924 publications from this institution
{Fibre-optic Bragg Grating (FBG) strain sensors hold a great potential for vibration monitoring of civil structures because of their exceptional stability and accuracy; however, the accurate measurement of the very small strains levels occurring during ambient, or operational, excitation has been so far problematic. There are two ways to improve the measurement resolution: employing a strain-enhancing sensor package, and applying an improved wavelength detection algorithm. In this work, the potential of an improved wavelength detection algorithm for the identification of modal characteristics based on sub-microstrain data is investigated. The strategy is illustrated for a steel beam to which a chain of multiplexed FBG sensors has been attached at the top side of the beam. The raw FBG data are processed into strain values with an algorithm that is based on detecting the peak shifts in the wavelength spectrum by correlation analysis rather than by simply tracking the peak values. Subsequently, the strain sequences are used for identification of modal characteristics of the beam (natural frequencies and strain mode shapes) with ``Covariance driven Stochastic Subspace Identification (SSI/cov){''}. Computational results of a Finite Element Model are used to validate the experimental results.}
No abstract is provided for this article.
A monitoring campaign has been performed on a filler beam railway bridge for high-speed trains, measuring strains and accelerations due to train passages. For this purpose a very dense grid of measurement sensors was applied to the bottom side of the bridge deck, which allows the calculation of bridge deformations from strain measurements. Within this calculation the location of the cross section's neutral axis plays an important role, since it is required to derive curvatures from strains. The contribution shows how this location can be determined by means of modal analysis, using both acceleration and strain measurement data from decay curves after train passages. After a detailed description of the evaluation method experimental tests on a downscaled bridge deck are presented to prove the reliability of the proposed method. Similar to the situation on the real bridge deck, strain and acceleration sensors were distributed along the longitudinal axis of the test specimen. By measuring not only strains and accelerations but also deformations due to various kinds of excitation the evaluation method could be validated, since the calculated and measured location of the neutral axis and also the calculated and measured deformations were in good agreement. The evaluation method was successfully applied to measurement data from the presented monitoring campaign. The results are of great interest in view of assessing the bridge's dynamic behaviour. They also reveal valuable information about the cross-section properties of the monitored bridge deck which is finally discussed.
No abstract is provided for this article.
This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.
This paper deals with the problem of damage detection using output-only vibration measurements under changing environmental conditions. Two types of features are extracted from the measurements: eigenproperties of the structure using an automated stochastic subspace identification procedure and peak indicators computed on the Fourier transform of modal filters. The effects of environment are treated using factor analysis and damage is detected using statistical process control with the multivariate Shewhart- T control charts. A numerical example of a bridge subject to environmental changes and damage is presented. The sensitivity of the damage detection procedure to noise on the measurements, environment and damage is studied. An estimation of the computational time needed to extract the different features is given, and a table is provided to summarize the advantages and drawbacks of each of the features studied.
Este articulo trata de la determinacion de zonas de seguridad y minimos locales mediante el uso de analisis de elementos finitos. Se utiliza el metodo de elementos finitos para no restringir el analisis por los supuestos en la ubicacion de la superficie de deslizamiento ni en la funcion de fuerza entre dovelas. Se sabe que solo el mecanismo de falla mas critico y el minimo global son evaluados por el metodo de reduccion de resistencia, en tal enfoque los minimos locales la mayoria de las veces pasan desapercibidos. Aqui, se propone que las zonas de seguridad y los minimos locales se puedan detectar manteniendo la informacion generada en el proceso de reduccion de fuerza. Ademas, se destaca la importancia de las propiedades del suelo en la ubicacion del mecanismo de falla. La metodologia se presenta en un caso de estudio artificial y en un talud natural real. Las zonas de seguridad deben considerarse en la estabilizacion y remediacion de deslizamientos de tierra.
No abstract is provided for this article.
A new optimisation method to find the global minimum of a cost function, named Coupled Local Minimisers (CLM), is presented. In CLM, several local optimisation processes are coupled, i.e. they interact and exchange information, resulting in better solutions than multi-start local optimisation with independent runs. The principal idea of CLM is worked out and illustrated with a mathematical test function, on which successful optimisation runs are carried out. Next, CLM is applied to finite element (FE) model updating. FE models of civil engineering structures are updated by minimising differences between experimental and numerical modal data, i.e. natural frequencies and mode shapes. The former reflect the global dynamic behaviour and can be measured quite accurately, the latter contain local information but the measured values are more noisy.
Vibration-Based Monitoring (VBM) from modal strains is a promising alternative to traditional VBM methods, which are primarily based on exclusively monitoring natural frequencies. The advantages of modal strains are that they offer a lower sensitivity to temperature and a higher sensitivity to small-scale damage, when compared to natural frequencies, rendering them ideal for detecting damage at an early stage. Furthermore, modal strains can be obtained in a dense grid with a relatively low cost, when fiber-optic sensors, such as fiber-Bragg gratings (FBG) are employed, which enables the monitoring of several critical locations and increases the possibility of early damage detection and localization. Modal strains can be obtained with high accuracy and precision thanks to a recently developed methodology that makes use of FBG sensors and of a high resolution acquisition system. The temperature sensitivity of modal strains has been investigated in the field but their damage sensitivity has previously only been investigated through laboratory experiments and numerical simulations. In this study, a 110-years-old steel railway bridge is monitored with 80 FBGs. Its modal strains and natural frequencies are automatically identified with the use of hierarchical clustering from ambient and operational dynamic strains. Before the scheduled replacement of the bridge, artificial damage is introduced by means of local cuts, simulating fatigue cracks, in order to investigate the damage detection and localization capabilities of modal strains on a full-size civil structure for the first time. The influence of temperature and operational factors is also investigated and compared with that of damage.
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This paper investigates on the use of advanced optical measurement methods, i.e. 3D coordinate measurement machines (3D CMM) and stereo-vision digital image correlation (3D DIC), for the mechanical analysis of shear deficient prestressed concrete members. Firstly, the experimental program is elaborated. Secondly, the working principle, experimental setup and corresponding accuracy and precision of the considered optical measurement techniques are reported. A novel way to apply synthesised strain sensor patterns for DIC is introduced. Thirdly, the experimental results are reported and an analysis is made of the structural behaviour based on the gathered experimental data. Both techniques yielded useful and complete data in comparison to traditional mechanical measurement techniques and allowed for the assessment of the mechanical behaviour of the reported test specimens. The identified structural behaviour presented in this paper can be used to optimize design procedure for shear-critical structural concrete members.
