Prediction and uncertainty quantification of ultimate bond strength between UHPC and reinforcing steel bar using a hybrid machine learning approach

Abstract

The composite action of the reinforcing bars in the UHPC involves complex and nonlinear mechanisms. Inadequate knowledge of their interaction may lead to insufficient bond resistance in the concrete structures. The behavior of reinforcing steel bars in UHPC is generally determined by conducting pull-out tests. However, these tests need amounts of time and cost. A soft computing technique is an alternative approach that exterminates the need to conduct pull-out tests. This study presents the application of artificial intelligence technique in predicting ultimate bond strength (UBS) between ultrahigh-performance concrete (UHPC) and steel reinforcement bars and performs uncertainty quantification of such values. A total of 333 pull-out test data points were collected. A hybrid machine learning (ML) model, improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) was developed to predict the UBS between UHPC and reinforcing bars. The efficacy of the IEPANN was evaluated against other ML models such as particle swamp optimization hybridized artificial neural network (PANN), support vector regression (SVR), and multiple linear regression (MLR). The IEPANN model was benchmarked against three design codes, including CEB-FIP, AS3600, EC2, and four empirical models. The result obtained based on R = 0.973, MAE = 1.880, RMSE = 2.595, MAPE = 7.708, and RI = 0.944 shows that the IEPANN model can predict UBS between UHPC and reinforcing steel bars with acceptable accuracy. The uncertainty of the UBS was quantified with geometrical and material configuration randomness based on Monte Carlo simulation. The simulation result shows that the yield strength of steel rebar is the most sensitive, while the embedment length is the least sensitive to the bond strength. Systematic assessment of uncertainties in UBS in the design of reinforced concrete members may lead to a higher level of confidence and enhance construction reliability.