Prediction of the tensile strength of a tropical wood species Terminalia superba assembled by gluing: a comparative intelligent study

ABSTRACT

The present study deals with the prediction of the strength of the glue joint stressed in tension on a local wood species called Terminalia Superba (Fraké) assembled according to the bevel configuration using two artificial intelligence models namely ANFIS and LSTM. The experimental data obtained during tensile tests on a tropical species allowed us to determine the mechanical properties taken as structural parameters for the LSTM and ANFIS models. The results of the analysis show that among all the LSTM methods, LSTM ‘ADAM’ offers a low root mean square error (RMSE), a high accuracy (Acc) (RMSE = 2.16, Acc = 0.756). For all methods, ANFIS obtained the best results, a high R-squared and a very low root mean square error (RMSE) (R-squared = 0.979, RMSE = 0.51). This indicates that the prediction of the tensile strength of the adhesive joint is more satisfactory with ANFIS than with LSTM.

KEYWORDS:

• Terminalia Superba
• tensile strength
• glued
• bevel topology
• Neuro Fuzzy and deep learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors contributions

The final manuscript has been read by and approved by all authors.

Data availability statement

This article includes all of the data produced or utilised for this study.

Additional information

Funding

There was no specific grant from the funding source for this research.