STRUCTURAL RELIABILITY ASSESSMENT OF CONCRETE-FILLED STEEL FRAME BUILDINGS VIA A COMPLEX-VALUED DEEP SURROGATE MODEL

This paper presents a novel framework for structural reliability assessment of buildings incorporating Concrete-Filled Steel Tubular columns, utilizing a deep surrogate model formulated in the complex number domain. High-fidelity numerical models are developed using SAP۲۰۰۰ software, with analysis outputs pre-processed in MATLAB. A hybrid deep learning architecture is implemented within the PyTorch framework, featuring complex-valued parameters and activation functions that enable superior representation of phase-dependent and oscillatory behaviors inherent in nonlinear limit state functions. Each complex parameter simultaneously encodes both real and imaginary influences, enhancing representational efficiency while requiring fewer parameters than conventional real-valued networks. Bidirectional communication between MATLAB and PyTorch is established through system-level execution protocols, enabling seamless integration with the SM Toolbox for parametric structural modeling. The surrogate model is trained on strategically sampled datasets, with architecture complexity and dataset size adaptively determined based on parameter counts. Reliability indices are computed using the Weighted Average Simulation Method applied separately to real and imaginary components, with final reliability estimated through weighted averaging. The proposed method is validated through three mathematical benchmark functions and three engineering case studies, including a three-span continuous beam, a roof truss, and a ten-story building with CFST columns. Results demonstrate minimum improvements of ۷۹% in mathematical examples and up to ۹۵% in engineering applications regarding required function evaluations, while maintaining essentially zero estimation error. For the ten-story building, computation time reduced from approximately ۳.۹ days using conventional simulation to ۲.۳ hours—a ۹۸% improvement—demonstrating the framework's potential for efficient and accurate reliability assessment of complex structural systems.

کلیدواژه ها:

CFST ، SAP ۲۰۰۰ ، Py Torch ، Reliability ، Complex Space ، SM Toolbox