Mechanical properties and probabilistic models of wood and engineered wood products: A review of green construction materials

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Abstract

Wood and engineered wood products are green construction materials with excellent mechanical properties, widely utilized in various buildings and bridges. This paper reviews their short-term and long-term mechanical properties and probabilistic models. Short-term mechanical properties analysis reveals strong tensile (up to 165 MPa) and bending strengths (up to 264 MPa), contrasted by lower compressive strength (up to 103 MPa) and minimal shear/rolling strength (up to 14 MPa). These properties vary based on species type, lay-up angle, and dimensions, and are mutually influential. It also demonstrated that innovative techniques such as compression and cross-laminated lay-ups enhance the strength and sustainability of wood products, utilizing smaller logs effectively and reducing carbon emissions. For long-term mechanical properties, empirical models typically used for creep strength assessment fail to account for load duration variability, a gap partly addressed by damage accumulation models which require further development. The considerable time for long-term tests underlines the need for developing accelerated load tests to more effectively assess durability. As for probabilistic models, numerical methods and machine learning provide advanced probabilistic models by analyzing extensive data, offering significant improvements over traditional experimental methods. The global contribution of the work is mainly to enhance the understanding of wood and engineered wood products for properly design and extensive application of timber structures in civil engineering and promoting sustainable development. • Damage models offer the benefits of taking account of duration of load uncertainties when wood and wood materials. • Mechanical model can describe the creep deformation of wood and wood-based materials. • Experimental methods, numerical methods and machine learning have been utilized to develop probabilistic mechanical models of wood and wood-materials.

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