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The latest studies on failures in steel truss-type bridge found that they are highly vulnerable to damage and thus prone to potential local or total collapse. Many authors recommend monitoring the critical elements in the existing steel truss-type bridges in real time to anticipate any failures in local members. Although mechanical strain is the most frequently used variable for this purpose, this method also happens to be the most expensive monitoring strategy. This paper describes a case study of failure scenarios assessment in a steel truss-type railway bridge after extensive long-term monitoring conducted by the authors, based on measuring vertical deflections and modal frequencies. The structure has both an isostatic and a hyperstatic configuration, and was assessed by means of a combination of: (i) long-term monitoring results, and (ii) a finite element analysis to simulate several failure scenarios. A sensitivity study of the different failure scenarios has been carried out, identifying those that can be detected. The results are used to define practical recommendations for failure detection by measuring vertical deflections and modal frequencies.

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-91877-4_22., Monitoring structural damage is widely used for sustaining and preserving the service life in civil structures, especially in bridges. The influence of environmental variability like temperature affects the dynamic behavior, which can mask subtler structural changes caused by damage. The direct application of vibration-based damage detection methods to measured responses without a prior treatment of the ambient data may lead to false condition assessments. In this article, the main objective is to separate the structural damage conditions from the changes caused by the environmental effects in a numerical benchmark bridge. The Principal Component Analysis (PCA) is applied to decide if the change in vibration characteristics is due to environmental effects or structural damages. The proposed approach in the use of PCA not only allows to detect the damage without the requirement of the baseline to consist of damage sensitivity features obtained from a wide range of environmental conditions, but also serves as a measure for its quantification. The effectiveness and robustness of the proposed methodology is applied to a benchmark bridge structure generated as part of COST Action TU1402 on quantifying the value of information (VoI) in SHM. The benchmark model consisted of a two-span steel bridge under environmental effects, in which two levels of damage were introduced., Peer Reviewed, Postprint (author's final draft)

This thesis presents the development of non-destructive characterization of functional material properties of additively manufactured metals. In particular, this study was focused on the common structural stainless steel alloy, 316L, utilizing a coupon designed specifically for simple modal analysis. Additive manufacturing (AM) has moved to the forefront of the manufacturing world, particularly in aerospace and defense segments because of the potential to produce multi-functional, highly optimized components. The ability to confidently qualify these complex, and thus expensive, components has been lagging behind the advancing technology. The adoption of traditional characterization techniques developed for wrought materials has been most common, and much useful data can be extracted from these methods, but many of these tests are destructive in nature and thus are performed on representative samples. By taking advantage of the Modal Frequency Technique (MFT), the functional bulk properties of an as-printed component can be accurately characterized and the anisotropies inherent to AM can be quantified. Tests were performed using laser Doppler vibrometry and coupled with finite element analysis to show the ability to determine the functional Young’s modulus of AM coupons. These tests also identified a dependence of this bulk Young’s modulus on both the print orientation and the feature thickness. Modal frequencies were determined across a range of material parameters and experimental data was aligned with these values to determine the resultant Young’s modulus for a suite of coupon dimensions printed in to traditional orientations using powder bed direct metal laser sintering (DMLS).

This thesis presents the development of non-destructive characterization of functional material properties of additively manufactured metals. In particular, this study was focused on the common structural stainless steel alloy, 316L, utilizing a coupon designed specifically for simple modal analysis. Additive manufacturing (AM) has moved to the forefront of the manufacturing world, particularly in aerospace and defense segments because of the potential to produce multi-functional, highly optimized components. The ability to confidently qualify these complex, and thus expensive, components has been lagging behind the advancing technology. The adoption of traditional characterization techniques developed for wrought materials has been most common, and much useful data can be extracted from these methods, but many of these tests are destructive in nature and thus are performed on representative samples. By taking advantage of the Modal Frequency Technique (MFT), the functional bulk properties of an as-printed component can be accurately characterized and the anisotropies inherent to AM can be quantified. Tests were performed using laser Doppler vibrometry and coupled with finite element analysis to show the ability to determine the functional Young’s modulus of AM coupons. These tests also identified a dependence of this bulk Young’s modulus on both the print orientation and the feature thickness. Modal frequencies were determined across a range of material parameters and experimental data was aligned with these values to determine the resultant Young’s modulus for a suite of coupon dimensions printed in to traditional orientations using powder bed direct metal laser sintering (DMLS).

