Parichatprecha, Rattapoohm
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Parichatprecha, Rattapoohm
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rattapoohm.pa@kmitl.ac.th
5 results
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Item type:Publication, Structural Behavior of LC-GFRP Confined Waste Aggregate Concrete Square Columns with Sharp and Round Corners(2022-09-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Collapse prevention of pre-stressed electric transmission poles using glass fiber reinforced polymers(2025-07-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detection of Isolated Track Defects Using Axle Box Acceleration from Multibody Simulation and Recurrent Neural Network(2026-01-23); Railway track inspection is typically conducted cyclically, which is slow, budget-intensive, and time-consuming, as it can only be performed during non-service periods when trains are not in operation. Currently, there are emerging concepts and research studies utilizing railway defect detection through acceleration measurements from axle box mounted on service trains, as this approach offers easier implementation, reduced costs, and time efficiency compared to conventional methods. However, the diagnostic process remains challenging due to the high complexity and large volume of data involved. This research presents a method for applying Recurrent Neural Network (RNN) that works with time-series data in diagnosing railway defects by classifying three specific types of isolated defects: squats and corrugation. The training samples for the neural network were derived from axle box acceleration data obtained through multibody simulation analysis that modeled various railway conditions, speeds, and defect characteristics. A total of 360 samples from the multibody model were used for training, validation, and testing. The study results showed that the Recurrent Neural Network (RNN) achieved an average defects classification accuracy of 91.67%. Furthermore, the study findings revealed that the developed RNN model can accurately predict the location and classify defects, with particularly high accuracy in predicting the location and defects caused by long-pitch corrugation. The study concluded that the developed RNN model can efficiently learn and classify defects from axle box acceleration data obtained from multibody simulation. This approach can be applied as a guideline for railway damage diagnosis in future maintenance applications. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Low-Cost Glass Fiber-Reinforced Polymer Composite Wraps for Strengthening Deep Beams with and without Longitudinal Openings(2025-08-01); This is a preliminary work on the application of low-cost glass fiber-reinforced polymer composites (Lo-Gs) wraps to enhance the structural response of shear-critical deep beams with and without openings. This study explores the performance of nine deep beams divided into three groups depending on the existence and number of longitudinal openings: solid section beams (Group 1), beams with one opening (Group 2), and beams with two openings (Group 3). Each group consisted of one unstrengthened beam and two beams strengthened with either one or two layers of Lo-Gs wraps. The results showed that Lo-Gs confinement effectively delayed failure in strengthened beams, while having minimal impact on the sudden failure behavior of unstrengthened specimens. Solid section beams exhibited peak load increases of 12.1% and 20.2% with one and two wraps, respectively. In contrast, beams with openings demonstrated higher but more variable strength gains. The presence of longitudinal openings diminished the effectiveness of the wraps in improving ultimate deflection and energy dissipation. While solid beams achieved up to a 130.1% increase in energy dissipation, beams with one and two openings showed lower gains of 63.4% and 57.0%, respectively. Existing design models, calibrated for synthetic FRPs, poorly predicted the behavior of beams with Lo-Gs wraps and neglected the effects of openings, emphasizing the need for further research and model development to address these limitations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of Multibody Dynamics Simulation in Approximation of Rail Vehicle Dynamic Envelope(2023-01-01);