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This study introduces a novel framework for establishing the stress-strain relationship for equivalent strut models of cross-laminated timber panels infilled in moment-resisting RC frames. High-fidelity models of RC frames with CLT infills are developed using a parametric finite element model in OpenSeesPy. A comprehensive parametric study using Latin Hypercube Sampling was conducted to generate 8.000 different frame configurations, varying parameters describing both the frame, the infill and the applied loads. Calibration of the stress-strain curves was conducted using a genetic algorithm-based optimization process, aimed at minimizing the mean squared error between the global force-displacement curves of the high-fidelity model and the equivalent strut model. Optimal parameters found by the genetic algorithm for the material properties assigned to the strut were used to derive a predictive model. A comprehensive predictive model was developed using the CatBoost machine learning algorithm, incorporating 16 input variables.

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Equivalent Strut Models for CLT Infills in RC Frames Based on Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Carlotta Pia Contiguglia,
  • Cristoforo Demartino,
  • Camillo Nuti

摘要

This study introduces a novel framework for establishing the stress-strain relationship for equivalent strut models of cross-laminated timber panels infilled in moment-resisting RC frames. High-fidelity models of RC frames with CLT infills are developed using a parametric finite element model in OpenSeesPy. A comprehensive parametric study using Latin Hypercube Sampling was conducted to generate 8.000 different frame configurations, varying parameters describing both the frame, the infill and the applied loads. Calibration of the stress-strain curves was conducted using a genetic algorithm-based optimization process, aimed at minimizing the mean squared error between the global force-displacement curves of the high-fidelity model and the equivalent strut model. Optimal parameters found by the genetic algorithm for the material properties assigned to the strut were used to derive a predictive model. A comprehensive predictive model was developed using the CatBoost machine learning algorithm, incorporating 16 input variables.