A SYSTEMATIC REVIEW OF THE STRUCTURAL BEHAVIOUR OF REINFORCED CONCRETE DEEP BEAMS WITH WEB OPENINGS UNDER COMBINED SHEAR AND FLEXURAL LOADING CONDITIONS

Abstract

Reinforced concrete deep beams with web openings represent a critical structural challenge in modern construction, particularly when subjected to combined shear and flexural loading conditions. This systematic review synthesizes the current state of knowledge regarding the structural behavior of such beams, encompassing experimental investigations, numerical modeling studies, and strengthening methodologies. The presence of web openings can reduce load-carrying capacity by 25-66% when positioned within critical load transfer zones, with capacity reduction ranging from merely 3-5% in flexure-dominated regions to 53% when openings intersect primary strut paths. Multiple strengthening techniques have been investigated, including externally bonded fiber-reinforced polymers (FRP), near-surface mounted (NSM) systems, welded wire mesh reinforcement, and hybrid approaches combining multiple techniques. Performance data from 40+ peer-reviewed studies reveal that hybrid engineered cementitious composite (ECC) and CFRP systems can achieve capacity recovery of up to 125% compared to baseline solid beams. Numerical finite element modeling using ABAQUS software has demonstrated prediction accuracy of 94-97%, enabling parametric analysis of opening size, shape, location, and reinforcement configurations. Key material parameters affecting performance include concrete compressive strength (capacity increases 45.5% from 40 to 100 MPa), shear span-to-depth ratio (50% capacity reduction for a/d increase from 1.08 to 2.7), and fiber volumetric content (11-68% improvement for 0.4-2.0% steel fiber addition). This review identifies critical research gaps in combined loading effects, long-term durability of strengthened systems, environmental exposure impacts, and the applicability of design codes for deep beams with openings. Future research priorities include large-scale testing, development of optimized hybrid strengthening systems, advanced machine learning-based predictive models, and investigation of sustainable and innovative materials.