Bridge condition monitoring under moving loads using two sensor measurements

2019 ◽  
Vol 19 (3) ◽  
pp. 917-937 ◽  
Author(s):  
Zhenhua Nie ◽  
Jun Lin ◽  
Jun Li ◽  
Hong Hao ◽  
Hongwei Ma

A novel damage detection approach using only two sensors to detect the damage in beam bridges subjected to a moving vehicle is proposed in this article. In this approach, a moving mass is considered representing a vehicle moving across the bridge, and structural vibration responses at two locations are measured from a pair of sensors. A moving window is defined with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses. The windowed pair time series extracted from these two measured responses are used to calculate the cross-correlation, which is used to define the local damage index. A simply supported beam bridge subjected to a moving mass is simulated to demonstrate the effectiveness and accuracy of the proposed approach. Numerical results indicate that the proposed approach can accurately identify the single and multiple damages using both displacement and acceleration responses, even when the responses are smeared with a significant noise. This indicates a good robustness to the noise effect. Experimental verifications on a laboratory beam bridge model demonstrate that the proposed approach can successfully identify the damage location using different selections of sensor pairs. Both the numerical and experimental results demonstrate that the new damage index is a good candidate for structural damage detection with very limited measurement information.

2007 ◽  
Vol 334-335 ◽  
pp. 1149-1152
Author(s):  
Long Yu ◽  
Yun Ju Yan ◽  
Jie Sheng Jiang ◽  
Li Cheng

A method based on entropy-based criteria is present to choose the optimal decomposition of Wavelet Packets Analysis (WPA) for damage detection in composite materials. The structural damage indexes constructed based on energy spectrum variation of the structural vibration responses decomposed using WPA before and after the occurrence of structural damage usually generate a complete binary tree to calculate its elements. Date mining is carried out in this paper by adoption entropy as the criteria to choose the optimal decomposition tree. In the decomposition process, only the sub-signals which contain main information of the original signal are decomposed to generate next level sub-signals. New damage index is constructed based on the optimal decomposition. Then the dimension of the damage index is reduced while still keeping its sensitive to damage. Whether Artificial Neural Network (ANN) or genetic algorithm (GA) is used in the further process of telling structural damage status from damage index, this reduction will make remarkable time saving.


2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Juntao Wu ◽  
Zhenhua Nie

A novel damage detection approach based on Auto-encoder neural network is proposed to identify damage in beam-like bridges subjected to a moving mass. In this approach, several sensors are used to measure structural vibration responses during a mass moving across the bridge. An auto-encoder (AE) neural network is designed to extract features from the measured responses. A fixed moving window is used to cut out the time-domain responses to generate inputs of the AE neural network. Moreover, some constraints are applied on the hidden layer to improve the performance of the AE network in training process. When the training is complete, the encoder was regarded as a feature extractor. And the damage index is defined as the cosine distance between two feature vectors obtained from adjacent data windows. By moving the window along the measured vibration data, we can calculate a damage index series and locate the damage position of the structure. To demonstrate the performance of the proposed method, numerical simulation is carried out. The results show that the proposed method can accurately locate both single and multiple damages using acceleration response. It infers the proposed method is promising for structural damage detection.


2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Zhenhua Nie ◽  
Tuan Ngo ◽  
Hongwei Ma

This paper presents a novel damage detection method based on the reconstructed phase space of vibration signals using a single sensor. In this approach, a moving mass is applied as excitation source, and the structure vibration responses at different positions are measured using a single sensor. A Moving Filter Function (MFF) is also presented to be used to separate and filter the responses before phase space reconstruction. Using the determined time delay and embedding dimensions, the responses are translated from time domain into the spatial domain. The index CPST (changes of phase space topology) values are calculated from the reconstructed phase space and used to identify structural damage. To demonstrate the method, six analysis scenarios for a beam-like structure considering the moving mass magnitude, damage location, the single sensor location, moving mass velocity, multiple types of damage, and the responses contaminated with noise are calculated. The acceleration and displacement responses are both used to identify the damage. The results indicate that the proposed method using displacement response is more sensitive to damage than that of acceleration responses. The results also proved that the proposed method can use a single sensor installed at different location of the beam to locate the damage/much damage reliably, even though the responses are contaminated with noise.


