MS17.8- Structural Health Monitoring

During their lifetime, civil structures and infrastructures are subjected to environmental and human-induced hazards that may lead to structural damages or, in some cases, to full collapse. Such failures are associated with increased risk for life and limb while environmental as well as monetary losses may be triggered with adverse societal footprint. Consequently, it is of high relevance to screen reliably the condition of the structural systems and, if necessary, to intervene as a means to safeguard the structural integrity and avoid undesirable failures. To this end, several structural health monitoring schemes are becoming a key element for various structures and infrastructure systems enabling the continuous assessment of their state. The latter, in turn, can allow for detecting damages at early age avoiding extensive failures or even sudden collapse. In order to identify the failures due to excessive loading events, measured time-varying responses are frequently used as the basis for damage detection. However, in reality, practical restrictions exist in mounting sensors on all the locations of the structure that an engineer would prefer to know the structural response. For example, sensing data from the submerged part of an offshore platform is commonly not available while the latter is also valid for various locations in bridges where the access is limited to apply and operate sensors. Therefore, the available measured data is, mostly, incomplete since only a limited number of sensors can be employed. Additionally, various sources of noise usually pollute the sparsely measured responses and the overall quality is compromised. As a remedy to this challenge, a new methodology is proposed herein to aid in estimating reliably both the linear and nonlinear dynamic response of a structural system with the use of limited and noisy measurements. Especially, a smoothing technique, called the fixed-lag algorithm, is coupled with an augmented extended Kalman filter, and such an integrated scheme is anticipated to increase the accuracy of the state estimation. The current study adopts a simulation framework as the testbed for the proposed scheme and the use of the FE software OpenSees allows the numerical nonlinear modeling of the structural system subjected to time-varying loads. Compared with the ones calculated directly from linear and nonlinear response history analysis, the estimated responses based on the proposed scheme are found to be associated with increased accuracy.

The main purpose of the Structural Health Monitoring (SHM) techniques applied in bridges is to monitor many structural parameters to prevent serious damage that may eventually lead to collapse of the entire structure. To date, Operational Modal Analysis (OMA) methods are widely seen as the most reliable SHM techniques. These encompass a series of procedures for deriving the modal parameters of a structure using the data acquired under its operating conditions, without recording the external excitation. However, traditional OMA methods generally require expensive setup and time-consuming procedures. With the aim of overcoming these issues, in this paper, an innovative Vehicle-Bridge Interaction (VBI) based approach is investigated as a low-cost monitoring system for the identification of the structural modal parameters. These are obtained from the analysis in frequency domain of the measured vehicle’s vibration response during several passages on the structure. The proposed approach has been therefore adopted in an experimental campaign carried out on a pedestrian bridge in the city of Palermo (Italy). In particular, a very low-cost setup comprising a scaled-up electric vehicle as mobile platform and few accelerometers has been used. Moreover, with the aim of using low-cost and diffuse sensors in a crowdsensing perspective, a MEMS accelerometer of a common smartphone has been adopted for the first time in this innovative approach. This choice was made to develop a crowdsensing approach, using sensors and smartphones inside each vehicle passing on the bridge, providing a significative database in order to monitor the structure continuously over time.

This paper presents the application of reverse engineering in assessing the magnitude of dynamic loading on an industry steel structure. The studied steel structure is subjected to a frequently occurring plug load in the pipeline of the conveying system. This dynamic load leads to significant vibrations of the structure, and it is likely to govern the fatigue analysis. To assess the magnitude of the dynamic load, vibration measurements have been performed. Velocities in the transverse, longitudinal and vertical directions are measured at five points throughout the structure for a continuous period of seven days. A finite element (FE) model of the structure is properly set up such that its eigenfrequencies match the dominant vibration frequencies found in the measurement data. Furthermore, a linear time history (LTH) analysis with impulse loading is carried out, to interpret and reproduce the measured vibrations. Two selected measurement transients, one representative excitation and one randomly selected excitation, are used to validate and fine-tune the FE model to match the vibration responses of the structure. The magnitude of the impulse load is tuned such that the response of the analysis model corresponds best to the measurement data in terms of the first and the second peak velocities, as well as frequencies. Based on the results of the FEM analyses, the magnitude of the dynamic load is determined and the maximum occurring stresses are calculated. Besides these, a statistical analysis is performed to determine the distribution of vibration velocities over the entire measurement period. With the combination of vibration measurements, FEM analysis and statistical analysis, a good estimation is made for the amount of stress cycles in 50 years, which can be further used in the fatigue analysis of the steel structure.