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All Journal Indonesian Journal Of Civil Engineering Education Research of Scientia Naturalis
Aco Wahyudi Efendi
Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Education Physics

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ChatGPT application in ground settlement analysis using LISA V.8 FEA Aco Wahyudi Efendi
Research of Scientia Naturalis Vol. 1 No. 1 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i1.826

Abstract

OpenAI, an artificial intelligence research center situated in Ohio, USA, created ChatGPT. The model can be used to create autonomous discussions in conversational apps, assist with content generation, and even assist with multi-language translation with various degrees of accuracy for each language. ChatGPT is increasingly being used in all scientific domains and has a good influence, as evidenced by past research findings. This research will use ChatGPT in the field of geotechnical engineering by studying the settlement of soil layers with spongy clay type and validating it with modeling using LISA V.8 FEA finite element analysis (license). It is expected that this research will provide similar results to previous studies in engineering and other social fields. This research was conducted to be able to determine and provide validation of the behavior of the subsidence that occurred using ChatGPT and Finite Element Method Software LISA FEA V.8 from the results obtained were in model (a) there was a decrease in soil up to 0.0206 mm and in model (b) there was a decrease of 0.0167 with a ratio of 0.811 and with the ChatGPT model obtained a decrease of 0.0226 mm with a ratio of 1.097.
The 3D Potential Flow Simulation of an Electric Commuter Train's Velocity and Pressure with LISA FEA V.8 Aco Wahyudi Efendi
Indonesian Journal Of Civil Engineering Education Vol 10, No 2 (2024): Indonesian Journal of Civil Engineering Education
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijcee.v10i2.94454

Abstract

The current research examines the aerodynamic behaviour of an electric commuter rail vehicle moving at 100 km/h by comparing the findings of 3D simulation with manual estimations of dynamic pressure and velocity. Compared to manual approaches, the simulation yielded more accurate insights using the potential flow theory-based LISA FEA V.8 program. There was a 5% discrepancy between the simulation's range of 0.546 kN/m² to 4.865 kN/m² and the manually determined dynamic pressure of 4.633.3 kN/m². The simulation demonstrated the impact of intricate aerodynamic interactions, which produced a velocity of 31.66 m/s, 14% greater than the manual calculation at the train's front of 27.78 m/s.Furthermore, the simulation revealed an uneven velocity distribution contributing to drag, with the highest speeds along the sides and a low-pressure wake at the back. These findings highlight the need for sophisticated simulations to improve train design, lower drag, increase energy efficiency, and improve passenger comfort. They also show the limitations of simplified computations
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