• Proceedings of the Korean Geotechical Society Conference
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• 1997.10a
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• pp.141-150
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• 1997

• Smart Structures and Systems
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• v.33 no.4
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• pp.301-311
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• 2024

This paper proposes a deep convolutional long short-term memory (ConvLSTM)-based crack growth prediction technique for predictive maintenance of structures. Since cracks are one of the critical damage types in a structure, their regular inspection has been mandatory for structural safety and serviceability. To effectively establish the structural maintenance plan using the inspection results, crack propagation or growth prediction is essential. However, conventional crack prediction techniques based on mathematical models are not typically suitable for tracking complex nonlinear crack propagation mechanism on civil structures under harsh environmental conditions. To address the technical issue, a field data-driven crack growth prediction technique using ConvLSTM is newly proposed in this study. The proposed technique consists of the four steps: (1) time-series crack image acquisition, (2) target image stabilization, (3) deep learning-based crack detection and quantification and (4) crack growth prediction. The performance of the proposed technique is experimentally validated using a concrete mock-up specimen by applying step-wise bending loads to generate crack growth. The validation test results reveal the prediction accuracy of 94% on average compared with the ground truth obtained by field measurement.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.275-276
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• 2004

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2007.04a
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• pp.338-342
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• 2007

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.598-599
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.442-443
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• 2005

• Food Science and Preservation
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• v.30 no.3
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• pp.546-546
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• 2023

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.3
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• pp.363-369
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• 2012

The moderator thermal flow in the CANDU calandria is generally complex and highly turbulent because of the interaction of the buoyancy force with the inlet jet inertia. In this study, the prediction performance of turbulence models for the accurate analysis of the moderator thermal flow are assessed by comparing the results calculated with various types of turbulence models in the commercial flow solver FLUENT with experimental data for the test vessel at Sheridan Park Engineering Laboratory (SPEL).Through this comparative study of turbulence models, it is concluded that turbulence models that include the source term to consider the effects of buoyancy on the turbulent flow should be used for the reliable prediction of the moderator thermal flow inside the CANDU calandria.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1562-1582
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• 2024

The prediction of pedestrian trajectory is conducive to reducing traffic accidents and protecting pedestrian safety, which is crucial to the task of intelligent driving. The existing methods mainly use the past pedestrian trajectory to predict the future deterministic pedestrian trajectory, ignoring pedestrian intention and trajectory diversity. This paper proposes a multi-modal trajectory prediction model that introduces pedestrian intention. Unlike previous work, our model makes multi-modal goal-conditioned trajectory pedestrian prediction based on the past pedestrian trajectory and pedestrian intention. At the same time, we propose a novel Gate Recurrent Unit (GRU) to process intention information dynamically. Compared with traditional GRU, our GRU adds an intention unit and an intention gate, in which the intention unit is used to dynamically process pedestrian intention, and the intention gate is used to control the intensity of intention information. The experimental results on two first-person traffic datasets (JAAD and PIE) show that our model is superior to the most advanced methods (Improved by 30.4% on MSE_0.5s and 9.8% on MSE_1.5s for the PIE dataset; Improved by 15.8% on MSE_0.5s and 13.5% on MSE_1.5s for the JAAD dataset). Our multi-modal trajectory prediction model combines pedestrian intention that varies at each prediction time step and can more comprehensively consider the diversity of pedestrian trajectories. Our method, validated through experiments, proves to be highly effective in pedestrian trajectory prediction tasks, contributing to improving traffic safety and the reliability of intelligent driving systems.

• Fire Science and Engineering
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• v.33 no.6
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• pp.9-19
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• 2019

In fire simulations for building fire safety evaluation, changes in the fire area and grid size can significantly influence the prediction results. Therefore, the effects of area changes of the fire source with identical maximum heat release rates on the prediction results of a compartment fire were investigated. The dependence of the prediction results on the grid size using the identical fire area was also examined. No significant changes were observed in the thermal and chemical characteristics of the fires with variable grid sizes, even though the fire area was changed when six or more grids were set based on the fire diameter. In addition, changes in the fire area caused significant differences in the prediction of major physical quantities associated with available safety egress time (ASET) within a compartment. However, the fire area changes did not considerably influence the overall fire characteristics outside the compartment after reaching a certain distance from the opening.