AbstractWe investigated the use of an Artificial Neural Network (ANN) to predict the Local Bond Stress (LBS) between Ultra-High-Performance Concrete (UHPC) and steel bars, in order to evaluate the accuracy of our LBS equation, proposed by Multiple Linear Regression (MLR). The experimental and numerical LBS results of specimens, based on RILEM standards and using pullout tests, were assessed by the ANN algorithm using the TensorFlow platform. For each specimen, steel bar diameters ($$d_{b} )$$
d
b
)
of 12, 14, 16, 18, and 20, concrete compressive strength ($$f_{c}^{\prime }$$
f
c
′
), bond lengths ($$L$$
L
), and concrete covers ($$C$$
C
) of $$d_{b}$$
d
b
, $$2d_{b}$$
2
d
b
, $$3d_{b}$$
3
d
b
and $$4d_{b}$$
4
d
b
were used as input parameters for our ANN. To obtain an accurate LBS equation, we first modified the existing formula, then used MLR to establish a new LBS equation. Finally, we applied ANN to verify our new proposed equation. The numerical pullout test values from ABAQUS and experimental results from our laboratory were compared with the proposed LBS equation and ANN algorithm results. The results confirmed that our LBS equation is logically accurate and that there is a strong agreement between the experimental, numerical, theoretical, and the predicted LBS values. Moreover, the ANN algorithm proved the precision of our proposed LBS equation.
Experimental Study on One-Way BFRP Bar-Reinforced UHPC Slabs under Concentrated Load