MS17.22 - Structural Health Monitoring

Nonlinear guided waves show great promise for structural health monitoring (SHM) due to their high sensitivity to the early detection of material micro-structural defects. However, an SHM system contain inevitable non-damage-related nonlinear sources that may overwhelm the damage-induced nonlinear wave components, which in turn may jeopardize the practical implementation of the nonlinear-guided-wave-based SHM methodology. To eliminate these deceptive nonlinear interferences, this study introduces the concept of metamaterials to SHM. A wave filtering device, referred to as a meta-filter, is developed to be surface-mounted on the structure under inspection under a second harmonic Lamb wave based SHM paradigm. Through a topological design, the meta-filter enables ultra-wide stopbands to eliminate the secondary Lamb waves of the probing waves while preserving their strong fundamental wave components. The band structure, underlying wave filtering mechanism and the wave filtering function of the meta-filter are investigated through finite element simulations. Upon tactically introducing deceptive nonlinear interferences at the actuation area, exemplified by adhesive bonding layers in a piezoelectric-transducer-activated SHM system, the performance of the meta-filter is examined from an SHM perspective, and finally validated experimentally using a metal specimen containing local plasticity-related incipient damage. Results demonstrate that the designed MF entails significant enhancement of the detection ability of nonlinear-guided-wave-based SHM system for incipient damages on one hand, and also allows for flexible selection of the excitation frequency on the other hand, thanks to the customized band features enabled by the topological optimization.

In 2021, the road network in Germany included approximately 40,000 bridges of which most had been constructed between 1960 and 1985. Thus, most bridges have reached a critical age which makes the lifecycle management crucial and gives structural health monitoring (SHM) great significance. Unfortunately, SHM methods are still rather matter of research than part of technical regulations. Therefore, the German Research Foundation (DFG) has launched in September 2022 the priority program SPP 2388 100+ to develop new methods for digital representation, SHM and lifetime management of complex structures suitable for engineering practice. The present contribution is prepared within the LEMOTRA project as a part of SPP 2388 100+. Among various SHM methods, the approach based on the Kalman update for data assimilation between model and measurement is applied and further developed. A sound numerical model, the measurement system, a permanent data flow and assimilation shall provide an online prediction of the state and response parameters of the structure. The whole system could be considered as a kind of functional digital twin for SHM. A two-step update procedure is proposed and applied in this context. Various Kalman filters have already been used for state updates in structural dynamics. They require, however, the knowledge of the actual loading for robust predictions. As an alternative, white noise input functions have been applied that cannot generally guarantee reliable results for real structures. Therefore, a part of the measurement system is used to identify the loading itself in the first step. The choice of the sensor types, locations and measurement directions is based on two requirements. The measured values need to correlate well with the loading and are less influenced by potential system changes or damage. As a result, a suitable correlation between the measured values and the loading history can be defined, calibrated and used in the Kalman filter. In the second step, a different set of sensors is used for the data assimilation process. Here, the identified load from the first step is used as input for the model prediction at any given point of time which is then compared to the measurement data of the second set of sensors. The state parameters (e.g. displacement, velocity, acceleration) and the model parameters (e.g. stiffness, mass, damping) are then sequentially updated by a combination of different ensemble based Kalman filters. The proposed approach is implemented in MATLAB and tested on laboratory structures under various loading and damage scenarios. At that, the advantages and drawbacks of the approach are discussed.

Natural frequencies are perhaps the most widely used modal characteristics in vibration-based structural health monitoring. However, they can be highly influenced by temperature and this influence can high enough to completely mask the effect of even severe damage. This translates into a necessity for data normalization techniques to remove the influence of temperature and identify damage. Displacement mode shapes are less influenced by temperature, but obtaining them in a dense grid, which is required for damage localization, is cumbersome due to the large number of sensors needed. Strain mode shapes on the other hand can be almost insensitive to temperature variations, while obtaining them in a dense grid is possible when fiber-optic sensors such as fiber-Bragg gratings (FBG) are used. The current work presents the results of the continuous monitoring of a steel railway bridge for an one-year period, where modal data were collected for a wide temperature range. The bridge is instrumented with eighty FBG strain sensors, multiplexed in four fibers. The natural frequencies and strain mode shapes of ten modes have been automatically identified from operational strain time histories, on an hourly basis. A clear influence of temperature on the natural frequency of most modes is identified, especially during frost periods. On the contrary, the strain mode shapes are mostly insensitive to temperature changes and only these of some higher-order modes are slightly and uniformly influenced when frost occurs. This behavior is confirmed also by a finite element model (FE) of the bridge. The FE model is also used to investigate the influence of local stiffness changes on the modal characteristics. A clear and local change of the modal strain amplitude is observed at the location of the reduced stiffness, especially when information from all modes is combined in a sensitive damage index.

Bridges represent an essential part of the transport infrastructure network. During their lifetime, they have been subjected to several threats, which can be related to material degradation due to climate change and aging effects, increase of the traffic loads, extreme natural events or damage due to slow deformation phenomena. An advanced numerical modelling able to perform reliable collapse simulations and evaluate the residual bearing capacity is an effective practice for assessing the health of a bridge and for planning maintenance interventions when necessary. In this framework, a new modelling approach, called Applied Element Method (AEM), is emerging as a powerful tool for structural analysis applications. More in detail, it is based on a discretization of the structure into rigid solid elements, connected through a series of normal and shear springs distributed at the interfaces and reflecting the mechanical materials properties. Recent studies have demonstrated as AEM is capable of predicting with a high degree of accuracy the structural behavior from the elastic stage to crack initiation and propagation, steel yielding, up to element separation and collision. In this work, an AEM model of a curved roadway bridge undergoing slow landslide-induced movements was built and calibrated by using the results of ambient vibration testing and modal identification. A collapse simulation was carried out, predicting the deformation evolution of the bridge under increasing displacements caused by the slow landslide.