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- 저자 : Rattapoohm Parichatprecha (검색결과 2 건)
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Rattapoohm Parichatprecha,Pichai Nimityongskul 사단법인 한국계산역학회 2009 Computers and Concrete, An International Journal Vol.6 No.3
This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.
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Application of Multibody Dynamics Simulation in Approximation of Rail Vehicle Dynamic Envelope
Songsak Suthasupradit,Thitiwut Petcharat,김기두,Rattapoohm Parichatprecha 한국철도학회 2023 한국철도학회논문집 Vol.26 No.12
The master plan for Thailand’s Eastern High-Speed Rail Project calls for the Airport Rail Link to be shared with high-speed trains. To ensure the safety of mixed operations, it is imperative to assess loading and structural gauges. This study determined the dynamic envelopes of rail vehicles using multibody dynamics modeling, focusing on three rolling stock models—Siemens Desiro UK Class 360/2 EMU, CRH2C EMU, and Shinkansen Series 300 High-Speed EMU— across a spectrum of operating conditions. Factors such as train mass, suspension characteristics, running speed, wheel wear, track geometry, and track irregularities were incorporated as dynamic simulation parameters. The greatest vehicle movement occurred along the westbound curve path, particularly at the transition between the 996-meter-radius curve to the 180-meter-radius curve. The Shinkansen Series 300 exhibited the greatest lateral and vertical maneuverability. Both the CRH2C EMU and Shinkansen 300 Series trains exceeded the structural envelope limitations of the Airport Rail Link.
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