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A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data. / En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.

Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.

<p>  </p> <p>Structural health monitoring is more efficient than traditional visual interval-based structural inspection because structural assessments are implemented when a sensor, such as an accelerometer, measures the vibration of the structure and detects any abnormal readings outside of a safety threshold. These vibrations tend to be atypical when there is damage to the structure. Processing the collected data from an accelerometer using Fast Fourier Transformation (FFT) allows for a graphical visualization of visualizing these atypical measurements in the frequency domain. The comparison and analysis of vibration frequency incurred from three different scenarios (damage, no damage, and impact) in the steel truss prototype has resulted in fundamental knowledge necessary to differentiate an abnormality in accelerometer readings resulting from a vehicular crash against one in which there is actual structural damage. The primary outcome of this work will lead to avoiding unnecessary inspection costs due to possible faulty diagnostics and determining the reliability of the structural health monitoring method.</p>

Sensing and analysis of a structure for the purpose of detecting, tracking, and evaluating damage and deterioration, during both regular operation and extreme events, is referred to as Structural Health Monitoring (SHM). SHM is a multi-disciplinary field, with a complete system incorporating sensing technology, hardware, signal processing, networking, data analysis, and management for interpretation and decision making. However, many of these processes and subsequent integration into a practical SHM framework are in need of development. In this study, various components of an SHM system will be investigated. A particular focus is paid to the investigation of a previously developed damage detection methodology for global condition assessment of a laboratory structure with a decking system. First, a review of some of the current SHM applications, which relate to a current UCF Structures SHM study monitoring a full-scale movable bridge, will be presented in conjunction with a summary of the critical components for that project. Studies for structural condition assessment of a 4-span bridge-type steel structure using the SHM data collected from laboratory based experiments will then be presented. For this purpose, a time series analysis method using ARX models (Auto-Regressive models with eXogeneous input) for damage detection with free response vibration data will be expanded upon using both wired and wireless acceleration data. Analysis using wireless accelerometers will implement a sensor roaming technique to maintain a dense sensor field, yet require fewer sensors. Using both data types, this ARX based time series analysis method was shown to be effective for damage detection and localization for this relatively complex laboratory structure. Finally, application of the proposed methodologies on a real-life structure will be discussed, along with conclusions and recommendations for future work

The main objective of this research is to explore, investigate and develop the new data analysis techniques along with novel sensing technologies for structural health monitoring applications. The study has three main parts. First, a systematic comparative evaluation of some of the most common and promising methods is carried out along with a combined method proposed in this study for mitigating drawbacks of some of the techniques. Secondly, nonparametric methods are evaluated on a real life movable bridge. Finally, a hybrid approach for non-parametric and parametric method is proposed and demonstrated for more in depth understanding of the structural performance. In view of that, it is shown in the literature that four efficient non-parametric algorithms including, Cross Correlation Analysis (CCA), Robust Regression Analysis (RRA), Moving Cross Correlation Analysis (MCCA) and Moving Principal Component Analysis (MPCA) have shown promise with respect to the conducted numerical studies. As a result, these methods are selected for further systematic and comparative evaluation using experimental data. A comprehensive experimental test is designed utilizing Fiber Bragg Grating (FBG) sensors simulating some of the most critical and common damage scenarios on a unique experimental structure in the laboratory. Subsequently the SHM data, that is generated and collected under different damage scenarios, are employed for comparative study of the selected techniques based on critical criteria such as detectability, time to detection, effect of noise, computational time and size of the window. The observations indicate that while MPCA has the best detectability, it does not perform very reliable results in terms of time to detection. As a result, a machine-learning based algorithm is explored that not only reduces the associated delay with MPCA but further iii improves the detectability performance. Accordingly, the MPCA and MCCA are combined to introduce an improved algorithm named MPCA-CCA. The new algorithm is evaluated through both experimental and real-life studies. It is realized that while the methods identified above have failed to detect the simulated damage on a movable bridge, the MPCA-CCA algorithm successfully identified the induced damage. An investigative study for automated data processing method is developed using nonparametric data analysis methods for real-time condition maintenance monitoring of critical mechanical components of a movable bridge. A maintenance condition index is defined for identifying and tracking the critical maintenance issues. The efficiency of the maintenance condition index is then investigated and demonstrated against some of the corresponding maintenance problems that have been visually and independently identified for the bridge. Finally, a hybrid data interpretation framework is designed taking advantage of the benefits of both parametric and non-parametric approaches and mitigating their shortcomings. The proposed approach can then be employed not only to detect the damage but also to assess the identified abnormal behavior. This approach is also employed for optimized sensor number and locations on the structure.

