changes in a linear multi-degree-of-freedom system and a bilinear single degree of freedom system. The impact of measurement noise on the identification approach is also studied. The noise characteristics under which damage identification can or cannot be made are clearly identified. The issue of quantification of the stiffness reduction by this approach is also examined. It is noted that even if the time at which the reduction in stiffness happens can be identified, the quantification of damage requires the knowledge of system displacement values. In principle, such displacements can be calculated by numerical integration of the acceleration response, but the numerical integrations are known to suffer from the low frequency drift error problems. To avoid the errors introduced due to numerical integration of the acceleration response, an approach utilizing the unscented Kalman filter is developed to track the sudden changes in stiffness values. This approach is referred to as the adaptive unscented Kalman filter (AUKF) approach. The successful application of the proposed AUKF approach is shown on two multi-degree of freedom systems that experience sudden loss of stiffness values while subjected to earthquake induced base excitation. / Ph. D.

The need for low order models capable of performing damage identification has become apparent in many structural dynamics applications where structural health monitoring and damage prognosis programs are implemented. These programs require that damage identification routines have low computational requirements and be reliable with some quantifiable degree of accuracy. Response surface metamodels (RSMs) are proposed to fill this need. Popular in the fields of chemical and industrial engineering, RSMs have only recently been applied in the field of structural dynamics and to date there have been no studies which fully demonstrate the potential of these methods. In this thesis, several RSMs are developed in order to demonstrate the potential of the methodology. They are shown to be robust to noise (experimental variability) and have success in solving the damage identification problem, both locating and quantifying damage with some degree of accuracy, for both linear and nonlinear systems. A very important characteristic of the RSMs developed in this thesis is that they require very little information about the system in order to generate relationships between damage indicators and measureable system responses for both linear and nonlinear structures. As such, the potential of these methods for damage identification has been demonstrated and it is recommended that these methods be developed further. / Master of Science

No description available.

Structural Health Monitoring (SHM) is a non-destructive evaluation tool that assesses the functionality of structural systems that are used in the civil, mechanical and aerospace engineering practices. A much desirable objective of a SHM system is to provide a continuous monitoring service at a minimal cost with ability to identify problems even in inaccessible structural components. In this dissertation, several such approaches that utilize the measured dynamic response of structural systems are presented to detect, locate, and quantify the damages that are likely to occur in structures. In this study, the structural damage is identified as a reduction in the stiffness characteristics of the structural elements. The primary focus of this study is on the utilization of measured dynamic strains for damage identification in the framed structures which are composed of interconnected beam elements. Although linear accelerations, being more convenient to measure, are commonly used in most SHM practices, herein the strains being more sensitive to elemental damage are considered. Two different approaches are investigated and proposed to identify the structural element stiffness properties. Both approaches are mode-based, requiring first the identification of system modes from the measured strain responses followed by the identification of the element stiffness coefficients. The first approach utilizes the Eigen equation of the finite element model of the structure, while the second approach utilizes the changes caused by the damage in the structural curvature flexibilities. To reduce size of the system which is primarily determined by the number of sensors deployed for the dynamic data collection, measurement sensitivity-based sensor selection criterion is observed to be effective and thus used. The mean square values of the measurements with respect to the stiffness coefficients of the structural elements are used as the effective measures of the measurement sensitivities at different sensor locations. Numerical simulations are used to evaluate the proposed identification approaches as well as to validate the sensitivity-based optimal sensor deployment approach. / Ph. D. / All modern societies depend heavily on civil infrastructure systems such as transportation systems, power generation and transmission systems, and data communication systems for their day-to-day activities and survival. It has become extremely important that these systems are constantly watched and maintained to ensure their functionality. All these infrastructure systems utilize structural systems of different forms such as buildings, bridges, airplanes, data communication towers, etc. that carry the service and environmental loads that are imposed on them. These structural systems deteriorate over time because of natural material degradation. They can also get damaged due to excessive load demands and unknown construction deficiencies. It is necessary that condition of these structural systems is known at all times to maintain their functionality and to avoid sudden breakdowns and associated ensuing problems. This condition assessment of structural systems, now commonly known as structural health monitoring, is commonly done by visual onsite inspections manually performed at pre-decided time intervals such as on monthly and yearly basis. The length of this inspection time interval usually depends on the relative importance of the structure towards the functionality of the larger infrastructure system. This manual inspection can be highly time and resource consuming, and often ineffective in catching structural defects that are inaccessible and those that occur in between the scheduled inspection times and dates. However, the development of new sensors, new instrumentation techniques, and large data transfer and processing methods now make it possible to do this structural health monitoring on a continuous basis. The primary objective of this study is to utilize the measured dynamic or time varying strains on structural components such as beams, columns and other structural members to detect the location and level of a damage in one or more structural elements before they become serious. This detection can be done on a continuous basis by analyzing the available strain response data. This approach is expected to be especially helpful in alerting the owner of a structure by identifying the iv occurrence of a damage, if any, immediately after an unanticipated occurrence of a natural event such as a strong earthquake or a damaging wind storm.

