scholarly journals A note on predetermined earthquake damage scenarios for structural health monitoring

2011 ◽  
Vol 19 (8) ◽  
pp. 746-757 ◽  
Author(s):  
Vlado Gičev ◽  
Mihailo D. Trifunac
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Earthquake Damage ◽  
Damage Scenarios ◽  
Structural Health

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

scholarly journals Output-only Structural Health Monitoring of a Riveted Steel Railway Bridge utilizing Proper Orthogonal Decomposition, Artificial Neural Network, and Strain Measurements

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
Ahmed Rageh ◽  
Saeed Eftekhar Azam ◽  
Daniel Linzell
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Operational Conditions ◽  
Damage Scenarios ◽  
Structural Health ◽  
Damage Indices ◽  
Wide Range ◽  
Artificial Neural ◽  
Output Only ◽  
Proper Orthogonal

This study presents a new scheme for autonomous health monitoring of railroad infrastructure using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge located in central Nebraska. The most common failure mode for the superstructure of this structural system is the stringer-to-floor beam connection failure, which was the focus of this study. However, the proposed methodology could be used to assess the condition of a wide range of structural elements and details. The damage feature adopted in this framework was the variations of Proper Orthogonal Modes (POMs) of the measured structural response. To automatically detect the occurrence, location, and intensity of deficiencies from the POMs, Artificial Neural Networks (ANN) were adopted. POM variations, which are traditionally input (load) dependent, were ultimately utilized as damage indicators. To alleviate the variability of POMs due to non-stationarity of the train loads, a preset windowing of measured output was completed in conjunction with automated peak-picking. Furthermore, input variability necessitated implementing ANNs to help decouple POM changes due to load variations from those caused by deficiencies, changes that would render the proposed framework input independent; a significant advancement. Damage “scenarios” were artificially introduced into select output (strain) datasets recorded while monitoring train passes across the selected bridge. This information, in turn, was used to train ANNs using MATLAB’s Neural Net Toolbox. Trained ANNs were tested against monitored loading events and artificial damage scenarios. Applicability of the proposed, output-only framework was investigated via studies of the bridge under operational conditions. To account for the effects of potential deficiencies at the stringer-to-floor beam connections, measured signal amplitudes were artificially decreased at select locations. Finally, to validate the applicability of the proposed method using low-cost measurement devices, the measured signals were corrupted by high levels of white, Gaussian noises featuring spatial correlations. It was concluded that the proposed framework could successfully identify 20 damage indices, which were artificially imposed on measured signals under operational conditions.

scholarly journals Application of Artificial Immune System in Structural Health Monitoring

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Jiachen Zhang ◽  
Zhikun Hou
Keyword(s):  
Immune System ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Artificial Immune System ◽  
Structural Parameter ◽  
Artificial Immune ◽  
Damage Scenarios ◽  
Structural Health ◽  
Specific Parameter ◽  
Parameter Values

A large number of methods have been proposed in the area of structural health monitoring (SHM). However, many of them rely on the prior knowledge of structural-parameter-values or the assumption that the structural-parameter-values do not change without damage. This dependence on specific parameter values limits these methods’ applicability. This paper proposes an artificial immune system- (AIS-) based approach for the civil structural health monitoring, which does not require specific parameter values to work. A linear three-floor structure model and a number of single-damage scenarios were used to evaluate the proposed method’s performance. The high success rate showed this approach’s great potential for the SHM tasks. This approach has merits of less dependence on the structural-parameter-values and low demand on the training conditions.

scholarly journals Stretching Method-Based Operational Modal Analysis of An Old Masonry Lighthouse

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3599 ◽  
Author(s):  
Emmanouil Daskalakis ◽  
Christos G. Panagiotopoulos ◽  
Chrysoula Tsogka ◽  
Nikolaos S. Melis ◽  
Ioannis Kalogeras
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Natural Frequencies ◽  
Time Window ◽  
Structural Vibration ◽  
Actual State ◽  
Time Interval ◽  
Actual Measurement ◽  
Damage Scenarios ◽  
Structural Health

We present in this paper a structural health monitoring study of the Egyptian lighthouse of Rethymnon in Crete, Greece. Using structural vibration data collected on a limited number of sensors during a 3-month period, we illustrate the potential of the stretching method for monitoring variations in the natural frequencies of the structure. The stretching method compares two signals, the current that refers to the actual state of the structure, with the reference one that characterizes the structure at a reference healthy condition. For the structure under study, an 8-day time interval is used for the reference quantity while the current quantity is computed using a time window of 24 h. Our results indicate that frequency shifts of 1% can be detected with high accuracy allowing for early damage assessment. We also provide a simple numerical model that is calibrated to match the natural frequencies estimated using the stretching method. The model is used to produce possible damage scenarios that correspond to 1% shift in the first natural frequencies. Although simple in nature, this model seems to deliver a realistic response of the structure. This is shown by comparing the response at the top of the structure to the actual measurement during a small earthquake. This is a preliminary study indicating the potential of the stretching method for structural health monitoring of historical monuments. The results are very promising. Further analysis is necessary requiring the deployment of the instrumentation (possibly with additional instruments) for a longer period of time.

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