On New Ideas for Design of Road Infrastructure: Hybrid Fatigue Analyses

Amir M Horr

doi:10.3390/app11020759

Abstract

To increase the pace of the design of safer road infrastructure and raise the active and passive safety features of road structures on the global stage, innovative and smart virtual tools are essential. One of the basic steps for such ground breaking numerical simulation technology would be to develop advanced smart hybrid techniques with dynamic adaptation into mainstream design and simulation tools that are used by engineering offices. In the research work herein, a new numerical framework including dynamic zoning, advanced grid interfacing, new computationally-efficient solvers, and genetic algorithm symbolic-regression has briefly been presented to address long-standing problems of speed, accuracy, and reliability of numerical tools. The fundamental physical and mathematical aspects of the new simulation framework are concisely presented. In addition, some outcomes of real-world case studies utilized using the proposed hybrid analytical and data-driven (i.e., machine learning, ML) scheme have been shown, where the design rule for road gantry structures is interrogated using the developed virtual tool. One of the main contributions of this paper is to show the benefits of using hybrid simulation technologies to model engineering systems along with the ML-based method to optimize their designs.

Highlights

The time and effort associated with the accurate virtual simulation of large engineering systems and the sophisticated nature of their designs have historically been challenging for mainstream design applications [5,6]
The purpose of this paper is to present a new prospect, which improves the design of safe road/rail infrastructural systems under various external loads
The technique has already impacts in which design rules were extracted from the large simulation scenario tables engineering systems was investigated

Summary

Introduction

Sci. The use of conventional numerical techniques including discretization and solutions of computational domains have been extensively utilized in the past fifty years to predict and design the responses of engineering systems. The use of conventional numerical techniques including discretization and solutions of computational domains have been extensively utilized in the past fifty years to predict and design the responses of engineering systems These design optimizations have come at high costs as extensive computational time, power, and efforts are required to handle difficult technical challenges related to the simulations of complex systems. The process of initial design/analyses under possible design life-time scenarios along with accurate virtual optimization of these dynamic systems has always been computationally stimulating and various representative (discrete and continuous) techniques have been proposed. The implementations of innovative concepts, including smart finite zoning and continuous/discrete interfacing along with the use of artificial intelligence (AI) and machine learning (ML) notions for data-driven virtual optimizations, have been proposed

Objectives

Methods

Conclusion