Vibration-based structural health monitoring (SHM) seeks to detect, quantify, locate, and prognosticate damage by processing vibration signals measured while the structure is operational. The basic premise of vibration-based SHM is that damage will affect the stiffness, mass or energy dissipation properties of the structure and in turn alter its measured dynamic characteristics. In order to make SHM a practical technology it is necessary to perform damage assessment using only a minimum number of permanently installed sensors. Deducing damage at unmeasured regions of the structural domain requires solving an inverse problem that is underdetermined and(or) ill-conditioned. In addition, the effects of local damage on global vibration response may be overshadowed by the effects of modelling error, environmental changes, sensor noise, and unmeasured excitation. These theoretical and practical challenges render the damage identification inverse problem ill-posed, and in some cases unsolvable with conventional inverse methods. This dissertation proposes and tests a novel interpretation of the damage identification inverse problem. Since damage is inherently local and strictly reduces stiffness and(or) mass, the underdetermined inverse problem can be made uniquely solvable by either imposing sparsity or non-negativity on the solution space. The goal of this research is to leverage this concept in order to prove that damage identification can be performed in practical applications using significantly less measurements than conventional inverse methods require. This dissertation investigates two sparsity inducing methods, L1-norm optimization and the non-negative least squares, in their application to identifying damage from eigenvalues, a minimal sensor-based feature that results in an underdetermined inverse problem. This work presents necessary conditions for solution uniqueness and a method to quantify the bounds on the non-unique solution space. The proposed methods are investigated using a wide range of numerical simulations and validated using a four-story lab-scale frame and a full-scale 17 m long aluminum truss. The findings of this study suggest that leveraging the attributes of both L1-norm optimization and non-negative constrained least squares can provide significant improvement over their standalone applications and over other existing methods of damage detection.

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method, an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared numerically and experimentally using measurements from two structures, a simply supported beam and a pedestrian bridge. The dynamic properties for the numerical comparison are extracted by modal analysis in ABAQUS, while the dynamic characteristics for the experimental comparison are obtained with the Wireless Sensor Network and the Time Domain Decomposition. In both the numerical and experimental phases, the accuracy of damage predictions from each method is quantified. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate when applied to both structures. The proposed IDIM has the potential to improve SHM.

No description available.

Bedeutung im Kontext von Schäden erweitert. Es wird eine systematische Methodik für die Schadensidentifikation entwickelt, die die Datenerfassung, Schadensbewertung und Schadensdatenverwaltung umfasst. Für die Erfassung von sichtbaren Schadensdaten wird die semantische Segmentierung verwendet. Zahlreiche Strategien und innovative Convolutional Neural Networks wurden entwickelt, um herkömmliche Netzwerke im Kontext der semantischen Segmentierung zu verbessern. Allerdings wurde ein umfassender Vergleich dieser Netzwerke selten durchgeführt. Für zwei Strategien, der Attention Mechanismen und der Generative Adversarial Networks, wird eine vergleichende Studie durchgeführt, um die semantische Segmentierung zu verbessern. Basierend auf dem U-Net werden neuartige Verteilungstypen für beide Strategien mit verschiedenen Diskriminatoren entwickelt und verglichen. Die am besten abschneidenden Netzwerke werden dann einem Validierungsprozess unterzogen, und auch die kombinierten Effekte der beiden Strategien werden vertieft untersucht. Die Cloud Simulation wird zur numerischen Bewertung von Brückenschäden und der Identifikation von verdeckten Schäden angewendet. Zwei Ansätze, nämlich der Single Variation Approach (SVA) und der Dual Variation Approach (DVA), werden vorgestellt. Beide Ansätze werden auf unterschiedliche Szenarien angewendet, um den Einfluss verschiedener Lastfälle und Überwachungspunkte zu studieren. Der effektivere DVA-Ansatz wird in einem Prototyp implementiert, der Funktionalitäten wie Datenkonvertierung, Visualisierung, Generierung von Modellvariationen und Ergebnisanalyse umfasst. Zur Validierung wird eine Stahlbetonbrücke analysiert. Qualitative und quantitative Bewertungen für die Schadensrehabilitation werden in eine Wissensbasis integriert, die automatische Vorschläge für die praktische Schadensrehabilitation für die infizierten Schäden liefert. Augmented Reality wird zur Verbesserung des Visualisierungsergebnisses bei der Schadensinformationsverwaltung für die Vor-Ort-Inspektion und die Rehabilitationsinformationen eingesetzt und in einer Baustellenumgebung validiert. Den Abschluss bildet eine Marketingperspektive der neugewonnenen Ergebnisse. / The entire research work is dedicated to reinterpreting the concept of system identification by exploring its connotation and expanding its denotation in the context of damage. The work focuses on bridges as representative structures and proposes a systematic methodology for damage identification, encompassing damage data acquisition, damage assessment, and damage data management.  Semantic segmentation is employed for viewable damage data identification. Numerous strategies and innovative convolutional neural networks have been developed to enhance traditional networks in the context of semantic segmentation. However, a comprehensive comparison of these networks has been rarely conducted. Two strategies, namely attention mechanisms and generative adversarial networks, are examined in order to enhance semantic segmentation. Based on the U-net, novel distribution types of attention mechanisms and generative adversarial networks with different discriminators are compared in a lightweight test. The best performed networks are then implemented in the validation process, and in addition the combined effects of the attention mechanism and discriminator are investigated. Cloud simulation is applied for quantitative evaluation and identification of non-viewable damage. Two approaches, namely the Single Variation Approach (SVA) and the Dual Variation Approach (DVA), are introduced and applied to different scenarios to account for various load cases and monitoring points as variables. A prototype is developed to implement the more effective DVA approach, incorporating functionalities such as data conversion, visualization, model variation generation and result analysis. A monitored concrete bridge is employed for validation of the assessment of the effectiveness and reliability of the method. Qualitative and quantitative assessments are incorporated into a knowledge base for damage rehabilitation, which automatically provide practical suggestion for the specific identified damage.  Augmented reality is utilized to enhance the visualization experience for on-site inspection providing rehabilitation information and a prototype in a construction setting. The conclusion presents a marketing perspective on the findings.