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The Measurement- and Model-based Structural Analysis (MeMoS) integrates a finite element model into least squares adjustment and thus allows to evaluate a mechanical model and measurements in a combined analysis. To examine the capability to detect and localise damage using this integrated analysis MeMoS, a small-scale truss bridge made of aluminium profiles is built as a test specimen for this purpose.
Within the presented research project, experimental and numerical investigations were performed to develop a thin-shelled, modular, mobile element system made of a micro-reinforced ultra-high-performance ductile concrete (DUCON®). Material parameters were experimentally determined to adapt the material model within the numerical analysis applying the Drucker-Prager relationship. Afterwards, for validation of the numerical models, quasi-static and high-velocity impact tests were performed on plate-like structures. Finally, a suitable geometry of transportable barrier elements will be designed, which provides a maximum of resistance against impact by a minimum of weight and a maximum of mobility.
Surface-applied fibre optic strain sensors were investigated using a unique validation facility equipped with application-independent optical reference systems. First, different adhesives for the sensor's application were analysed regarding their material properties. Measurements resulting from conventional measurement techniques, such as thermo-mechanical analysis and dynamic mechanical analysis, were compared with measurements resulting from digital image correlation, which has the advantage of being a non-contact technique. Second, fibre optic strain sensors were applied to test specimens with the selected adhesives. Their strain-transfer mechanism was analysed in comparison with conventional strain gauges. Relative movements between the applied sensor and the test specimen were visualized easily using optical reference methods, digital image correlation and electronic speckle pattern interferometry. Conventional strain gauges showed limited opportunities for an objective strain-transfer analysis because they are also affected by application conditions.
Bending beams and slabs are typical examples for structural elements used for reinforced concrete structures such as bridge girders, T-beams and bridge decks. Their strength related failure modes at maximum loading can be divided into bending and shear failure. The failure of beams loaded in bending can occur with or without indication. Therefore, conventional design concepts aim on failure modes with sufficient indication (e.g. large deflections or cracks), as it occurs in the case of secondary flexural compression failure. These indicating factors can also be used for Structural Health Monitoring (SHM) of civil infrastructure systems (e.g. bridges) to identify structural changes. In this context, non-destructive testing (NDT) methods offer different techniques for measuring deflections or crack formation and opening. However, profound knowledge on the determining failure modes of bending beams and their detection by NDT methods is required for the reliable application of SHM. Different NDT methods have been used in this study for analysing the load-bearing behaviour of a reinforced concrete beam in bending. The different measuring techniques are briefly described and their applicability is discussed by means of experimental results. For this purpose, the load-bearing behaviour of a reinforced concrete beam having a span of 2.75 m was investigated in a four-point bending flexural test at laboratory scale. The focus is on the characterization of determining failure modes by optical NDT and the comparison with classical measuring techniques (e.g. deformation measurements by displacement transducers). The bending beam was equipped with two single-mode (SM) sensor fibres. One fibre served as Distributed Optical Fibre Sensor (DOFS), whereas the other fibre contained Fibre Bragg Grating (FBG) sensors. In addition, optical deformation measurements using Digital Image Correlation (DIC) and Stereophotogrammetry (SP) were conducted.
Damage mechanisms analysis of reinforced concrete beams in bending using non-destructive testing
(2019)
Beams that are loaded in transverse direction are one of the main structural elements used in reinforced concrete structures. Bending beams are used, for example, in bridge girders or joists. The strength related failure modes at maximum loading can be divided into bending and shear failure that could occur with or without indication. Conventional design concepts used in practice for designing structural elements subjected to bending aim at failure modes with sufficient indication (e.g. large deflections or cracks), as it occurs in the case of secondary flexure-compression-failure. These indicators can also be used to identify structural changes of civil infrastructure systems (e.g. bridges) using non-destructive testing methods for Structural Health Monitoring (SHM). However, profound knowledge on the determining failure modes of bending beams is required for the reliable application of structural health monitoring. Therefore, resulting effects of different thermal and mechanical loads on the response of a structure are investigated by means of a bridge demonstrator. The results of this investigation are used for developing a monitoring system. Different non-destructive testing (NDT) methods have been used for analyzing the deformation behavior of a reinforced concrete beam in bending. The suitability of the different measuring techniques will be discussed by means of the obtained experimental results. For this purpose, the deformation behavior of a reinforced concrete beam having a span of 2.75 m was investigated in a four-point bending flexural test at laboratory scale. The focus is on the characterization of determining failure modes by the NDT methods and the comparison with classical measuring techniques (e.g. deformation measurements by displacement transducers). The bending tests have been accompanied by Acoustic Emission analysis (AE), optical deformation measurements using Stereophotogrammetry (SP) and Digital Image Correlation (DIC). The conducted tests have shown that the three-dimensional detection of deformations and resulting strains with optical techniques is beneficial for analyzing the damage mechanism of loaded structures and related crack formation and propagation. Optical techniques can be supplemented by acoustic emission testing which gives detailed information on crack formation and progressing damage. The combination of both techniques offers the application as efficient monitoring system for larger structures.
Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.