Future space missions are expected to use robotic systems to assemble, inspect, and maintain large space structures automatically in orbit. For effective planning and control, robots must know the deformations and motions of the structures with which they interact. This paper presents a method for estimating the shape, motion, and dynamic model parameters of a vibrating space structure using asynchronous raster-scanning range imagers. We mounted a flexible panel, which emulates the motion of the large space structure, on an air table. We then conducted experiments using the representative structure and asynchronous raster-scanning range imagers to evaluate the performance of the estimation algorithm., 将来、ロボットが自律的に軌道上での大型柔軟宇宙構造物の組立や不具合検知、修理を行うことが期待される。その際に計画立案や制御を効率よく行うためにはロボット自身と相互作用する構造物の変形や動きを知る必要がある。本稿は非同期ラスタスキャン方式のレンジイメージャを用いて振動するモードシェイプ既知の宇宙構造物の形状や動き、モデルパラメタなどを推定する方法を述べる。本手法は近似的なモードシェイプが既知であると仮定し、その情報と振動モデルを利用し、モーダル係数やモーダル周波数、ダンピング係数などを推定することができる。筆者らは宇宙空間での柔軟構造物の低周波部分の動きを模擬できるシステムを空気浮上式定盤の上に構築し、本手法の有効性を実験により検証した。

Quantifying the uncertainty in the dynamic properties of large-scale complex engineering structures presents significant computational challenges. Monte Carlo simulation (MCS) method is extensively employed to perform uncertainty quantification (UQ) because of its generality, stability, and easy implementation. However, a brute-force MCS approach may be unaffordable and impractical when the target model contains a large number of uncertain parameters. In this circumstance, MCS requires a potentially burdensome (if not computationally intractable) number of model evaluations to obtain a credible estimate of the global statistics. In this study, a general framework for analytical UQ of model outputs using a Gaussian process (GP) metamodel is presented, where case inputs are characterized as normal and/or uniform random variables. A detailed derivation of important low-order statistical moments (mean and variance) is given analytically. This analytical method is adopted to characterize the uncertainty of modal frequencies of two bridges with assumed normally- and uniformly-distributed parameters. Meanwhile, the brute-force MCS approach is used for comparison of GP metamodel-derived statistics. Results show that the GP method outperforms the MCS methodology in terms of computational cost, with consistency in the "true" values obtained by MCS. It demonstrates that this GP method is feasible and reliable for modal frequency UQ of complex structures. © 2014 Elsevier Ltd.

Quantifying the uncertainty in the dynamic properties of large-scale complex engineering structures presents significant computational challenges. Monte Carlo simulation (MCS) method is extensively employed to perform uncertainty quantification (UQ) because of its generality, stability, and easy implementation. However, a brute-force MCS approach may be unaffordable and impractical when the target model contains a large number of uncertain parameters. In this circumstance, MCS requires a potentially burdensome (if not computationally intractable) number of model evaluations to obtain a credible estimate of the global statistics. In this study, a general framework for analytical UQ of model outputs using a Gaussian process (GP) metamodel is presented, where case inputs are characterized as normal and/or uniform random variables. A detailed derivation of important low-order statistical moments (mean and variance) is given analytically. This analytical method is adopted to characterize the uncertainty of modal frequencies of two bridges with assumed normally- and uniformly-distributed parameters. Meanwhile, the brute-force MCS approach is used for comparison of GP metamodel-derived statistics. Results show that the GP method outperforms the MCS methodology in terms of computational cost, with consistency in the "true" values obtained by MCS. It demonstrates that this GP method is feasible and reliable for modal frequency UQ of complex structures. © 2014 Elsevier Ltd.

