• Smart Structures and Systems
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• 제29권1호
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Structural Engineering and Mechanics
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• 제71권1호
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• Structural Engineering and Mechanics
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• 제81권3호
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• pp.293-303
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• 2022

As infrastructure ages and traffic load increases, serious public concerns have arisen for the well-being of bridges. The current health monitoring practice focuses on large-scale bridges rather than short span bridges. However, it is critical that more attention should be given to these behind-the-scene bridges. The relevant information about the construction methods and as-built properties are most likely missing. Additionally, since the condition of a bridge has unavoidably changed during service, due to weathering and deterioration, the material properties and boundary conditions would also have changed since its construction. Therefore, it is not appropriate to continue using the design values of the bridge parameters when undertaking any analysis to evaluate bridge performance. It is imperative to update the model, using finite element (FE) analysis to reflect the current structural condition. In this study, a FE model is established to simulate a concrete culvert bridge in New South Wales, Australia. That model, however, contains a number of parameter uncertainties that would compromise the accuracy of analytical results. The model is therefore updated with a neural network (NN) optimisation algorithm incorporating Monte Carlo (MC) simulation to minimise the uncertainties in parameters. The modal frequency and strain responses produced by the updated FE model are compared with the frequency and strain values on-site measured by sensors. The outcome indicates that the NN model updating incorporating MC simulation is a feasible and robust optimisation method for updating numerical models so as to minimise the difference between numerical models and their real-world counterparts.

• 대한토목학회논문집
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• 제28권5A호
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• pp.631-639
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• 2008

본 논문은 서해대교 사장교의 보강형에 대한 고해상도 다점 모드형상을 TDD기법을 이용하여 추출한 사례를 소개한다. 상시진동에 대한 진동시험이 실시되었으며, 총 72개의 센서위치에서 수직방향 가속도 시간응답을 계측할 수 있는 계측 통신망이 구축되었다. 계측된 가속도 시간응답에 대하여 TDD기법을 적용하여 총 24차 수직방향 모드형상이 추출되었다. 추출된 모드형상은 현재 운용중인 상시모니터링 시스템에서는 계측되지 못한 많은 모드들을 포함하고 있다. 장대교량과 같이 대형구조물의 고해상도 모드형상 추출에는 다른 여러 가지 동특성 추출기법보다 TDD기법이 매우 효과적인 것을 확인 할 수 있었다.

• 한국지진공학회논문집
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• 제24권2호
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• pp.67-76
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• 2020

This study aims to optimize the cochlea-inspired artificial filter bank (CAFB) using El-Centro seismic waveforms and test its performance through a shaking table test on a two-span bridge model. In the process of optimizing the CAFB, El-Centro seismic waveforms were used for the purpose of evaluating how they would affect the optimizing process. Next, the optimized CAFB was embedded in the developed wireless-based intelligent data acquisition (IDAQ) system to enable response measurement in real-time. For its performance evaluation to obtain a seismic response in real-time using the optimized CAFB, a two-span bridge (model structures) was installed in a large shaking table, and a seismic response experiment was carried out on it with El-Centro seismic waveforms. The CAFB optimized in this experiment was able to obtain the seismic response in real-time by compressing it using the embedded wireless-based IDAQ system while the obtained compressed signals were compared with the original signal (un-compressed signal). The results of the experiment showed that the compressed signals were superior to the raw signal in response performance, as well as in data compression effect. They also proved that the CAFB was able to compress response signals effectively in real-time even under seismic conditions. Therefore, this paper established that the CAFB optimized by being embedded in the wireless-based IDAQ system was an economical and efficient data compression sensing technology for measuring and monitoring the seismic response in real-time from structures based on the wireless sensor networks (WSNs).

• Smart Structures and Systems
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• 제30권6호
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권12호
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• pp.357-362
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• 2023

교량은 중요한 교통 인프라지만 다양한 환경적 요인과 지속적인 교통 부하로 손상 및 균열을 겪게 되며, 이러한 요인들은 교량의 노후화를 가속화시킨다. 현재 건설한 지 오래된 교량이 많아지면서 안전성을 보장하고 노후화를 진단하기 위한 시스템의 필요성이 대두되고 있다. 이미 교량에서는 실시간 또는 주기적으로 교량의 상태를 모니터링하기 위해 구조물 건전도 모니터링(SHM) 기술이 활용되고 있다. 이 기술과 함께 인공지능과 사물인터넷 기술을 활용한 지능형 교량 모니터링 기술 개발이 진행 중이다. 본 논문에서는 노후화된 교량의 유지관리를 위해 고속 푸리에 변환과 차원 축소 알고리즘을 활용한 교량 안전성을 예측 엣지 시스템 기법을 연구한다. 특히, 기존 연구와는 다르게 실제 교량에서 수집된 센서 데이터를 이용하여 데이터셋을 형성하고 교량의 안전성을 확인할 수 있는지 알아본다.

• Structural Monitoring and Maintenance
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• 제5권2호
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

• 한국소음진동공학회논문집
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• 제25권11호
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• pp.787-797
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• 2015

In this paper, a cochlea-inspired artificial filter bank(CAFB) was developed in order to efficiently acquire dynamic response of structure, and it was also evaluated via dynamic response experiments. To sort out signals containing significant modal information from all the dynamic responses of structure, it was made to adopt a band-pass filter optimizing algorithm(BOA) and a peak-picking algorithm (PPA). Optimally designed on the basis of El-centro and Kobe earthquake signals, it was then embedded into the wireless multi-measurement system(WiMMS). In order to evaluate the performance of the developed CAFB, a vibration test was conducted using the El-centro and Kobe earthquake signals, and structural responses of a two-span bridge were obtained and analyzed simultaneously by both a wired measurement system and a CAFB-based WiMMS. The test results showed that the compressed dynamic responses acquired by the CAFB-based WiMMS matched with those of the wired system, and they included significant modal information of the two-span bridge. Therefore this study showed that the developed CAFB could be used as a new, economic, and efficient measurement device for wireless sensor networks(WSNs) based real time structural health monitoring because it could reconstruct the whole dynamic response using the compressed dynamic response with significant modal information.

• Smart Structures and Systems
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• 제29권1호
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.