This paper presents a novel computational approach to empirical hysteresis modelling applied to timber-based structures based on a combined data model-driven strategy. While the backbone curve is simulated using the experimental cyclic response based on a step-by-step optimization problem (data-driven approach), analytical functions describe the re-loading curves (model-driven approach). Empirical hysteresis models developed so far for timber structures are model-driven. However, the backbone curves can exhibit a highly irregular non-smooth trend, difficult to mirror using analytical formulations. The challenge in mirroring the experimental backbone using closed-form formulations has led to an extended set of parameters to be calibrated in existing literature models This paper presents a novel approach to the empirical hysteresis model, where the experimental data are directly involved, as a whole, in the model formulation. This model aims to be a possible trade-off between model complexity and accuracy. A reduced number of parameters needed to describe the re-loading paths is counterbalanced using an entire subset of the experimental data. The paper delivers the developed Matlab and Python codes for further implementation as a user-defined element within a Finite Element software. (C) 2022 Elsevier Ltd. All rights reserved.

Hysteresis modeling of timber-based structural systems using a combined data and model-driven approach

Aloisio, A
;
Fragiacomo, M
2022

Abstract

This paper presents a novel computational approach to empirical hysteresis modelling applied to timber-based structures based on a combined data model-driven strategy. While the backbone curve is simulated using the experimental cyclic response based on a step-by-step optimization problem (data-driven approach), analytical functions describe the re-loading curves (model-driven approach). Empirical hysteresis models developed so far for timber structures are model-driven. However, the backbone curves can exhibit a highly irregular non-smooth trend, difficult to mirror using analytical formulations. The challenge in mirroring the experimental backbone using closed-form formulations has led to an extended set of parameters to be calibrated in existing literature models This paper presents a novel approach to the empirical hysteresis model, where the experimental data are directly involved, as a whole, in the model formulation. This model aims to be a possible trade-off between model complexity and accuracy. A reduced number of parameters needed to describe the re-loading paths is counterbalanced using an entire subset of the experimental data. The paper delivers the developed Matlab and Python codes for further implementation as a user-defined element within a Finite Element software. (C) 2022 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/194839
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