Experiment and numerical analysis of laminated veneer lumber beams with carbon fiber reinforced polymer reinforcement
Year: 2025
Type: article
Abstract: This study investigates the reinforcement of Laminated Veneer Lumber (LVL) beams using Carbon Fiber-Reinforced Polymer (CFRP) sheets ranging from 300 mm to 1800 mm in length to enhance structural performance. While previous studies largely examined single reinforcement configurations, this work systematically identifies the effective reinforcement length range for LVL beams and introduces numerical expressions linking reinforcement ratio to key strength parameters. Experimental testing under four-point bending and advanced finite element (FE) modeling in ABAQUS were conducted to evaluate structural behavior. The innovation of this study lies in establishing a standardized FE modeling procedure validated against experiments, enabling accurate prediction of failure modes and strength improvements. The results indicate that reinforcement lengths of 300–900 mm (14 % - 42 % reinforcement ratio) yielded the most significant improvements, with ultimate load-carrying capacity increasing by up to 31 %, and fracture load and bending moment improving by up to 36 % compared to unreinforced beams. Qualitatively, reinforcement transformed brittle failure into ductile responses and delayed crack initiation, while longer reinforcement lengths (≥1200 mm) provided only marginal gains. The FE model closely matched experimental results, with a maximum variance of less than 14 %, confirming its reliability. The findings establish the optimal reinforcement ratio for LVL beams, provide design predictive equations, and clarify the load transfer mechanism between CFRP sheets and timber, offering practical guidance for sustainable structural applications.
Source: Structures
Institution: La Trobe University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,510
Funder: La Trobe University
OpenAlex ID: W4416816465
