Modal tracking of seismically-excited buildings using stochastic system identification

Chia-Ming Chang,Jau-Yu Chou 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.4

Investigation of structural integrity has been a critical issue in the field of civil engineering for years. Visual inspection is one of the most available methods to explore deteriorative components in structures. Still, this method is not applicable to invisible damage of structures. Alternatively, system identification methods are capable of tracking modal properties of structures over time. The deviation of these dynamic properties can serve as indicators to access structural integrity. In this study, a modal tracking technique using frequency-domain system identification from seismic responses of structures is proposed. The method first segments the measured signals into overlapped sequential portions and then establishes multiple Hankel matrices. Each Hankel matrix is then converted to the frequency domain, and a temporal-average frequency-domain Hankel matrix can be calculated. This study also proposes the frequency band selection that can divide the frequency-domain Hankel matrix into several portions in accordance with referenced natural frequencies. Once these referenced natural frequencies are unavailable, the first few right singular vectors by the singular value decomposition can offer these references. Finally, the frequency-domain stochastic subspace identification tracks the natural frequencies and mode shapes of structures through quick stabilization diagrams. To evaluate performance of the proposed method, a numerical study is carried out. Moreover, the long-term monitoring strong motion records at a specific site are exploited to assess the tracking performance. As seen in results, the proposed method is capable of tracking modal properties through seismic responses of structures.

SHM data anomaly classification using machine learning strategies: A comparative study

Shieh-Kung Huang,Jau-Yu Chou,Yuguang Fu,Chia-Ming Chang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

Deformation estimation of truss bridges using two-stage optimization from cameras

Chia-Ming Chang,Jau-Yu Chou 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.4

Structural integrity can be accessed from dynamic deformations of structures. Moreover, dynamic deformations can be acquired from non-contact sensors such as video cameras. Kanade-Lucas-Tomasi (KLT) algorithm is one of the commonly used methods for motion tracking. However, averaging throughout the extracted features would induce bias in the measurement. In addition, pixel-wise measurements can be converted to physical units through camera intrinsic. Still, the depth information is unreachable without prior knowledge of the space information. The assigned homogeneous coordinates would then mismatch manually selected feature points, resulting in measurement errors during coordinate transformation. In this study, a two-stage optimization method for video-based measurements is proposed. The manually selected feature points are first optimized by minimizing the errors compared with the homogeneous coordinate. Then, the optimized points are utilized for the KLT algorithm to extract displacements through inverse projection. Two additional criteria are employed to eliminate outliers from KLT, resulting in more reliable displacement responses. The second-stage optimization subsequently fine-tunes the geometry of the selected coordinates. The optimization process also considers the number of interpolation points at different depths of an image to reduce the effect of out-of-plane motions. As a result, the proposed method is numerically investigated by using a truss bridge as a physics-based graphic model (PBGM) to extract high-accuracy displacements from recorded videos under various capturing angles and structural conditions.

A hybrid deep neural network compression approach enabling edge intelligence for data anomaly detection in smart structural health monitoring systems

Yuguang Fu,Tarutal Ghosh Mondal,Jau-Yu Chou,Jian-Xiao Mao 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.3

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

Outcomes of limited period of adalimumab treatment in moderate to severe Crohn’s disease patients: Taiwan Society of Inflammatory Bowel Disease Study

( Wei-chen Lin ),( Jen-wei Chou ),( Hsu-heng Yen ),( Wen-hung Hsu ),( Hung-hsin Lin ),( Jen-kou Lin ),( Chiao-hsiung Chuang ),( Tien-yu Huang ),( Horng-yuan Wang ),( Shu-chen Wei ),( Jau-min Wong ) 대한장연구학회 2017 Intestinal Research Vol.15 No.4

Background/Aims: In Taiwan, due to budget limitations, the National Health Insurance only allows for a limited period of biologics use in treating moderate to severe Crohn’s disease (CD). We aimed to access the outcomes of CD patients following a limited period use of biologics, specifically focusing on the relapse rate and remission duration; also the response rate to second use when applicable. Methods: This was a multicenter, retrospective, observational study and we enrolled CD patients who had been treated with adalimumab (ADA) according to the insurance guidelines from 2009 to 2015. Results: A total of 54 CD patients, with follow-up of more than 6 months after the withdrawal of ADA, were enrolled. The average period of treatment with ADA was 16.7±9.7 months. After discontinuing ADA, 59.3% patients suffered a clinical relapse. In the univariate analysis, the reason for withdrawal was a risk factor for relapse (P=0.042). In the multivariate analysis, current smoker became an important risk factor for relapse (OR, 3.9; 95% CI, 1.2-14.8; P=0.044) and male sex was another risk factor (OR, 2.9; 95% CI, 1.1-8.6; P=0.049). For those 48 patients who received a second round of biologics, the clinical response was seen in 60.4%, and 1 anaphylaxis occurred. Conclusions: Fifty-nine percent of patients experienced a relapse after discontinuing the limited period of ADA treatment, and most of them occurred within 1 year following cessation. Male sex and current smoker were risk factors for relapse. Though 60.4% of the relapse patients responded to ADA again. (Intest Res 2017;15:487-494)