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T.H. Lee, Y. Lee, J.-W. Hong, "Random Forest-Based Surrogate Modeling of Blast Parameters from Cuboid Charges", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 9.2, 2025,

Keywords: blast simulation, arbitrary Lagrangian-Eulerian method, machine learning, random forest, reinforced concrete, surrogate modeling.

Abstract

Assessing the blast resistance of reinforced concrete slabs is crucial for protecting infrastructure against increasing risks from explosions and industrial hazards. The complex behavior of slabs under blast loading, particularly when subjected to cuboid-shaped charges, poses significant challenges for conventional experimental and numerical methods. We develop a surrogate model that conducts high-fidelity arbitrary Lagrangian-Eulerian (ALE) simulation results with machine learning techniques to predict key blast parameters such as the arrival time, peak time, peak pressure, decay duration, and impulse across a wide range of charge geometries. A total of 363 simulations are conducted by systematically varying the length, width, and height of cuboid-shaped charges to generate the dataset. Using the random forest algorithm, the surrogate model generates pressure-time history curves, which are verified against numerical simulation results. This study provides an efficient approach for evaluating blast loading on critical structures and contributes to the enhancement of safety design.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 10 PROCEEDINGS OF THE EIGHTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 9.2 Random Forest-Based Surrogate Modeling of Blast Parameters from Cuboid Charges T.H. Lee, Y. Lee and J.-W. Hong Department of Civil and Environmental Engineering, KAIST, Daejeon, Republic of Korea Full Bibliographic Reference for this paper T.H. Lee, Y. Lee, J.-W. Hong, "Random Forest-Based Surrogate Modeling of Blast Parameters from Cuboid Charges", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 9.2, 2025, Keywords: blast simulation, arbitrary Lagrangian-Eulerian method, machine learning, random forest, reinforced concrete, surrogate modeling. Abstract Assessing the blast resistance of reinforced concrete slabs is crucial for protecting infrastructure against increasing risks from explosions and industrial hazards. The complex behavior of slabs under blast loading, particularly when subjected to cuboid-shaped charges, poses significant challenges for conventional experimental and numerical methods. We develop a surrogate model that conducts high-fidelity arbitrary Lagrangian-Eulerian (ALE) simulation results with machine learning techniques to predict key blast parameters such as the arrival time, peak time, peak pressure, decay duration, and impulse across a wide range of charge geometries. A total of 363 simulations are conducted by systematically varying the length, width, and height of cuboid-shaped charges to generate the dataset. Using the random forest algorithm, the surrogate model generates pressure-time history curves, which are verified against numerical simulation results. This study provides an efficient approach for evaluating blast loading on critical structures and contributes to the enhancement of safety design. download the full-text of this paper (PDF, 12 pages, 533 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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