Seismic damage identification of cable-stayed bridge in near-real-time using unsupervised deep neural network

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.Prompt damage identification of infrastructure systems is essential for effective post-disaster responses. However, most infrastructure systems have a high level of structural complexity, making damage identification extremely difficult. To overcome the challenge, the authors recently proposed a deep neural network (DNN) based framework for identifying the seismic damage based on the structural response data recorded during an earthquake event (Kim and Song, 2022). The DNN of the proposed framework is constructed by a Variational Autoencoder, one of the self-supervised DNNs capable of constructing a continuous latent space of input data by learning probabilistic characteristics. The DNN model is trained using the covariance matrices of the snapshot of the response data obtained from the undamaged structure. To consider the load-de-pendency, the undamaged state of the structure is represented by the covariance matrix, which is closest to that obtained from the measured seismic response in the latent space. To identify the severity of the structural damage, a structural damage index based on the difference in the covariance matrices is introduced. This paper improves the DNN-based framework to facilitate its applications to complex structural systems such as the Incheon Grand Bridge, a reinforced concrete cable-stayed bridge in South Korea. To generate train, validation, and test datasets, structural analyses are first performed under the ground motions from the PEER-NGA strong motion data-base. The proposed framework is verified with near-real-time simulations using ground motions with various time steps from the test dataset. The example shows that the proposed framework can accurately identify seismic damage of the complex structural system in near-real-time

Infectious disease control and its economic gains in a pandemic: the case of South Korea

We investigate the role of the infectious disease control (IDC) system in curbing the spread of infectious disease and preventing economic damage during the COVID-19 pandemic. To this end, we propose incorporating a clustering analysis into the synthetic control method. This contributes to constructing a homogeneous donor pool, which is necessary for an unbiased treatment effect estimator. South Korea’s effective IDC system, the so-called K-Quarantine, is estimated to have reduced the number of disease infections and to have prevented a 3.6% loss of GDP and a 0.3%p rise in the unemployment rate in South Korea in 2020. These results are robust in an alternative reduced-form regression analysis under various specifications

Adaptive active subspace-based metamodeling for high-dimensional reliability analysis

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is leveraged to identify low-dimensional salient features of a high-dimensional computational model. A surrogate computational model is built in the low-dimensional feature space by a heteroscedastic Gaussian process. Active learning adaptively guides the surrogate model training toward the critical region that significantly contributes to the failure probability. A critical trait of the proposed method is that the three main ingredients-active subspace, heteroscedastic Gaussian process, and active learning-are coupled to adaptively optimize the feature space mapping in conjunction with the surrogate modeling. This coupling empowers the proposed method to accurately solve nontrivial high-dimensional reliability problems via low-dimensional surrogate modeling. Finally, numerical examples of a high-dimensional nonlinear function and structural engineering applications are investigated to verify the performance of the proposed method

Tracing the Inheritance Line of Traditional Martial Arts Subak

At the end of the Joseon Dynasty, Subak's successors all died of old age. It is difficult to inherit the national cultural heritage and hand it down to descendants through individual efforts alone. We all need to take care of it and preserve it. As with arts that have been handed down by the people, there are no written records related to the genealogy of Subak and Subak Dance. However, it is possible to look into the context after the late Joseon Dynasty. Song Chang-yeol wanted to become a movie star in his youth, and it is said that he stayed for a while in a place called ‘Shin-Film’, which was directed by Shin Sang-ok. It seems that he is a former movie star, as he has appeared in several films, including a minor role in the movie “The Martyr” directed by Yoo Hyeon-mok. This person's maternal uncle is director Choi Hyun-min, who planned a movie called 'Sorrow in the Sky', which turned half of the Korean peninsula into a sea of tears in 1965. Song Chang-ryeol was born in February 1932 in Bukcheong-gun, Hamgyeongnam-do. In 1938, his father ran the 'Ohara Sewing Association' in Osaka, Japan. From 1938 to 40 he lived in Osaka. He later moved from Japan to Kaesong, Joseon

Residual Bias Phenomenon in Air‐Coupled Ultrasonic Capacitive Film Transducers

We discuss in this paper the underlying physics of a residual bias phenomenon, whereby the metalized Mylar films of air‐coupled film transducers accept and retain a residual electrostatic charge. Experimental measurements to demonstrate and quantify this effect are reported here, along with a hypothesis of the mechanism of charge transfer and embedding. The measurements show the amplitude performance of the capacitive film transducers as a function of applied bias voltage and frequency. Factors such as humidity and decay time also play roles in the acquisition and holding of charge on a film. We hypothesize that charge transfers from the conductive backplate and collects on the non‐metalized side of the film. The charged films therefore are electrostatically attracted to the transducer backplate even with no applied voltage bias. Typically, an externally applied bias voltage is needed to charge the capacitor. With a persistent residual bias effect, these air‐coupled capacitive film transducers could be used like conventional piezoelectric transducers with no biasing required. This effect has substantial implications for the operation of air‐coupled film transducers

Spherically focused capacitive-film, air-coupled ultrasonic transducer

A spherically focused no mirrors capacitive-film, air-coupled ultrasonic transducer, constructed using a spherically deformed backplate and metalized polymer film, has been designed, fabricated, and its performance characterized. A 1 cm diameter device has a center frequency of 805 kHz and a 6 dB bandwidth of 760 kHz. Comparisons of field strength in the focal zone with theoretical calculations for a spherically focused piston show that the device achieves diffraction-limited focusing. The nominal focal point of 25 mm lies within 0.01 mm of the calculated value for this device

MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection

Detecting anomalies in real-world multivariate time series data is challenging due to complex temporal dependencies and inter-variable correlations. Recently, reconstruction-based deep models have been widely used to solve the problem. However, these methods still suffer from an over-generalization issue and fail to deliver consistently high performance. To address this issue, we propose the MEMTO, a memory-guided Transformer using a reconstruction-based approach. It is designed to incorporate a novel memory module that can learn the degree to which each memory item should be updated in response to the input data. To stabilize the training procedure, we use a two-phase training paradigm which involves using K-means clustering for initializing memory items. Additionally, we introduce a bi-dimensional deviation-based detection criterion that calculates anomaly scores considering both input space and latent space. We evaluate our proposed method on five real-world datasets from diverse domains, and it achieves an average anomaly detection F1-score of 95.74%, significantly outperforming the previous state-of-the-art methods. We also conduct extensive experiments to empirically validate the effectiveness of our proposed model's key components