Keywords
Strengthen, Textile-reinforced mortar, Ferrocement, Torsion, FE analysis, ABAQUS
Document Type
Article
Abstract
Strengthening reinforced concrete (RC) elements is vital to rehabilitate aging infrastructure and adopting modern standards. While conventional designs focus on axial, shear, and bending forces, torsional moments become highly effective in specific beams. This study employs finite element analysis using ABAQUS to investigate effective strengthening strategies for enhancing the torsional performance of RC beams. Two strengthening systems, ferrocement and Textile Reinforced Mortar (TRM), were examined using two-sided and three-sided wrapping configurations. The numerical investigation evaluated torsional capacity, twist response, and crack propagation, with results compared to an unstrengthened control beam. The findings indicate that both strengthening techniques significantly improved stiffness and torsional resistance. Three-sided wrapping provided enhanced confinement, resulting in improved crack control and higher torsional capacity compared with two-sided strengthening. Among the investigated systems, ferrocement with three-sided wrapping achieved the best overall performance, increasing ultimate torque by approximately 50% while enhancing ductility and energy absorption due to the continuous wire mesh action that delayed crack initiation. TRM-strengthened beams exhibited increased stiffness but comparatively lower deformation capacity, achieving an ultimate torque capacity increase of about 41%. Overall, ferrocement demonstrated superior improvement in cracking and ultimate torsional moments relative to TRM. The results highlight ferrocement, particularly with three-sided application, as an efficient and practical solution for upgrading RC beams subjected to torsional loading.
Recommended Citation
Taha, Roula; Tarsha, Ihssan; and AL-Allaf, Muneeb
(2026)
"Comparison of Textile-Reinforced Mortar and Ferrocement in Torsional Strengthening of RC Beams Using Finite Element Analysis,"
Al-Farahidi Expert Systems Journal: Vol. 2:
Iss.
1, Article 9.
DOI: https://doi.org/10.65645/3105-9104.1028
DOI
10.65645/3105-9104.1028