Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

Acoustics is a field with significant application in wood science and technology for the
classification and grading, through non-destructive tests, of a large variety of products from …

This work presents a soft-sensor approach for estimating critical mechanical properties of
sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed …

In this paper, the artificial neural network technique using a multi-layer perceptron feed
forward scheme was used to model and predict the mode-I fracture behaviour of particulate …

This study proposes a combined machine learning–wave propagation approach for
nondestructive prediction of the modulus of elasticity (MOE) and rupture (MOR) of timber …

The in-situ monitoring and characterization of the mechanical properties of wood and timber
materials is of great importance due to their broad structural applications. The purpose of …

Classification of thermally modified wood is a critical assessment and control task that
assures the quality of thermally treated wood. Machine learning methods can be used for …

Background The elastic modulus of polyethylene terephthalate (PET) sheets is typically
measured through destructive tests that require specific sample preparation and time …

Nondestructive testing (NDT) includes several highly efficient techniques for the estimation
of the physical and mechanical properties of structural timber. Apart from visual grading …

The main goal of this study was to investigate the visual characteristics, recovery rate, and
flexural properties of sawn boards from a fibre-managed plantation Eucalyptus globulus …