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Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains
No full textStructural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system
Creating Tunable Mesoporosity by Temperature???Driven Localized Crystallite Agglomeration
No full textA new synthetic approach for tunable mesoporous metal???organic frameworks (MeMs) is developed. In this approach, mesopores are created in the process of heat conversion of highly mosaic metal???organic framework (MOF) crystals with non-interpenetrated low-density nanocrystallites into MOF crystals with two-fold interpenetrated high-density nanocrystallites. The two-fold interpenetration reduces the volume of the nanocrystallites in the mosaic crystal, and the accompanying localized agglomeration of the nanocrystallites results in the formation of mesopores among the localized crystallite agglomerates. The pore size can be easily modulated from 7 to 90 nm by controlling the heat treatment conditions, that is, the aging temperature and aging time. Various proteins can be encapsulated in the MeM, and immobilized enzymes show catalyst activity comparable to that of the free native enzymes. Immobilized ??-galactosidase is recyclable and the enzyme activity of the immobilized catalase is maintained after exposure to high temperatures and various organic solvents
Capturing research trends in structural health monitoring using bibliometric analysis
No full textAs civil infrastructure has continued to age worldwide, its structural integrity has been threatened owing to material deteriorations and continual loadings from the external environment. Structural Health Monitoring (SHM) has emerged as a cost-efficient method for ensuring structural safety and durability. As SHM research has gradually addressed an increasing number of structure-related problems, it has become difficult to understand the changing research topic trends. Although previous review papers have analyzed research trends on specific SHM topics, these studies have faced challenges in providing (1) consistent insights regarding macroscopic SHM research trends, (2) empirical evidence for research topic changes in overall SHM fields, and (3) methodological validations for the insights. To overcome these challenges, this study proposes a framework tailored to capturing the trends of research topics in SHM through a bibliometric and network analysis. The framework is applied to track SHM research topics over 15 years by identifying both quantitative and relational changes in the author keywords provided from representative SHM journals. The results of this study confirm that overall SHM research has become diversified and multi-disciplinary. Especially, the rapidly growing research topics are tightly related to applying machine learning and computer vision techniques to solve SHM-related issues. In addition, the research topic network indicates that damage detection and vibration control have been both steadily and actively studied in SHM research
Anisotropic silver nanowire dielectric composites for self-healable triboelectric sensors with multi-directional tactile sensitivity
No full textSelf-healable wearable devices with tactile directional sensitivity have attracted significant attention due to their damage-free sustainable and precise detection of directional body motions in various applications such as hand gesture monitoring and electrooculography (EOG) sensors. In this study, a self-powered triboelectric sensor with tactile directional sensitivity and self-healing property based on the dielectric multilayers of aligned silver nanowire (AgNW) composite gel sandwiched between self-healable polymers has been developed. Its output performances are superior to those of similar devices without the AgNW gel due to the stronger interfacial polarization of the dielectric multilayers and charge trapping by AgNWs. Moreover, the aligned AgNW composite gel possesses anisotropic dielectric properties, which enhances the contact direction-sensitive triboelectric performance of the sensor during rolling or rubbing motions. The fabricated sensor can be used in wearable EOG and electrocardiography applications because of the ability to monitor extremely weak blood pulse waves in the temporal artery around the human eye, while its directional force sensitivity allows differentiating eight eye movement directions. The successful use of the multilayered structure containing the self-healable and aligned AgNW composite gel as a triboelectric dielectric material opens a new avenue for designing self-healable wearable sensors with tactile directional sensitivity