Prediction of water absorption and swelling of thermally modified fir wood by artificial neural network models

Haftkhani, Akbar Rostampour; Abdoli, Farshid; Rashidijouybari, Iman; Garcia, R. Á.

doi:10.1007/s00107-022-01839-x

Cited by 4 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, back propagation (BP) neural networks have gained widespread use due to their ability to learn and generalize from input and output data [24,25]. Haftkhani et al [26] predicted water absorption and swelling of heat-treated fir wood by using the single-and multiple-input BP network model. The mean absolute percentage error (MAPE) for the prediction was less than 10%.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Wang,

et al. 2024

Forests

View full text Add to dashboard Cite

The physical and mechanical properties of heat-treated wood are essential factors in assessing its appropriateness for different applications. While back-propagation (BP) neural networks are widely used for predicting wood properties, their accuracy often falls short of expectations. This paper introduces an improved Beluga Whale Optimisation (IBWO)-BP model as a solution to this challenge. We improved the standard Beluga Whale Optimisation (BWO) algorithm in three ways: (1) use Bernoulli chaos mapping to explore the entire search space during population initialization; (2) incorporate the position update formula of the Firefly Algorithm (FA) to improve the position update strategy and convergence speed; (3) apply the opposition-based learning based on the lens imaging (lensOBL) mechanism to the optimal individual, which prevents the algorithm from getting stuck in local optima during each iteration. Subsequently, we adjusted the weights and thresholds of the BP model, deploying the IBWO approach. Ultimately, we employ the IBWO-BP model to predict the swelling and shrinkage ratio of air-dry volume, as well as the modulus of elasticity (MOE) and bending strength (MOR) of heat-treated wood. The benefit of IBWO is demonstrated through comparison with other meta-heuristic algorithms (MHAs). When compared to earlier prediction models, the results revealed that the mean square error (MSE) decreased by 39.7%, the root mean square error (RMSE) by 22.4%, the mean absolute percentage error (MAPE) by 9.8%, the mean absolute error (MAE) by 31.5%, and the standard deviation (STD) by 18.9%. Therefore, this model has excellent generalisation ability and relatively good prediction accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Wang,

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

Damage identification and failure characterization of plywood based on AE-BP Model

Liu,

Feng,

Zhang

et al. 2024

Eur. J. Wood Prod.

View full text Add to dashboard Cite

Prediction of equilibrium moisture content and swelling of Thermally modified hardwoods by artificial neural networks

Masoumi,

Bond

2024

Preprint

View full text Add to dashboard Cite

In this study artificial neural network (ANN) models were developed for predicting the effects of wood species, density, modifying time and temperature on the equilibrium moisture content (EMC) and swelling of six different thermally modified hardwood species. Lumber of Yellow-poplar (Liriodendron tulipifera); red oak (Quercus borealis); white ash (Fraxinus americana), red maple (Acer rubrum); hickory (Carya glabra), and black cherry (Prunus serotina) were selected. Using Keras and Pytorch libraries in Python, different feed forward and back propagation multilayer ANN models were created and tested. The best prediction models, determined based on the errors in training iterations, were selected and used for testing. Based on the performance analysis, the prediction ANN models are accurate, reliable and effective tools in terms of time and cost-effectiveness, for predicting the EMC and swelling characteristics of thermally modified wood. The multiple-input model was more accurate than the single-input model and it provided a prediction with R2 of 0.9975, 0.92 and MAPE of 1.36, 7.77 for EMC and swelling.

show abstract

Prediction of water absorption and swelling of thermally modified fir wood by artificial neural network models

Cited by 4 publications

References 46 publications

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

Damage identification and failure characterization of plywood based on AE-BP Model

Prediction of equilibrium moisture content and swelling of Thermally modified hardwoods by artificial neural networks

Contact Info

Product

Resources

About