Health monitoring of Bridges based on multifractal theory

Abstract The multifractal theory is applied in an analysis of bridge disturbance signals with the aim of investigating their nonlinear characteristics, and then the recognisable fault features are extracted from them. By calculating the box dimension and correlation dimension of the bridge disturbance signal, the dimensional characteristics of the disturbance data are analysed to distinguish the health-state of the bridge. Finally, taking the bridge disturbance data as an example, and by using the multifractal spectrum analysis of the disturbance data, it is concluded that the multifractal method can accurately identify the fault state and realise the bridge health monitoring.

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Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

The Journal of Supercomputing ◽

10.1007/s11227-019-03045-8 ◽

2019 ◽

Vol 76 (2) ◽

pp. 932-947 ◽

Cited By ~ 1

Author(s):

Aiping Guo ◽

Ajuan Jiang ◽

Jie Lin ◽

Xiaoxiao Li

Keyword(s):

Neural Network ◽

Health Monitoring ◽

Short Term Memory ◽

Bridge Engineering ◽

Normal Operation ◽

Massive Data ◽

Health State ◽

Bridge Structure ◽

Continuous Growth ◽

Bridge Health Monitoring

Abstract In recent years, bridge health monitoring system has been widely used to deal with massive data produced with the continuous growth of monitoring time. However, how to effectively use these data to comprehensively analyze the state of a bridge and provide early warning of bridge structure changes is an important topic in bridge engineering research. This paper utilizes two algorithms to deal with the massive data, namely Kohonen neural network and long short-term memory (LSTM) neural network. The main contribution of this study is using the two algorithms for health state evaluation of bridges. The Kohonen clustering method is shown to be effective for getting classification pattern in normal operating condition and is straightforward for outliers detection. In addition, the LSTM prediction method has an excellent prediction capability which can be used to predict the future deflection values with good accuracy and mean square error. The predicted deflections agree with the true deflections, which indicate that the LSTM method can be utilized to obtain the deflection value of structure. What’s more, we can observe the changing trend of bridge structure by comparing the predicted value with its limit value under normal operation.

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NONLINEAR CHARACTERISTICS OF ACOUSTIC EMISSION DURING THE HEATING PROCESS OF COAL AND ROCK

Fractals ◽

10.1142/s0218348x18500469 ◽

2018 ◽

Vol 26 (04) ◽

pp. 1850046 ◽

Cited By ~ 2

Author(s):

ZHIBO ZHANG ◽

ENYUAN WANG ◽

ENLAI ZHAO ◽

SHUAI YANG

Keyword(s):

Acoustic Emission ◽

Hurst Exponent ◽

Multifractal Spectrum ◽

Heating Process ◽

Nonlinear Characteristics ◽

Rock Samples ◽

Spectrum Width ◽

Multifractal Theory ◽

Ae Signal ◽

Temperature Levels

In this paper, acoustic emission (AE) signal of coal and rock samples during the heating process are measured. The results show that AE energy of coal samples is higher than that of rock samples. Based on the multifractal theory, the multifractal characteristics of AE signal are researched. The multifractal spectrum width ([Formula: see text]) of coal samples is wider than that of rock samples, which means AE signal of coal samples is more complex than AE signal of rock samples during the heating process. Multifractal parameter ([Formula: see text]) is more than zero, illustrating that small AE signal is dominate. The time-varying multifractal characteristics are analyzed, and the change trend of multifractal spectrum width ([Formula: see text]) of coal and rock samples is consistent. At the stage of 40–50[Formula: see text]C, multifractal spectrum width ([Formula: see text]) gets the maximum value, whereas multifractal spectrum width ([Formula: see text]) gets the minimum value at the stage of 80–100[Formula: see text]C. For coal samples, multifractal parameter ([Formula: see text] is more than zero except at the stage of 40–50[Formula: see text]C. However, multifractal parameter ([Formula: see text] of rock samples is always more than zero during the entire heating process. By [Formula: see text] analytical method, Hurst exponent of AE signal is calculated. The results show that Hurst exponent of coal and rock samples are more than 0.5, which indicate that AE signal presents persistence, and there is a positive correction between AE signal and temperature. In different temperature levels, Hurst exponent curve presents an increase trend after the initial decrease.

