MS17.20 - Structural Health Monitoring

The use of fibre reinforced polymer (FRP) in civil construction applications has gained considerable popularity worldwide as suitable method for strengthening of existing concrete structures. However, the implementation of methods able to give a reliable prediction about the health of this type of structures is needed since the usual failure modes occur in a sudden and brittle way. The electromechanical impedance (EMI)-based structural health monitoring technique, based on the piezoelectric or electromechanical coupling effects, has been applied successfully for these structures. By analyzing the electric impedance variation of piezoelectric sensors installed on the structure in different frequency intervals along the monitoring process the structural damage can be investigated. However, EMI technique is very sensitive not only to structural incipient damages, but also to temperature fluctuations, that may adversely cause false alarms in real-life monitoring applications. On the other hand, FBGs are intrinsically sensitive not only to the strain but also to temperature and, thus, the combination of FBG and PZT technologies in combination with suitable data processing techniques might provide a suitable methodology to identify incipient damage in the presence of temperature variations. Machine learning (ML) approaches, such as clustering techniques, are an interesting alternative to detect hidden patterns from monitored data and, therefore, to separate the baseline from any anomalies experienced by the structure, either due to mechanical deterioration or change of temperature. This kind of techniques might identify complex patterns related to different stages of the tested structure. In this paper, an integrated electromechanical impedance approach with clustering machine learning techniques and the use of fibre Bragg grating (FBG) temperature and strain sensors for identification of concrete damage under varied temperatures is presented. Effectiveness of the proposed approach was verified on a concrete specimen strengthened with FRP NSM technique and subjected to different levels of sustained load for various time periods and at different temperatures. Strain and temperature were continuously measured using FBG sensors. In the same way, after each sustained loading period or important temperature variation, an EMI test was performed with different PZT sensors located at different areas of the beam. Even although the method has been applied in RC beams strengthened with NSM-FRP technique, it is an effective and original contribution to any SHM system based on EMI. It allows to address in a direct way, with low computational complexity, temperature variations and mechanical deterioration without using any reference baseline signatures measured at different temperatures such as it is done in the usual temperature variation compensation techniques.

In many structural health monitoring applications, different types of sensors monitor the same structure. Measured vibration response can include accelerations, velocities, displacements, and strains. It is important that all available information is utilized in damage detection for an early warning. Although displacement, velocity and acceleration are related (via time differentiation), their signals are not directly correlated. This makes it difficult to utilize all available sensor data in damage detection. Damage detection in the time domain includes certain advantages over feature-domain methods: (1) system identification is not needed, and (2) the number of data points is large, and the dimensionality is relatively low, resulting in higher statistical reliability. Some challenges of using measured response data directly in the time domain are: (1) The signal-to-noise ratio (SNR) may be too low to detect small damage. (2) The number of sensors must be larger than the number of active modes. (3) Different measured quantities are not directly correlated, as mentioned before. A possible solution to handle heterogeneous response data in the time domain for damage detection is to use autocovariance function (ACF) estimates instead of direct sensor data. If the excitation is stationary random, the ACFs have the same form as a free decay of the system. In particular, different response quantities yield functionally similar ACFs, which can be utilized in centralized sensor fusion. However, phase differences exist between the ACFs of different quantities, so that applying only spatial correlation between different ACFs is insufficient. However, spatiotemporal correlation between different ACFs exists and should be used. Spatiotemporal correlation makes it also possible to have a smaller number of sensors than the number of active modes. The SNR can be controlled by adjusting the measurement period. In the proposed method, a spatiotemporal covariance matrix is estimated from the ACFs of the training data from the undamaged structure under different environmental or operational conditions. Using novelty detection techniques, an extreme value statistics control chart is designed to detect damage. A numerical experiment was performed by simulating vibration measurements of a bridge deck under stationary random excitation and variable environmental conditions. Neither excitation nor environmental variables were measured. Damage was a crack in a steel girder. Noisy response was measured with displacement, velocity, acceleration, and strain sensors at different locations of the deck. Assuming ergodicity, ACFs were estimated and used in time-domain data analysis to detect damage.

This article presents the fabrication and proof-of-concept evaluation of a new reusable piezo-magnetic composite sensor apt for rapid application of the electro-mechanical impedance (EMI) technique on steel structures with minimal number of sensors. Conventioanlly, the EMI technique employs piezo sensors permanently surface bonded on the structure. However, this not only increases the risk of deterioration over a period, but also demands a large number of piezo sensors to accurately locate damage and assess its severity. The authors have developed several non-bonded sensor configurations, however, all of them involve time consuming installation and dismantling process which warrants high level of expertise. The proposed sensor configuration, on the other hand, deploys a miniaturized hassle-free configuration consisting of a piezo sensor bonded on a detachable magnetic substrate based , imparting it flexibility of multiple usage at various locations of the same structure on a one-by-one basis, thus significantly reducing the number of sensors. It is the capability of most rapid usability from among the reusable sensors developed so far. The earlier configurations warranted very accurate tightening using torque wrench. However, there is no such requirement in the proposed configuration. Repeatability of the proposed sensor has been verified experimentally and found to be excellent in terms of correlation coefficient. The damage sensitivity of the proposed sensor is also found to be of same order as the permanently bonded sensors. In nutshell, the proposed sensor is very much suitable for rapid health assessment of large steel structures in a cost-effective