Monitoring the health of vibrating gears is important to ensure proper operation especially in potentially life-threatening structures, such as helicopters, nuclear power plants, and uninterruptible power supply transitions in hospitals. The most common monitoring technique is casing mounted accelerometers to measure vibration. In contrast, during the last few years acoustic monitoring techniques have also provided a few diagnostic methods for gear failure. Current diagnostic methods to indicate improper gear behavior use either existing vibration data, recorded from defective gear systems, or modern dynamic models predicting gear failure behavior. This thesis uses dynamic models to indicate, predict, and diagnose healthy and unhealthy gear systems. Influence of Tip Relief on contact forces are introduced for a decent understanding of gear dynamics followed by evaluation of common gear failure mechanisms. Two software systems were used to model gear failure: Adams®, a vibration based software that uses a rigid-elastic model for multi-body dynamics, and LSDYNA ®, a transient dynamic finite element solver, capable of solving acoustic problems with the boundary element method. Results describe tooth loads along the line of contact with respect to different Tip Reliefs and contact ratios. Gear failure is examined using a Fast Fourier Transformation to characterize patterns that can be used to diagnose unhealthy gear systems. Agreement of experimental results validates theoretical predictions of analytical and numerical solutions of gear failure especially of tooth breakage.

The discovery of carbon nanotubes in 1990s popularized a new area of research in materials science called Nanoscience. In the following decades, several carbon based nanoparticles were discovered or engineered and with the discovery of Graphene nanoplatelets (GNP) in 2010, carbon based nanoparticles were propelled as the most promising class of nanoparticles. High mechanical strength and stiffness, excellent electrical and thermal conductivity, and high strength to weight ratios are some of the unique abilities of CNTs and GNPs which allow their use in a wide array of applications from aerospace materials to electronic devices. In the current work presented herein, CNTs and GNPs are added to polymeric materials to create a nanocomposite material. The effects of this nanoparticle addition (a.k.a reinforcement) on the mechanical properties of the nanocomposite polymer materials are studied. Specifically, efforts are focused on studying fracture toughness, a material property that describes the material's ability to resist crack growth. Relative to the conventional metals used in structures, epoxy-based composites have poor fracture toughness. This has long been a weak link when using epoxy composites for structural applications and therefore several efforts are being made to improve their fracture toughness. In the first, second and third chapters, the enhancement of fracture toughness brought about by the addition of carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) was investigated. CNT-Epoxy and GNP-Epoxy Compact Tension (CT) samples were fabricated with 0.1% and 0.5% nanofiller weight concentrations. The potential synergistic effects of dual nanofiller reinforcements were also explored using CNT/GNP-Epoxy CT samples at a 1:3, 3:1 and 1:1 ratio of CNT:GNP. Displacement controlled CT tests were conducted according to ASTM D5045 test procedure and the critical stress intensity factor, $K_{IC}$, and the critical fracture energy, $G_{IC}$, were calculated for all the material systems. Significant enhancements relative to neat epoxy were observed in reinforced epoxies. Fracture surfaces were analyzed via scanning electron microscopy. Instances of CNT pullouts on the fracture surface were observed, indicating the occurrence of crack bridging. Furthermore, increased surface roughness, an indicator of crack deflection, was observed along with some crack bifurcations in the GNP-Epoxy samples. In the fourth chapter of Part I, the influence of pre-crack characteristics on the Mode-I fracture toughness of epoxy is investigated. Pre-crack characteristics such as pre-crack length, crack front shape, crack thickness and crack plane profile are evaluated and their influence on the peak load, fracture displacement, and the critical stress intensity factor, $K_{IC}$ is studied. A new method of razor blade tapping was used, which utilized a guillotine-style razor tapping device to initiate the pre-crack and through-thickness compression to arrest it. A new approach of quantitatively characterizing the crack front shape using a two-parameter function is introduced. Surface features present on the pre-crack surface are classified and their effects on the post crack initiation behavior of the sample are analyzed. This study aims to identify and increase the understanding of the various factors that cause variation in the fracture toughness data of polymeric materials, thus leading to more informed engineering design decisions and evaluations. Chapters six and seven of Part II investigate the SHM capabilities of dispersed MWCNTs in mock, inert, and active energetics. In these experimental investigations, the strain and damage sensing abilities of multi-walled carbon nanotube (MWCNT) networks embedded in the binder phase of polymer bonded energetics (PBEs) are evaluated. PBEs are a special class of particulate composite materials that consist of energetic crystals bound by a polymer matrix, wherein the polymer matrix serves to diminish the sensitivity of the energetic phase to accidental mechanical stimuli. The structural health monitoring (SHM) approach presented in this work exploits the piezoresistive properties of the distributed MWCNT networks. Major challenges faced during such implementation include the low binder concentrations of PBEs, presence of conductive/non-conductive particulate phases, high degree of heterogeneity in the PBE microstructure, and achieving the optimal MWCNT dispersion. In chapter seven, Ammonium Perchlorate (AP) crystals as the oxidizer, Aluminum grains as the metallic fuel, and Polydimethylsiloxane (PDMS) as the binder are used as the constituents for fabricating PBEs. To study the effect of each constituent on the MWCNT network's SHM abilities, various materials systems are comprehensively studied: MWCNT/PDMS (nBinder) materials are first evaluated to study the binder's electromechanical response, followed by AP/MWCNT/PDMS (inert nPBE) to assess the impact of AP addition, and finally, AP/AL/MWCNT/PDMS (active nPBE-AL) to evaluate the impact of adding conductive aluminum grains. Compression samples (ASTM D695) were fabricated and subjected to monotonic compression. Electrical resistance is recorded in conjunction with the mechanical test via an LCR meter. Gauge factors relating the change in normalized resistance to applied strain are calculated to quantify the electromechanical response. MWCNT dispersions, and mechanical failure modes are analyzed via scanning electron microscopy (SEM) imaging of the fracture surfaces. Correlations between the electrical behavior in response to the mechanical behavior are presented, and possible mechanisms that influence the electromechanical behavior are discussed. The results presented herein demonstrate the successful ability of MWCNT networks as structural health monitoring sensors capable of real-time strain and damage assessment of polymer bonded energetics. / Doctor of Philosophy / The discovery of carbon nanotubes in 1990s popularized a new area of research in materials science called Nanoscience. Carbon nanotubes (CNTs) are one of several forms of Carbon, meaning a differently structured carbon molecule in the same physical state similar to diamonds, graphite, and coal. In the following decades, several carbon based nanoparticles were discovered or engineered and with the discovery of Graphene (GNP) in 2010, carbon based nanoparticles were propelled as the most promising class of nanoparticles. High mechanical strength and stiffness, excellent electrical and thermal conductivity, and high strength to weight ratios are some of the unique abilities of CNTs and GNPs which allow their use in a wide array of applications from aerospace materials to electronic devices. In the current work presented herein, CNTs and GNPs are added to polymeric materials to create a nanocomposite material, where the term "composite" refers to a material prepared with two or more constituent materials. The effects of this nanoparticle addition (a.k.a reinforcement) on the mechanical properties of the nanocomposite polymer materials are studied. Specifically, efforts are focused on studying fracture toughness, a material property that describes the material's ability to resist crack growth. Fracture toughness is a critical material property often associated with material and structural failures, and as such it is very important for safe and reliable engineering design of structures, components, and materials. Moving from a single function (i.e. mechanical enhancement) to a more multi-functional role, taking advantage of the excellent electrical and mechanical abilities of CNTs, a structural health monitoring system is developed for use in polymer bonded energetics (eg. solid rocket propellants). When a material undergoes mechanical deformation or damage, the measured electrical properties of the material undergo some change as well. Using sensor networks built with multiple CNTs dispersed within a polymeric material, a whole structure can be made into an effective sensor where by simply monitoring the electrical properties, the extent of material deformation and damage can be known. Such a system is geared towards providing early warning of impending catastrophic material failures thus directly improving the safety during material handling and operations.

