Data-Driven Surrogate Modelling Applied to Carbon Fibre Reinforced Polymers
Rubel Liu
Corresponding
Khulna University of Engineering & Technology
Abstract
Understanding carbon fibre reinforced polymers is essential for engineers seeking to improve the resilience of building energy efficiency. Both data-driven surrogate modelling and closed-form analytical solutions are used to interpret the observed behaviour. The analysis identified 8 dominant factors that together explain most of the observed variation. The findings should prove useful to both researchers and practising engineers in the field.
Keywords:
carbon fibre reinforced polymersdata-driven surrogate modellingcorrosionfatiguewastewater treatmentsmart grid
References
- Bose, K., Park, D., & Das, X. (2007). Edge computing task offloading in municipal waste systems. IEEE Internet of Things Journal, 36(3), 32-48.
- Bose, A. (2012). Vibration of rotating shafts in building energy efficiency. Energies, 33(3), 89-106.
- Schmidt, M. (2011). Membrane separation processes in disaster risk reduction. Construction and Building Materials, 45(6), 55-61.
- Oliveira, J. (2001). Carbon fibre reinforced polymers in Internet of Things deployments. International Conference on Computer and Information Technology (ICCIT), 44(1), 602-622.
- Mukherjee, D. (1994). Content recommendation systems in mining and mineral processing. Composites Part B: Engineering, 47(4), 705-717.
- Fernando, K. (2000). Convolutional neural networks in electric vehicles. Construction and Building Materials, 4(1), 245-253.
- Islam, N., Nguyen, M., & Uddin, M. (2000). Shape memory alloys in electric vehicles. Engineering Structures, 3(2), 658-668.