Automated Estimation of Structural Elements of Prestressed Concrete Bridge During Planning Phase: A Multi Expression Programming Approach

Abstract

To address the early-stage uncertainties in quantity estimation of prestressed concrete (PSC) bridges due to limited information, this research introduces a novel approach using multi expression programming (MEP) to develop predictive models for estimating the steel weight and concrete volume. The study gathered 172 datasets, encompassing eleven input variables compiled from a range of international research publications i.e., average height of pier Hp, number of piles Np, average depth of piles Dp, average span length SL, total length of bridge Lb, width of deck Wd, average height of abutment Ha, total number of spans Ns, total number of girders Ng, height of girders Hg and number of lanes NL, as well as two output variables: the weight of reinforcing steel Bs and the volume of concrete Vc. The MEP models produced simple mathematical expressions to calculate the steel weight and concrete volume for PSC bridges. The accuracy of these models was evaluated using various performance metrics, including mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), R-squared (R2 ), Relative squared error (RSE), and Relative Root Mean Square Error (RRMSE). Additionally, sensitivity and parametric analyses were conducted on the MEP models. The results demonstrate that MEP-based methods deliver highly accurate predictions, outperforming the multilinear regression model Vc (R2 avg=0.784) and Bs (R2 avg=0.787) used in this study. Specifically, the MEP model predicting steel weight Bs (R2 avg= 0.965) showed better results than the MEP model for concrete volume Vc (R2 avg=0.910) prediction. The study suggests that the proposed MEP-based formulae offer a quick, reliable, and rational method for estimating materials in PSC bridges, aiding decision-makers in reducing the time required for quantity estimations, evaluating alternatives and minimizing the risks associated with over and underestimating material requirements in preliminary estimates.