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<p>The goal of the present work is to propose a novel bi-level optimization framework that simultaneously considers vibration serviceability at the upper level along with fire resistance performance at the lower level in an effort to minimize the floor depth while keeping integrity sets to both objectives. Most existing studies in this area have either used deterministic or single-objective optimization techniques, which are incapable of recognizing uncertainty and multi-scale interactions in real-life scenarios of fire vibration in process. These approaches often fall short in their ability to encapsulate material behavior which is stochastic in nature, the topological degradation, and the coupling behavior across scales, particularly when considered in the light of two hazard conditions. In this context, five innovative computational strategies are envisaged in process. The Uncertainty Infused Pareto Front Propagation (UPFP) captures stochastic variability in material and loading parameters to create robust Pareto fronts. The Graph-Coupled Fire Vibration Topology Optimizer (GCFVTO) models geometry- and physics-couplings through a dynamic graph. Deep Surrogate-Assisted Multi-Fidelity Optimization (DSAMFO) means to deploy deep Gaussian process surrogates for real-time design evaluation to make it computationally cheaper. Multi-Scale Serviceability-Safety Coupled Simulator (MS3CS) connects microstructural degradation with modal performance across scales. Ultimately, the Game-Theoretic Dual-Level Decision Optimizer (GTDLDO) provides a strategic equilibrium setting for counterbalancing conflicting objectives, utilizing Stackelberg game theory during implementation. These methods make together a computationally reliable, physically consistent, and uncertainty-aware optimization framework. This work may provide entirely new avenues towards robust and multi-objective decision-making within the performance-based floor system design.</p>

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Bi-level optimization of composite floor systems integrating fire resistance and vibration serviceability using multi-method computational intelligence process

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • Nischal P. Mungle,
  • Dnyaneshwar M. Mate,
  • Sham H. Mankar,
  • Sarang Pande,
  • Tejas R. Patil,
  • Manda Ukey,
  • Nisha Gongal,
  • Mona Mulchandani

摘要

The goal of the present work is to propose a novel bi-level optimization framework that simultaneously considers vibration serviceability at the upper level along with fire resistance performance at the lower level in an effort to minimize the floor depth while keeping integrity sets to both objectives. Most existing studies in this area have either used deterministic or single-objective optimization techniques, which are incapable of recognizing uncertainty and multi-scale interactions in real-life scenarios of fire vibration in process. These approaches often fall short in their ability to encapsulate material behavior which is stochastic in nature, the topological degradation, and the coupling behavior across scales, particularly when considered in the light of two hazard conditions. In this context, five innovative computational strategies are envisaged in process. The Uncertainty Infused Pareto Front Propagation (UPFP) captures stochastic variability in material and loading parameters to create robust Pareto fronts. The Graph-Coupled Fire Vibration Topology Optimizer (GCFVTO) models geometry- and physics-couplings through a dynamic graph. Deep Surrogate-Assisted Multi-Fidelity Optimization (DSAMFO) means to deploy deep Gaussian process surrogates for real-time design evaluation to make it computationally cheaper. Multi-Scale Serviceability-Safety Coupled Simulator (MS3CS) connects microstructural degradation with modal performance across scales. Ultimately, the Game-Theoretic Dual-Level Decision Optimizer (GTDLDO) provides a strategic equilibrium setting for counterbalancing conflicting objectives, utilizing Stackelberg game theory during implementation. These methods make together a computationally reliable, physically consistent, and uncertainty-aware optimization framework. This work may provide entirely new avenues towards robust and multi-objective decision-making within the performance-based floor system design.