Filippo Ubertini

Dynamic Characterization of a Soft Elastomeric Capacitor for Structural Health Monitoring

AbstractStructural health monitoring of civil infrastructures is a difficult task, often impeded by the geometrical size of the monitored systems. Recent advances in conducting polymers enabled the fabrication of flexible sensors capable of covering large areas, a possible solution to the monitoring challenge of mesoscale systems. The authors have previously proposed a novel sensor consisting of a soft elastomeric capacitor (SEC) acting as a strain gauge. Arranged in a network configuration, the SECs have the potential to cover very large surfaces. In this paper, understanding of the proposed sensor is furthered by evaluating its performance at vibration-based monitoring of large-scale structures. The dynamic behavior of the SEC is characterized by subjecting the sensor to a frequency sweep, and detecting vibration modes of a full-scale steel beam. Results show that the sensor can be used to detect fundamental modes and dynamic input. Also, a network of SECs is used for output-only modal identification of...

Novel nanocomposite technologies for dynamic monitoring of structures: a comparison between cement-based embeddable and soft elastomeric surface?sensors

The authors have recently developed two novel solutions for strain sensing using nanocomposite materials. While they both aim at providing cost-effective solutions for the monitoring of local information on large-scale structures, the technologies are different in their applications and physical principles. One sensor is made of a cementitious material, which could make it suitable for embedding within the core of concrete structures prior to casting, and is a resistor, consisting of a carbon nanotube cement-based transducer. The other sensor can be used to create an external sensing skin and is a capacitor, consisting of a flexible conducting elastomer fabricated from a nanocomposite mix, and deployable in a network setup to cover large structural surfaces. In this paper, we advance the understanding of nanocomposite sensing technologies by investigating the potential of both novel sensors for the dynamic monitoring of civil structures. First, an in-depth dynamic characterization of the sensors using a uniaxial test machine is conducted. Second, their performance at dynamic monitoring of a full-scale concrete beam is assessed, and compared against off-the-shelf accelerometers. Experimental results show that both novel technologies compare well against mature sensors at vibration-based structural health monitoring, showing the promise of nanocomposite technologies for the monitoring of large-scale structural systems.

Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis:

This article presents the development and the results of 1 year of implementation of a simple vibration-based structural health monitoring system for preventive conservation and condition-based maintenance of an Italian monumental masonry bell-tower. The system is based on the data recorded by a small number of high-sensitivity accelerometers, on remote automated frequency tracking and on a multivariate statistical analysis criterion for damage detection, combining data regression, principal component analysis, and novelty analysis. The analysis of monitoring data highlights the main characteristics of the response of the tower to wind, swinging bells, and low-return period earthquakes. Despite the low levels of vibration in operational conditions, the system is seen able to track the time evolution of five natural frequencies of the structure and successfully use such information for detecting anomalous deviations from normal conditions. More in general, the presented results show a promise toward a more widespread use of low-cost vibration-based monitoring systems for cultural heritage preservation.

Electromechanical modelling of a new class of nanocomposite cement-based sensors for structural health monitoring

This work focuses on the analysis of a new nanocomposite cement-based sensor (carbon nanotube cement-based sensor), for applications in vibration-based structural health monitoring of civil engineering structures. The sensor is constituted of a cement paste doped with multi-walled carbon nanotubes, so that mechanical deformations produce a measurable change of the electrical resistance. Prior work of some of the authors has addressed the fabrication process, dynamic behaviour and implementation to full-scale structural components. Here, we investigate the effectiveness of a linear lumped-circuit electromechanical model, in which dynamic sensing is associated with a strain-dependent modulation of the internal resistance. Salient circuit parameters are identified from a series of experiments where the distance between the electrodes is parametrically varied. Experimental results indicate that the lumped-circuit model is capable of accurately predicting the step response to a voltage input and its steady-state response to a harmonic uniaxial deformation. Importantly, the model is successful in anticipating the presence of a superharmonic component in sensor’s output.

Wind Analysis of a Suspension Bridge: Identification and Finite-Element Model Simulation

A framework for numerically predicting the wind-excited response of suspension bridges with a certain level of confidence is established by means of output only system identification, model updating, wind-response simulation, and input-output comparison. A real case study represented by the identification and analysis of a newly built suspension bridge is considered. In system identification, the estimates of the modal parameters of the structure are provided with uncertainty bounds that take into account variations in identified modal features arising from different selections of the main parameters in the implementation of the identification technique. Based on the identified modal parameters, a finite-element model of the bridge is updated via an optimization technique. The updated model is then employed for numerically predicting the wind-excited structural response. Comparison with recorded data allows to check the accuracy of the model’s predictions as well as to indicate possible strategies for ref...

Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications

The digital simulation of wind velocity fields, modeled as multivariate stationary Gaussian processes, is a widely adopted tool to generate the external input for response analysis of wind-sensitive nonlinear structures. The problem does not entail any theoretical difficulty, existing already a large number of well-established techniques, such as the accurate weighted amplitude wave superposition (WAWS) method. However, reducing the computational effort required by the WAWS method is sometimes necessary, especially when dealing with complex structures and high-dimensional simulation domains. In these cases, approximate formulas must be adopted, which however require an appropriate tuning of some fundamental parameters in such a way to achieve an acceptable level of accuracy if compared to that obtained using the WAWS method. Among the different techniques available for this purpose, autoregressive (AR) filters and algorithms exploiting the proper orthogonal decomposition (POD) of the spectral matrix deserve a special attention. In this paper, a properly organized way for implementing stochastic wind simulation algorithms is outlined at first. Then, taking the WAWS method as a reference from the viewpoint of the accuracy of the simulated samples, a comparative study between POD-based and AR techniques is proposed, with a particular attention to computational effort and memory requirements.

