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Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.
Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load
events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data
measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to
varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load
bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the
monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To
address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems
that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data
obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods
and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital
twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam
in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass
monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian
system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.
The potentialities of a thin layer of steel-fiber reinforced self-compacting concrete (SFRSCC) for the flexural strengthening of handmade brick structural elements are investigated. For this purpose, an experimental program was carried out covering the relevant phenomena that can influence the effectiveness of this technique. The SFRSCC has a post-cracking residual tensile strength capable of improving the flexural stiffness, flexural resistance and ductility of prototypes representatives of ancient handmade brick based structures that fail in bending. To appraise the flexural strengthening effectiveness of this technique, straight beams composed of handmade bricks, low-strength mortar (LSM) and SFRSCC were tested. The obtained results have shown that this technique can increase significantly the flexural stiffness and resistance, and the ductility performance of this type of structures. The strengthening effectiveness depends on the post-cracking residual strength of the SFRSCC, its layer thickness and on the possibility of replacing part of the LSM by SFRSCC. Based on the experimental results for the characterization of the intervening materials, and adopting a cross section layer model capable of predicting the moment-curvature relationship for the distinct types of cross sections of this structural system, the maximum load registered in the tested prototypes was predicted with good accuracy.