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Fault Diagnosis of the Wind Turbine Main Bearing through Multifractal Theory

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.644.337 ◽

2013 ◽

Vol 644 ◽

pp. 337-340

Author(s):

Quan Gu ◽

Chang Zheng Chen ◽

Xiang Jun Kong ◽

Xian Ming Sun ◽

Bo Zhou ◽

...

Keyword(s):

Wind Turbine ◽

Large Scale ◽

Spectral Characteristics ◽

Multifractal Spectrum ◽

Hölder Exponent ◽

Main Bearing ◽

Vibration Signals ◽

Fault Features ◽

Multifractal Theory ◽

Incipient Fault

Because the vibration signals of faulty wind turbine are non-linear and non-stationary, to obtain the obvious fault features become difficult. In this study, the incipient fault of the main bearing used in large scale wind turbine is studied by using a multifractal method based on the Wavelet Modulus Maxima (WTMM) method. The real vibration signals from the main bearings are analyzed using the multifractal spectrum. The spectrum of the vibration signals is quantified by spectral characteristics including its range and the Hölder exponent corresponding to the maximum dimension. The results show that the range of Hölder exponent of the main bearing which worked normally is much narrower. While the ranges of the vibration signals of the main bearing with incipient fault are wider. We also found that the fault features are different at various wind turbine rotational frequencies. Those demonstrate that the incipient fault features of main bearing of large scale wind turbine can be extract effectively using the multifractal spectrum obtained from WTMM method.

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Numerical Parametric Study on the Effectiveness of the Contact-Point Response of a Stationary Vehicle for Bridge Health Monitoring

Applied Sciences ◽

10.3390/app11157028 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7028

Author(s):

Ibrahim Hashlamon ◽

Ehsan Nikbakht ◽

Ameen Topa ◽

Ahmed Elhattab

Keyword(s):

Numerical Model ◽

Health Monitoring ◽

Contact Point ◽

Response Spectra ◽

Roughness Effects ◽

Moving Vehicle ◽

Bridge Health Monitoring ◽

Vehicle Response ◽

Instrumented Vehicle ◽

The Time Domain

Indirect bridge health monitoring is conducted by running an instrumented vehicle over a bridge, where the vehicle serves as a source of excitation and as a signal receiver; however, it is also important to investigate the response of the instrumented vehicle while it is in a stationary position while the bridge is excited by other source of excitation. In this paper, a numerical model of a stationary vehicle parked on a bridge excited by another moving vehicle is developed. Both stationary and moving vehicles are modeled as spring–mass single-degree-of-freedom systems. The bridges are simply supported and are modeled as 1D beam elements. It is known that the stationary vehicle response is different from the true bridge response at the same location. This paper investigates the effectiveness of contact-point response in reflecting the true response of the bridge. The stationary vehicle response is obtained from the numerical model, and its contact-point response is calculated by MATLAB. The contact-point response of the stationary vehicle is investigated under various conditions. These conditions include different vehicle frequencies, damped and undamped conditions, different locations of the stationary vehicle, road roughness effects, different moving vehicle speeds and masses, and a longer span for the bridge. In the time domain, the discrepancy of the stationary vehicle response with the true bridge response is clear, while the contact-point response agrees well with the true bridge response. The contact-point response could detect the first, second, and third modes of frequency clearly, unlike the stationary vehicle response spectra.

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Challenges in Bridge Health Monitoring: A Review

Sensors ◽

10.3390/s21134336 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4336

Author(s):

Piervincenzo Rizzo ◽

Alireza Enshaeian

Keyword(s):

United States ◽

Health Monitoring ◽

Highway Bridges ◽

The United States ◽

Anomalous Behavior ◽

Existing Structures ◽

Bridge Health Monitoring ◽

Using Data ◽

The Many ◽

Major Factors

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures.

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