Unconfined compressive strength prediction of stabilized expansive clay soil using machine learning techniques

Ahmad, Mahmood; Al-Mansob, Ramez A.; Ramli, Ahmad Bukhari Bin; Ahmad, Feezan; Khan, Beenish Jehan

doi:10.1007/s41939-023-00203-7

Cited by 7 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Microsoft excel 2019(Zhu et al 2022;Khatti and Grover 2024). Other than this Different statistical parameters like, root mean squared error (RMSE) (Al-Haddad and Mahdi 2024), mean squared error (MSE)(Ahmad et al 2024), mean absolute error (MAE)(Zhao et al 2024), mean absolute percentage error (MAPE)(Ding et al 2023), and mean bias error (MBE)(Huang 2022)were calculated for comparing the different prediction capabilities at various CHTCs. The formulas for all the statistical parameters are as follows,…”

mentioning

confidence: 99%

Framework for Accurate Estimation of Freezing Time and Convective Heat Transfer Coefficient for Freezing of Food product in Domestic Refrigerator: A Numerical and Simulation Modeling approach

Ray,

Minz,

Sinha

2024

Preprint

View full text Add to dashboard Cite

Freezing time estimation is essential for quality assurance, process optimization, and innovation in a variety of industries. The total freezing time is greatly influenced by the heat transfer parameters like convective heat transfer coefficient (CHTC), which in turn affects the product's quality characteristics. The correct calculation of CHTC, especially in domestic freezers, remains largely unexplored despite the development of several mathematical models for freezing time prediction. To fill this gap, this research presents a framework-driven analysis of CHTC for estimating the freezing time of frozen sweetened yoghurt in a domestic refrigerator. This research not only compares the performance of several freezing time prediction models, but also seeks to determine the optimal CHTC range for accurate freezing time forecasts, determining the most accurate freezing time prediction model and to replicate the freezing process by simulation accurately for this specific scenario. The results showed great accuracy for the determined CHTC range (RMSE = 27.24, CV = 0.16, MSE = 741.85, MAE = 22.4, MAPE = 12.24 and MBE = 8.14) and lowest average residual (3.95 min) for the fitted prediction for the freezing time calculation of frozen yoghurt. The simulation analysis further backed the results by showcasing marginal temperature difference between the simulated and actual temperature (0.232℃ to 0.684℃) of the frozen yoghurt by using the determined CHTC range. This showed the developed framework’s reliability in freezing time prediction and CHTC range determination, which will eventually help in modeling the freezing process of different food products with high accuracy.

show abstract