Yes / Ultra-high performance concrete (UHPC) is an innovative cementitious composite, that has been widely applied in numerous structural projects because of its superior mechanical properties and durability. However, ensuring the safety of UHPC structures necessitates an urgent need for technology to continuously monitor and evaluate their condition during their extended periods of service. Self-sensing ultra-high performance concrete (SSUHPC) extends the functionality of UHPC system by integrating conductive fillers into the UHPC matrix, allowing it to address above demands with great potential and superiority. By measuring and analyzing the relationship between fraction change in resistivity (FCR) and external stimulates (force, stress, strain), SSUHPC can effectively monitor the crack initiation and propagation as well as damage events in UHPC structures, thus offering a promising pathway for structural health monitoring (SHM). Research on SSUHPC has attracted substantial interests from both academic and engineering practitioners in recent years, this paper aims to provide a comprehensive review on the state of the art of SSUHPC. It offers a detailed overview of material composition, mechanical properties and self-sensing capabilities, and the underlying mechanisms involved of SSUHPC with various functional fillers. Furthermore, based on the recent advancements in SSUHPC technology, the paper concludes that SSUHPC has superior self-sensing performance under tensile load but poor self-sensing performance under compressive load. The mechanical and self-sensing properties of UHPC are substantially dependent on the type and dosage of functional fillers. In addition, the practical engineering SHM application of SSUHPC, particularly in the context of large-scale structure, is met with certain challenges, such as environment effects on the response of SSUHPC. Therefore, it still requires further extensive investigation and empirical validation to bridge the gap between laboratory research and real engineering application of SSUHPC. / The full-text of this article will be released for public view at the end of the publisher embargo on 28 Dec 2024.