2022 ◽  
pp. 136943322110561
Author(s):  
Zhenhua Nie ◽  
Yongkang Xie ◽  
Jun Li ◽  
Hong Hao ◽  
Hongwei Ma

This paper proposes a data-driven method using subspace projection residual of the responses to identify the damage locations in bridges subjected to moving loads. In this method, a moving window with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses is used to cut out the acceleration responses of the bridge subjected to a moving vehicle. The characteristic subspaces of the windowed signals are subsequently extracted to calculate the local damage index using the subspace projection residual. When the window moves to the damage location, the orthogonality between the active subspace of the damaged state and the null subspace of the healthy state is invalid, which leads to a relatively large projection residual that can be used to localize the damage. To improve the reliability of the proposed approach, a one-side upper confidence limit is introduced. A simply supported beam bridge subjected to a moving mass is simulated to verify the effectiveness of the proposed method. Numerical results indicate that the proposed approach can accurately localize the single and multiple damages, even when the responses are smeared with a significant noise. Experimental tests conducted on a steel beam bridge model also demonstrate the performance and accuracy of the proposed approach. The results demonstrate that the proposed method can localize the damage even with a small number of sensors, indicating the method has a good and promising performance for practical engineering applications.


Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1271 ◽  
Author(s):  
Asma Alsadat Mousavi ◽  
Chunwei Zhang ◽  
Sami F. Masri ◽  
Gholamreza Gholipour

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.


Author(s):  
Chin-Hsiung Loh ◽  
Min-Hsuan Tseng ◽  
Shu-Hsien Chao

One of the important issues to conduct the damage detection of a structure using vibration-based damage detection (VBDD) is not only to detect the damage but also to locate and quantify the damage. In this paper a systematic way of damage assessment, including identification of damage location and damage quantification, is proposed by using output-only measurement. Four level of damage identification algorithms are proposed. First, to identify the damage occurrence, null-space and subspace damage index are used. The eigenvalue difference ratio is also discussed for detecting the damage. Second, to locate the damage, the change of mode shape slope ratio and the prediction error from response using singular spectrum analysis are used. Finally, to quantify the damage the RSSI-COV algorithm is used to identify the change of dynamic characteristics together with the model updating technique, the loss of stiffness can be identified. Experimental data collected from the bridge foundation scouring in hydraulic lab was used to demonstrate the applicability of the proposed methods. The computation efficiency of each method is also discussed so as to accommodate the online damage detection.


Author(s):  
Wen-Yu He ◽  
Wei-Xin Ren ◽  
Lei Cao ◽  
Quan Wang

The deflection of the beam estimated from modal flexibility matrix (MFM) indirectly is used in structural damage detection due to the fact that deflection is less sensitive to experimental noise than the element in MFM. However, the requirement for mass-normalized mode shapes (MMSs) with a high spatial resolution and the difficulty in damage quantification restricts the practicability of MFM-based deflection damage detection. A damage detection method using the deflections estimated from MFM is proposed for beam structures. The MMSs of beams are identified by using a parked vehicle. The MFM is then formulated to estimate the positive-bending-inspection-load (PBIL) caused deflection. The change of deflection curvature (CDC) is defined as a damage index to localize damage. The relationship between the damage severity and the deflection curvatures is further investigated and a damage quantification approach is proposed accordingly. Numerical and experimental examples indicated that the presented approach can detect damages with adequate accuracy at the cost of limited number of sensors. No finite element model (FEM) is required during the whole detection process.


2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.


Author(s):  
Zhiwei Chen ◽  
Yigui Zhou ◽  
Wen-Yu He ◽  
Mengqi Liu

The critical signal component extracted from the bridge response caused by a moving vehicle is normally used to construct damage index for damage detection. The dynamic response of bridges subjected to moving vehicle includes several components, among which the quasi-static component reflects the inherent characteristics of the bridge. In view of this, this paper presents a bridge damage detection method based on quasi-static component of the moving vehicle-induced dynamic response. First, damage-induced changes of the natural-frequency component, moving-frequency component and quasi-static component responses are investigated via a simply-supported beam bridge. The quasi-static component response is proved to be less sensitive to the moving velocity of the load and more suitable for damage detection. Subsequently, a quasi-static component response extraction method is proposed based on analytical mode decomposition (AMD) and moving average filter (MAF). The extracted quasi-static component response is further employed to localize and quantify damages. Finally, numerical simulations are conducted to examine the feasibility, accuracy and advantages of the proposed damage detection method. The results indicated that the proposed method performs well in different damage scenarios and is insensitive to the moving velocity of the load and road roughness.


2007 ◽  
Vol 353-358 ◽  
pp. 2317-2320 ◽  
Author(s):  
Zhe Feng Yu ◽  
Zhi Chun Yang

A new method for structural damage detection based on the Cross Correlation Function Amplitude Vector (CorV) of the measured vibration responses is presented. Under a stationary random excitation with a specific frequency spectrum, the CorV of the structure only depends on the frequency response function matrix of the structure, so the normalized CorV has a specific shape. Thus the damage can be detected and located with the correlativity and the relative difference between CorVs of the intact and damaged structures. With the benchmark problem sponsored by ASCE Task Group on Structural Health Monitoring, the CorV is proved an effective approach to detecting the damage in structures subject to random excitations.


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