What does this research mean for the field?

A deep learning-based predictive model provides superior accuracy and reliability in estimating the shear strength of CFRP-strengthened concrete beams compared to traditional empirical design codes. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a predictive model for estimating shear strength of CFRP-wrapped concrete beams using deep learning techniques.

March 21, 2026Buildings0 citationsOpen Access

Predicting the Shear Capacity of CFRP-Wrapped Concrete Beams with Steel Stirrups Using Deep Learning

NMNasim Shakouri Mahmoudabadi CCCharles V. Camp AAAnwar Ahmad

Key Points

To develop a predictive model for estimating shear strength of CFRP-wrapped concrete beams using deep learning techniques.
Developed a deep neural network model for shear strength prediction
Used a dataset of 216 CFRP-wrapped concrete beams from existing research
Optimized model parameters with Optuna and validated using k-fold cross-validation
Conducted parametric studies on influencing factors for shear strength
Achieved superior prediction accuracy compared to traditional empirical design models
Validated predictions against established international design codes
Demonstrated sensitivity of predictions to factors like concrete strength and CFRP wrap thickness

Abstract

The use of fiber-reinforced polymers (FRPs) for strengthening existing reinforced concrete (RC) structures has significantly improved structural rehabilitation processes, providing efficient, durable, and non-invasive solutions. This study presents an advanced deep learning-based predictive model specifically developed to estimate the shear strength of concrete beams strengthened externally with carbon fiber-reinforced polymer (CFRP) composites. Using a comprehensive dataset of 216 experimentally tested CFRP-wrapped concrete beams drawn from existing research, a deep neural network model was rigorously optimized with the Optuna hyperparameter tuning framework and k-fold cross-validation to ensure robustness and generalizability. Model validation involved a thorough comparative analysis against established international design codes (ACI PRC-440.2-17, CSA-S806-12, JSCE) and a parametric study examining the sensitivity of shear strength predictions to key influencing factors, including concrete compressive strength, beam depth, and CFRP wrap thickness. Results demonstrated superior prediction accuracy and reliability of the deep learning approach compared to traditional empirical design models. Consequently, this research significantly enhances the precision of shear strength predictions for CFRP-strengthened concrete beams, supporting the development of more efficient and accurate structural rehabilitation and design guidelines.

Ask AI

Helpful

Bookmark

View Full Paper