Random Decrement (RANDEC) Analysis was applied to time histories of roll motion for the Spruance class destroyer. The data was generated from sea trials conducted on a ship in 1987. The RANDEC process was applied to obtain estimates of roll- resonance modal damping and frequencies due to ship motion excitation by random-natured hydrodynamic forces and moments. Prior to applying the RANDEC technique to ship data, a numerical algorithm was developed and validated using reference data sets with known dynamic traits approaching that expected of the ship., http://archive.org/details/shiprollmodeinfo1094528531, Lieutenant, United States Navy, Approved for public release; distribution is unlimited.

The main aim of this work is to extract modal parameters of a light aircraft wing. Modal parameters like modal frequency, modal damping and mode shapes characterise the dynamic response of a particular structure and can be used for determining an excess wing flexure as well as the long-term effect of fatigue.For the experimental part the response of the Piper Warrior II was recorded during ground runs. A set of temporarily attached accelerometers was used to record the response of the wing and the signals were recorded. Subsequently a Frequency Response Function was generated in the post-processing stage, which resulted in determining the first and second bending modes as well as the first twisting mode. A Finite Elements Model was applied to validate the results.Due to the limitations of the taxiing speed the method cannot excite higher frequency modes; however the findings can find numerous applications in real time in-flight flexure monitoring.

From the point of view of structural health monitoring, it is extremely important to discriminate alteration in structural behavior/response attribute due to damage from that due to environmental and operational fluctuation. In this paper, the correlation between natural frequencies and temperature is investigated for the cable-stayed Ting Kau Bridge by using measurement data from a long-term monitoring system installed on this bridge. One-year continuously acquired data from 45 accelerometers (a total of 67 channels) and 83 temperature sensors are used for this study. The data from 20 temperature sensors at the locations susceptible to temperature are first selected for the correlation analysis. Natural frequencies of the first 10 modes are identified by spectral analysis of the acceleration data at one-hour intervals. In order to ensure the identification accuracy, the natural frequency for a specific mode is determined using only the data from the accelerometers which produce large spectral peaks at that mode. The identified natural frequencies for each hour are used to correlate with the one-hour average temperatures measured from the 20 sensors during the same time. Based on the one-year measurement data which cover a full cycle of varying environmental and operational conditions, a four-layer perceptron neural network with 20 input nodes and 1 output node is trained for each mode to represent the relation between the measured temperatures (input) and the corresponding natural frequency (output). The configured neural networks for the 10 modes show excellent capabilities for mapping between the temperatures and natural frequencies for all the one-year measurement data.

Random Decrement (RANDEC) Analysis was applied to time histories of roll motion for the Spruance class destroyer. The data was generated from sea trials conducted on a ship in 1987. The RANDEC process was applied to obtain estimates of roll-resonance modal damping and frequencies due to ship motion excitation by random-natured hydrodynamic forces and moments. Prior to applying the RANDEC technique to ship data, a numerical algorithm was developed and validated using reference data sets with known dynamic traits approaching that expected of the ship.

GPS and accelerometers have been broadly used the last decade for the monitoring of flexible structures and bridges, while Robotic Total Station (RTS) has been successfully assessed for the monitoring of slow and dynamic motions. Further experimental studies have revealed specific drawbacks of the GPS (multipath, etc.) and RTS (clipping, etc.) monitoring techniques and how these can be surpassed by their combined use. In the current study, we assess the performance of the complementary use of GPS and RTS for the monitoring of the semi-static and dynamic displacement of a relatively rigid pedestrian bridge, with main modal frequency more than 1Hz. Two RTS and GPS receivers were synchronised monitoring the deflection of the two sides of the mid-span of the bridge, while pedestrians excited the bridge. Several excitations (walking, marching, jumping, etc.) were examined, causing semi-static and/or dynamic displacement of the bridge, and rotation of the deck, of different amplitude and frequencies. The analysis of the RTS and GPS time-series, which was based on spectral analysis and band-pass filtering of the time-series, resulted to low- and high-frequency component expressing the semi-static and dynamic displacement. Finally, the combination of the GPS and RTS time-series made possible the estimation of 1-4mm semi-static displacement, the 5-10mm dynamic displacement and the estimation of the main modal frequencies.

