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Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric

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Linear Viscoelastic Wood Creep Models.

Tomasz Socha¹, Krzysztof Kula¹, Arkadiusz Denisiewicz¹

¹Institute of Civil Engineering, Faculty of Engineering and Technical Sciences, University of Zielona Góra, ul. Prof. Z. Szafrana 1, PL-65516 Zielona Góra, Poland.

Materials (Basel, Switzerland)

|December 11, 2025

Summary

This summary is machine-generated.

A six-parameter rheological model accurately describes wooden beam creep under multistage loading. Simpler models fail to capture wood

Keywords:

experimental verification long-term multistage loading model identification pine (Pinus sylvestris) wood beams six-parameter model wood rheology

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Area of Science:

Materials Science
Wood Mechanics
Rheology

Background:

Wooden beams exhibit complex viscoelastic behavior under load.
Accurate rheological models are crucial for predicting long-term performance.
Existing simple models inadequately describe wood creep, especially under varying loads.

Purpose of the Study:

To identify the optimal linear viscoelastic rheological model for wooden beams.
To evaluate the performance of different rheological models under multistage loading.
To determine the necessary model complexity for accurate wood creep prediction.

Main Methods:

Experimental testing of full-size pine wood beams.
Application of a long-term, multistage loading program.
Comparison of three-, four-, five-, and six-parameter rheological models.

Main Results:

Three- and four-parameter models are insufficient for wood creep.
A five-parameter model adequately describes creep under constant load.
A six-parameter model perfectly fits experimental data for multistage loading.

Conclusions:

A six-parameter rheological model is essential for accurately describing wood creep under multistage loading conditions.
Multistage loading experiments are critical for robust model verification.
The complexity of the rheological model must match the loading conditions for reliable predictions.