This paper covers reliability assessment of the fatigue life of a bridge-deck section based on the statistical analysis of the strain-time histories measured by the structural health monitoring system permanently installed on the long-span steel bridge under study. Through statistical analysis of online strain responses in the frequency domain using multiple linear regression, a representative block of daily cycles of strain history is obtained. It is further assumed that all cycles of online strain response during bridge service are repetitions of the representative block. The rain-flow counting method is then used to determine the stress spectrum of the representative block of daily cycles. The primary assessment of fatigue life at a given value of failure probability is undertaken for the sample component in a bridge-deck section by using the classification of details for welded bridge components and the associated statistical fatigue model provided by the British Standard BS5400. In order to evaluate bridge fatigue at any value of failure probability, a modified probability model is proposed based on BS5400. The fatigue life of the considered component in the bridge-deck section is then evaluated for some other values of probability of failure which are not included in BS5400 by use of the modified probability model. The analytical results show that the modified model is practical for reliable evaluation of the service life of existing bridges under random traffic loading.

Statistical analysis of online strain response and its application in fatigue assessment of a long-span steel bridge

In this paper, we investigate changes in the mechanical properties of complex structures using a combination of the discrete model, Fast Fourier Transform (FFT) analysis and deep learning. The first idea from this research utilizes the discrete model from a perspective that is different from the finite element method (FEM) of previous works. As the method in this paper only models the mechanical properties of structures with finite degrees of freedom instead of dividing them into smaller elements, it reduces error in evaluation and produces more realistic results compared to the FEM model. Another advantage is how it allows the research to survey both parameters that affect the mechanical properties of structures—the overall stiffness (K) and the damping coefficient (c)—during vibration, while previous researches focus only on one of these two parameters. The second idea is to use FFT analysis to increase the sensitivity of the signal received during vibration. FFT analysis simplifies calculations, thereby reducing the effect of noise or errors. The sensitivity achieved in FFT analysis increases by 25% compared to traditional Fourier Transform (FT) analysis; moreover, the error in FFT analysis compared to experimental results is quite small, less than 2%. This shows that FFT is a suitable method to identify sensitive characteristics in evaluating changes in the mechanical properties. When FFT is combined with the discrete model, results are much better than those of several existing approaches. For the last idea, the manuscript applies deep learning (FFT-deep learning) in the noise reduction process for the original data. This makes the results much more accurate than in previous studies. The results of this research are shown through the monitoring of spans of the Saigon Bridge—the biggest and most important bridge in Ho Chi Minh City, Vietnam—during the past 11 years. The correspondence between the theoretically obtained result and the experimental one at the Saigon Bridge suggests a new area for development in evaluating and forecasting structural changes in the future.

A Data-Driven Approach to Structural Health Monitoring of Bridge Structures Based on the Discrete Model and FFT-Deep Learning

Damage detection in beam structures using bayesian deep learning and balancing composite motion optimization

A deep learning platform for evaluating energy loss parameter in engineering structures

Using the vibration signals of damaged beams under the act of a moving load, this article investigates changes in the power spectral density caused by beam damage. Based on the results, the authors have proposed a parameter to monitor a structure’s deteriorating conditions. Cracks were created in beams that changed the stiffness of the structure, and simulations of deterioration were conducted. The features proposed in this article are based on the changing shapes of power spectral density from which deterioration in a damaged beam structure can be detected. The experiment was designed to achieve the simulation that best corresponds to a realistic flow of traffic along bridges. Acceleration sensors installed along the beams received the vibration signals that were used to establish the power spectral density. The results of this article show that for a damaged beam, changes in the shapes of power spectral density are much more prominent than changes in the fundamental frequency value. In other words, when compared to using fundamental frequency changes, using shape changes in power spectral density will increase the likelihood of detecting damage for a variety of beam structures.

Power Spectral Density of Defect Beams Under a Moving Load

This research proposes a correlation coefficient for detecting and evaluating defects in beams, which brings about a positive outcome in terms of accuracy and efficiency. This parameter surpasses other parameters, such as natural frequency and damping coefficient, thanks to its sensitivity to structural changes. Our results show that although the damping coefficient had more variation than the natural frequency value in the same experiment, its changes were insufficient and unstable at different levels of defects. In addition, the proposed correlation coefficient parameter has a linear characteristic and always changes significantly according to increasing levels of defects. The results outweigh damping coefficient and natural frequency values. Furthermore, this value is always sensitive to measurement channels, which could be an important factor in locating defects in beams. The testing index is statistically evaluated by a normal distribution of the amplitude value of vibration measurement signals. Changes and shifts in this distribution are the basis for evaluating beam defects. Thus, the suggested parameter is a reliable alternative for assessing the defects of a structure.

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Detecting and Evaluating Defects in Beams by Correlation Coefficients

This article proposes a new parameter in evaluating mechanical behaviors of defected bridge spans. It is Moment Cumulative Function of Power Spectral Density (MCF-PSD) based on changes in shape of power spectrum and trained via cumulative function of spectral moment value by deep learning model. This new parameter allows evaluating stiffness attenuation along time, thereby helps to forecast the workability of bridge span. It can identify risky positions in not only a bridge span but also various spans of the same bridge, which proves its sensitivity to the structure’s behavior change over time. This study reveals that training MCF-PSD using cumulative function algorithm has gained outstanding results in comparison with previous studies in structural quality assessment. Therefore, it fulfills criteria of evaluating the damage level in a structure and also fosters new development of defect diagnosis and forecast. Conclusions from this study show that the change of this function is the basis to evaluate difference among measurement positions in the same span or among different spans of the same bridge and behaviors at different positions in the same span. Therefore, MCF-PSD is more sensitive than other parameters in evaluating the structure’s stiffness attenuation.

Hoang B. Nguyen

Papers

Detecting and Evaluating Defects in Beams by Correlation Coefficients

Structural health monitoring of bridge spans using Moment Cumulative Functions of Power Spectral Density (MCF-PSD) and deep learning