GPS and accelerometers have been broadly used the last decade for the monitoring of flexible structures and bridges, while Robotic Total Station (RTS) has been successfully assessed for the monitoring of slow and dynamic motions. Further experimental studies have revealed specific drawbacks of the GPS (multipath, etc.) and RTS (clipping, etc.) monitoring techniques and how these can be surpassed by their combined use. In the current study, we assess the performance of the complementary use of GPS and RTS for the monitoring of the semi-static and dynamic displacement of a relatively rigid pedestrian bridge, with main modal frequency more than 1Hz. Two RTS and GPS receivers were synchronised monitoring the deflection of the two sides of the mid-span of the bridge, while pedestrians excited the bridge. Several excitations (walking, marching, jumping, etc.) were examined, causing semi-static and/or dynamic displacement of the bridge, and rotation of the deck, of different amplitude and frequencies. The analysis of the RTS and GPS time-series, which was based on spectral analysis and band-pass filtering of the time-series, resulted to low- and high-frequency component expressing the semi-static and dynamic displacement. Finally, the combination of the GPS and RTS time-series made possible the estimation of 1-4mm semi-static displacement, the 5-10mm dynamic displacement and the estimation of the main modal frequencies.

GPS and accelerometers have been broadly used the last decade for the monitoring of flexible structures and bridges, while Robotic Total Station (RTS) has been successfully assessed for the monitoring of slow and dynamic motions. Further experimental studies have revealed specific drawbacks of the GPS (multipath, etc.) and RTS (clipping, etc.) monitoring techniques and how these can be surpassed by their combined use. In the current study, we assess the performance of the complementary use of GPS and RTS for the monitoring of the semi-static and dynamic displacement of a relatively rigid pedestrian bridge, with main modal frequency more than 1Hz. Two RTS and GPS receivers were synchronised monitoring the deflection of the two sides of the mid-span of the bridge, while pedestrians excited the bridge. Several excitations (walking, marching, jumping, etc.) were examined, causing semi-static and/or dynamic displacement of the bridge, and rotation of the deck, of different amplitude and frequencies. The analysis of the RTS and GPS time-series, which was based on spectral analysis and band-pass filtering of the time-series, resulted to low- and high-frequency component expressing the semi-static and dynamic displacement. Finally, the combination of the GPS and RTS time-series made possible the estimation of 1-4mm semi-static displacement, the 5-10mm dynamic displacement and the estimation of the main modal frequencies.

GPS and accelerometers have been broadly used the last decade for the monitoring of flexible structures and bridges, while Robotic Total Station (RTS) has been successfully assessed for the monitoring of slow and dynamic motions. Further experimental studies have revealed specific drawbacks of the GPS (multipath, etc.) and RTS (clipping, etc.) monitoring techniques and how these can be surpassed by their combined use. In the current study, we assess the performance of the complementary use of GPS and RTS for the monitoring of the semi-static and dynamic displacement of a relatively rigid pedestrian bridge, with main modal frequency more than 1Hz. Two RTS and GPS receivers were synchronised monitoring the deflection of the two sides of the mid-span of the bridge, while pedestrians excited the bridge. Several excitations (walking, marching, jumping, etc.) were examined, causing semi-static and/or dynamic displacement of the bridge, and rotation of the deck, of different amplitude and frequencies. The analysis of the RTS and GPS time-series, which was based on spectral analysis and band-pass filtering of the time-series, resulted to low- and high-frequency component expressing the semi-static and dynamic displacement. Finally, the combination of the GPS and RTS time-series made possible the estimation of 1-4mm semi-static displacement, the 5-10mm dynamic displacement and the estimation of the main modal frequencies.