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303 results on '"Jongpradist, Pornkasem"'

7. Ground Movements in Shield Tunnelling for MRT Orange Line in Bangkok

Author

Duangsano, Ochok, Kandavorawong, Kangwan, Asanprakit, Auttakit, Jongpradist, Pornkasem, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Duc Long, Phung, editor, and Dung, Nguyen Tien, editor

Published
2024
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25. Sustainable use of laterite replaced with bottom ash as road construction materials.

Author

Rochanavibhata, Uthairith, Lhajai, Sakda, Jongpradist, Pornkasem, Makaratat, Nattapong, Jing, Guoqing, and Jamsawang, Pitthaya

Abstract

Bottom ash (BTA), a byproduct of burning coal in electric power plants, is often considered waste. Managing significant quantities of bottom ash remains a challenge. Laterite, commonly used in road construction, may not meet the required standards in some regions, necessitating the transport of higher-quality laterite from distant locations. This practice increases construction costs. This research explores the use of bottom ash and cement as replacements for laterite in pavement materials. The proportion of bottom ash used varied from 10 to 50% by dry weight of the laterite, while the cement contents were 1, 3, 5, and 7% by dry weight of the laterite-bottom ash mixture. The experiments included unconfined compressive strength, splitting tensile strength, California bearing ratio, and durability against wetting-drying cycles. The results indicate that the stabilised laterite significantly increases strength values—2 to 14 times greater than those of unstabilised laterite, with 20% bottom ash replacement yielding the best results. Microstructural analyses confirmed the strength test outcomes. Replacing laterite with bottom ash and cement proves to be a sustainable method for road construction, offering cost-effectiveness, conservation of natural resources, pollution reduction, and enhanced energy efficiency in accordance with the standards of the Department of Highways of Thailand. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Investigation on the effects of wall slenderness on failure behavior of DCM wall using a simplified small-scale physical model test

Author

Waichita, Siriwan, Jongpradist, Pornkasem, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Duc Long, Phung, editor, and Dung, Nguyen Tien, editor

Published
2020
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43. Application of artificial neural networks for prediction of crushing performance of a Quadricycle's S-rail.

Author

Homsnit, Thonn, Jongpradist, Pattaramon, Kongwat, Suphanut, and Jongpradist, Pornkasem

Subjects
ARTIFICIAL neural networks, RESPONSE surfaces (Statistics), FINITE element method, SURROGATE-based optimization, FORCE & energy
Abstract

Heavy electric quadricycles (L7e) typically have the problem of crash incompatibility because their size and weight are smaller and lighter than other vehicles. The S-rails, located in front of the automotive underbody, are the main components for absorbing the impact energy of the L7e. The design of the L7e's S-rail for crashworthiness is challenging because the design space is relatively limited. High computational resources are commonly required for crash simulation due to a vast amount of data from nonlinear explicit dynamic analyses. Machine learning techniques can more quicky provide and extract accurate crash predictions from a combination of feasible solutions. This work employs a finite element analysis via LS-DYNA to obtain discrete results of the peak crushing force and the specific energy absorption for S-rail impact. The artificial neural network (ANN) module in MATLAB and response surface methodology (RSM) were then applied to predict the crushing-performance surrogate models based on the S-rail's thickness and geometry. To conduct an accurate learning process of the ANN, 81 data points for the crashworthiness simulation were adopted as training inputs with ten test data points. Furthermore, the number of neurons and activation function are examined to acquire the most accurate results for predicting the dynamic crushing of an S-rail. The investigation shows that ANN is more accurate than RSM in predicting the S-rail's peak crash force and specific energy absorption, especially when applying ReLU activation functions. With its high accuracy and capability to predict multiple targets, the ANN-ReLU is recommended to perform surrogate-based model optimization. [ABSTRACT FROM AUTHOR]

Published
2024
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