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 A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Maribel Anaya ◽  
Diego A. Tibaduiza ◽  
Francesc Pozo
Keyword(s):  
Immune System ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Artificial Immune System ◽  
Structural Changes ◽  
Sensor Data ◽  
Artificial Immune ◽  
Structural Health ◽  
Industrial Sectors

Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.

scholarly journals Structural Health Monitoring using Artificial Immune System

2020 ◽  
Vol 6 (4) ◽  
pp. 16948-16963
Author(s):  
Daniela Cabral Oliveira ◽  
Fábio Roberto Chavarette ◽  
Fernando Parra dos Anjos Lima
Keyword(s):  
Immune System ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
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.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION IN STRUCTURAL HEALTH MONITORING: SOME EXPERIENCE

2009 ◽  
Vol 01 (04) ◽  
pp. 601-621 ◽  
Author(s):  
JUN CHEN
Keyword(s):  
Structural Health Monitoring ◽  
Data Processing ◽  
Parameter Identification ◽  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Structural Parameter ◽  
Raw Data ◽  
Structural Health ◽  
Long Span ◽  
Mode Decomposition

The installation of long-term structural health monitoring (SHM) system on super-tall buildings, long span bridges and large space structures has become a worldwide trend since last decade to monitor loading conditions, to detect damage, to assess structural safety and to guide maintenance during their service life. The core part of an SHM system is the function of data processing and structural parameter/damage identification that extracts useful information from huge amount of raw data and provides reliable knowledge for proper decision. Recently emerged data processing technique empirical mode decomposition (EMD) in conjunction with Hilbert transform (HT) provides a more better and powerful tool for SHM. This paper summarizes some research experience gained from application of EMD + HT in SHM with focuses on pre-processing raw data, structural parameter identification and damage detection. In particular, EMD is applied to determining time varying mean wind speed for wind data and to extract multipath effect from GPS data. For structural parameter identification, the EMD + HT approach is employed to identify natural frequencies and modal damping ratios of long span bridge during passage of strong typhoon and of structures with closely spaced modes of vibration. The results manifest the advantages of EMD + HT over traditional FFT-based methods in damping estimation. Furthermore, experimental investigation has been carried out to study the applicability of EMD for identifying structural damage caused by a sudden change of structural stiffness. It is concluded from all these investigations that EMD approach is a promising tool for structural health monitoring of large civil structures. Finally, some issues concerned for further practical application of EMD are highlighted and discussed based on these academic researches.

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.

A Theoretical Study on Knowledge Discovery Technique for Structural Health Monitoring

2012 ◽  
Vol 166-169 ◽  
pp. 1250-1253
Author(s):  
Jian Li ◽  
Jun Deng
Keyword(s):  
Structural Health Monitoring ◽  
Knowledge Discovery ◽  
Health Monitoring ◽  
Theoretical Study ◽  
Structural Parameter ◽  
Statistical Control ◽  
Data Bases ◽  
Useful Knowledge ◽  
Structural Health ◽  
Further Development

Based on the similarity between knowledge discovery from data bases (KDD) and Structural health monitoring (SHM), and considered the particularity of SHM problems, a four-step framework of SHM is proposed. The framework extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is consisted by second order structural parameter identification as feature extraction and statistical control chart analysis of identified stiffness for feature analysis is then presented. Through clarifying the goal and hierarchy of extracting useful knowledge of SHM problems, the framework has potential to facilitate the further development of SHM.

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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