Nonlinear State Observation for Cable Dynamics

We explore the applicability of non-linear state observation to cable dynamics. The aim is to capture from the minimal number of measurements a larger description of the state to be employed in active or semi-active control policies. To this end, a non-linear state observer is designed analytically, in the space of modal amplitudes, following relevant literature results. The main theory of non-linear state observation is preliminary reviewed and the applicability to the dynamics of structural cables is discussed, including asymptotic stability and minimal number of measurements. Next a sample non-resonant cable is considered and numerical simulations are carried out in order to test the observation error stability under different conditions. A non-collocated feedback control strategy, based on transversal actuation, is finally considered, in which the control algorithm is based on the estimated state variables. The with-observer control solution is compared with the ideal case in which the entire state of the system is known, thus highlighting the limits and potentialities of the proposed approach.

On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge

Structural health monitoring allows the automated condition assessment of civil infrastructure, leading to a cost-effective management of maintenance activities. However, there is still a debate in the literature about the effectiveness of available signal processing strategies to timely assess the health state of a structure. This paper is a contribution to this debate, by presenting the application of different vibration-based damage detection methods using up-to-date multivariate statistical analysis techniques applied to data acquired from a permanently monitored long-span arch bridge. Techniques based on dynamic regression models, linear and local principal component analysis, as well as on their combinations, including, in particular, the newly proposed method based on the combination of dynamic multiple linear regressions and local principal component analysis, and, finally, a method based on the recently proposed approach of cointegration, are considered. A first effort is made to formulate these methods within a unique mathematical framework, highlighting, in particular, the relevant parameters affecting their results and proposing objective criteria for their appropriate tuning and for choosing the length of the training period. Then, the considered damage detection methods are implemented and applied to field data, seeking for damage-sensitive features in the presence of variable environmental and operational conditions. The considered techniques are applied to time histories of identified modal frequencies of the bridge and their capability to reveal structural damage of varying severity is assessed using control charts. The case of an artificially imposed non-linear correlation between the features is also considered. The results provide, for the first time in the literature, an estimation of the minimum level of damage that can be realistically detected in the bridge using dynamic signatures and up-to-date signal processing algorithms, thus contributing to a more aware use of monitoring data and reliance over related health state assessment information.

Damage Detection and Localization from Dense Network of Strain Sensors

Structural health monitoring of large systems is a complex engineering task due to important practical issues. When dealing with large structures, damage diagnosis, localization, and prognosis necessitate a large number of sensors, which is a nontrivial task due to the lack of scalability of traditional sensing technologies. In order to address this challenge, the authors have recently proposed a novel sensing solution consisting of a low-cost soft elastomeric capacitor that transduces surface strains into measurable changes in capacitance. This paper demonstrates the potential of this technology for damage detection, localization, and prognosis when utilized in dense network configurations over large surfaces. A wind turbine blade is adopted as a case study, and numerical simulations demonstrate the effectiveness of a data-driven algorithm relying on distributed strain data in evidencing the presence and location of damage, and sequentially ranking its severity. Numerical results further show that the soft elastomeric capacitor may outperform traditional strain sensors in damage identification as it provides additive strain measurements without any preferential direction. Finally, simulation with reconstruction of measurements from missing or malfunctioning sensors using the concepts of virtual sensors and Kriging demonstrates the robustness of the proposed condition assessment methodology for sparser or malfunctioning grids.

Smart cement paste with carbon nanotubes

Abstract Carbon nanotubes can be added to cementitious materials to create a multifunctional nanocomposite with excellent piezoresistive and strain-sensing properties. The use of carbon nanotubes provides high strain sensitivity and signal-to-noise ratio, which are ideal for fabricating self-sensing nanocomposite cement paste for applications to structural health monitoring of concrete structures. The authors have recently developed an innovative sensor, the carbon nanotube cement-based sensor (CNTCS), made from a nanocomposite cement paste. This CNTCS is durable because it is fabricated with a material that has a life expectancy similar to the one of the monitored structure, and provides a natural strong mechanical bond with structural concrete. These characteristics enable embedded and spatially distributed sensing. The CNTCS was conceived to conduct vibration-based structural health monitoring. This chapter presents an overview of the sensing properties of cement pastes doped with multiwalled carbon nanotubes. After introducing the topic, including a brief state of the art on multifunctional cement-based materials, the fabrication process of the CNTCSs is discussed. Then, unstrained and strained electrical responses of the nanocomposite sensors are discussed and interpreted in light of a lumped electromechanical circuit model used to characterize both the polarization effects and the sensing capability of the CNTCSs. Finally, a proof-of-concept laboratory application consisting of vibration monitoring and modal identification of a full-scale concrete beam is presented, where outputs of CNTCSs are benchmarked against mature off-the-shelf sensing technologies.