Embodied Energy Optimization of Prestressed Concrete Road Flyovers by a Two-Phase Kriging Surrogate Model

Abstract

This study aims to establish a methodology for optimizing embodied energy while constructing lightened road flyovers. A cross-sectional analysis is conducted to determine design parameters through an exhaustive literature review. Based on this analysis, key design variables that can enhance the energy efficiency of the slab are identified. The methodology is divided into two phases: a statistical technique known as Latin Hypercube Sampling is initially employed to sample deck variables and create a response surface; subsequently, the response surface is fine-tuned through a Kriging-based optimization model. Consequently, a methodology has been developed that reduces the energy cost of constructing lightened slab bridge decks. Recommendations to improve energy efficiency include employing high slenderness ratios (approximately 1/28), minimizing concrete and active reinforcement usage, and increasing the amount of passive reinforcement.

Application of Neural Network Models with Ultra-Small Samples to Optimize the Ultrasonic Consolidation Parameters for ‘PEI Adherend/Prepreg (CF-PEI Fabric)/PEI Adherend’ Lap Joints

“…The first stage of approximation can be carried out using (i) linear interpolation algorithms based on triangulation, (ii) inverse distance weighting or polynomial methods, (iii) basis function approach, (iv) Kriging interpolation, (v) piecewise linear function, and (vi) component-wise splines [ 43 , 44 , 45 , 46 , 47 , 48 ]. Each of these methods has both advantages and drawbacks.…”