## 1 Introduction  
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. However, 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 challenging task of bringing together the advanced computational models (with reasonable computational time) with the detailed aspects of mainstream design of large engineering systems has promoted the introduction of innovative frameworks [1, 2, 3, 4]. 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 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. Hence, 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 herein. The combination of physical, mathematical, and computational approaches has been used herein in response to the fundamental question of optimum virtual design of large systems. For large engineering systems like road/rail infrastructural network (e.g., large gantries), the use of new simulation technology has been promoted herein. Instead of having a single numerical domain with its associated grid (mesh), the emerging idea includes a new approach to represent various complexities (joint fatigue life, buffeting, and so on) within limited dynamic zones in the computational domain. This works for both implementing advanced mathematical models (e.g., plasticity and fatigue models) as well as lowering computational time. The concept provides the basis for multiple numerical domains, running in parallel with multiple instances of solvers that communicate using advanced interface routines (e.g., mapping and condensation techniques). The interaction/communication of zones within the simulation system can be managed smartly using AI and ML technologies and optimum design can be carried out using the combination of analytical and smart data-driven schemes. One of the main concerns for these new-generation techniques is how to represent changes of the dynamic system (e.g., fatigue, cracking, and non-linearities) to understand the consequences of solutions on design decisions. The research work herein is the fundamental idea for the procedure to combine a complexity-driven solution technique (e.g., non-linearity within individual dynamic zones) with efficient interfacing. These are accomplished within a designed computationally-efficient parallel simulation environment. The technique relies on separate and parallel efforts devoted to predicting the response of complex road/rail infrastructure (e.g., large gantries) and preventing the failure and damage to the vital infrastructure. The framework would also combine the sophisticated elastic-plastic material modelling on predicting damage and failure through the implementation of cross scale micro-macro mechanical models at selected dynamic zones. For the real-world case studies, using the new simulation system, dynamic response and fatigue characteristics of large lightweight lattice gantry structures under external loads were investigated. In these case studies, problems of dynamic and pressure wave loading were considered for new design of large highway lattice structures, and special attention was devoted to the dynamic response and fatigue characteristics.