Uncertainty quantification of the flexural strengthening of RC beams with UHPC using improved metaheuristic algorithm

Abstract

This study presents an artificial intelligence-based model to predict the flexural strengthening of RC beam/slab strengthened with UHPC (RC-UHPC) and perform uncertainty quantification of such values. Improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) was a model developed on artificial neural network (ANN) optimized by improved-eliminate-particle-swamp optimization (IEPSO). Experimental datasets were used to establish the hybrid ML model. The efficiency of IEPANN was compared against support vector regression (SVR) and multiple linear regression (MLR). The IEPANN demonstrates outstanding performance over MLR and SVR. The flexural strengthening of RC-UHPC depends on materials and geometrical configurations, which have stochastic characteristics. Convergence studies, sensitivity, and probabilistic analyses were conducted on each random parameter. Simulation results indicated that strengthening configuration is the most sensitive to flexural strengthening. Systematic assessment of uncertainties in flexural strengthening may lead to a higher confidence level in model outcomes, and RC-UHPC is examined and designed. It enhances construction reliability.

Keywords

• Flexural strengthening
• Uncertainty quantification
• UHPC-NSC beam
• IEPANN
• Monte Carlo simulation