Validity of artificial neural modeling to estimate time-dependent deflection of reinforced concrete beams

Abstract

The architecture and weights of an artificial neural network model that predicts time-dependent deflection have been developed and optimized. To satisfy the serviceability limit states, a concrete structure must be serviceable and perform its intended function throughout its working life. Excessive deflection should not impair the function of the structure or be aesthetically unacceptable. Cracks should not be unsightly or wide enough to lead to durability problems. Design for the serviceability limit states involves making reliable predictions of the instantaneous and time-dependent deflection of reinforced concrete beams. This is complicated by the nonlinear behavior of concrete caused mainly by cracking, tension stiffening, creep, and shrinkage. This paper provides a statistical approach for predicting the time-dependent deflection of reinforced concrete beams at service loads and outlines a validity of the proposed method in comparison with the American Concrete Institute (ACI) method.

Keywords

• time-dependent deflection
• beams
• reinforced concrete
• statistical analysis
• artificial neural networks
• variability
• parametric study