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Hysteretic phenomena have been observed in different branches of engineering sciences. Although each of them has its own characteristics, Madelung’s rules are common among most of them. Based on Madelung’s rules, we propose a general approach to the simulation of both the rate-independent and rate-dependent hystereses with either congruent or non-congruent loops. In this approach, a static function accommodates different properties of the hystereses. Using the learning capability of the neural networks, an adaptive general model for hysteresis is introduced according to the proposed approach and it is called the neuro-Madelung model. Using various hystereses from different areas of engineering with different properties, the proposed model has been evaluated and the results show that the model is successful in the simulation of the considered hystereses. Comparison of the performance of the proposed model with different hysteresis models on experimental data indicates that the neuro-Madelung model has much better performance than them and its results are in excellent agreement with experimental data. In addition, an implicit inverse of the neuro-Madelung model is introduced. Its application in an open-loop control of a rate-dependent hysteresis is assessed and the results show its success.

This paper describes a novel technique for detecting internal or unseen damage in structural steel members by combining measurements from full-field three-dimensional digital image correlation (3D-DIC) with a topology optimization framework. Unlike the majority of conventional methods that rely on specialized forms of surface-penetrating waves or radiation imaging, this work employs optical cameras to measure surface strains and deformations using the 3D-DIC technique followed by an optimization approach to detect the existing damage. This data-rich representation of the structural component’s behavior is then used to reconstruct the underlying subsurface abnormalities via an inverse mechanical problem. The inverse problem is solved using a topology optimization formulation that iteratively adjusts a fine-tuned finite element model (FEM) of the structure to reveal irregularities within it. Having recently demonstrated the feasibility of detecting and reconstructing defects in small-scale structural components, this paper expands on the authors' previous work to demonstrate the feasibility and performance of the proposed method through an experimental program in which a set of large-scale structural steel beams with and without buried defects tested using a full-field 3D DIC sensing approach. The structure’s initial FEM is first created to discretize the member into elements whose constitutive properties are treated as unknowns in the optimization problem. The goal of the optimization is to minimize the discrepancies between the observed full-field response measured experimentally using DIC and that computed numerically using the model. To that end, an objective function is first computed as the sum of residuals by mapping both responses onto a common grid, which is then pushed to a minimum via the method of moving asymptotes (MMA) as the optimization algorithm. This study demonstrates that the proposed approach can identify unseen damage with an average accuracy (ACC) score of 96.80% on the defined configurations, with relatively minimal false identifications.