Optimizing beam performance: ANSYS simulation and ANN-based analysis of CFRP strengthening with various opening shapes

In modern construction, pipes and ducts are necessary for computer networking, electrical systems, air conditioning, water distribution, sewage management, and critical services. These conduits, which typically have diameters between a few millimeters and half a meter, can weaken beam strength, increase deflection, encourage cracking, and lessen stiffness, all of which can compromise the structural integrity of buildings. One creative and affordable way to overcome these obstacles is to retrofit concrete structures with CFRP sheets. Many advantages come with this technology, including a favourable strength-to-weight ratio, resistance to corrosion, remarkable fatigue durability, simplicity of installation, and minimum impact on existing structural parts. The current research examines the performance of Reinforced Cement Concrete (RCC) beams featuring various openings—rectangular, rounded rectangular, elliptical, and circular—in the shear zone. The study assesses the performance of three different CFRP reinforcement procedures using ANSYS software. It considers three different wrapping methods in comparison to a control beam and an opening without wrapping. The focus of the analysis is on Finite Element Analysis (FEA) to observe stress variations under applied loads, enabling comparisons of different beam deflections. According to analytical data, the use of CFRP reinforcement around the apertures—both internally and externally—significantly increases load-carrying capacity, which is nearly identical to the control beam's—especially for circular holes where there is a more equal distribution of stress. Additionally, the study explores the generation of beam deflection data through ANSYS FEA simulations, which is followed by training an Artificial Neural Network (ANN) model in MATLAB and Python. The resulting ANN model serves as a rapid and accurate alternative to traditional FEA in structural analysis by effectively predicting beam deflections across various scenarios. This research contributes valuable insights towards improving structural resilience in contemporary construction practices, particularly regarding the integration of essential services.