Eleni Chatzi

Experimental validation of the Kalman-type filters for online and real-time state and input estimation:

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.

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.

Vibration monitoring via spectro-temporal compressive sensing for wireless sensor networks

The reliable extraction of structural characteristics, such as modal information, from operating structural systems allows for the formation of indicators tied to structural performance and condition. Within this context, reliable monitoring systems and associated processing algorithms need be developed for a robust, yet cost-effective, extraction. Wireless Sensor Networks (WSNs) have in recent years surfaced as a promising technology to this end. Currently operating WSNs are however bounded by a number of restrictions relating to energy self-sustainability and energy data transmission costs, especially when applied within the context for vibration monitoring. The work presented herein proposes a remedy to heavy transmission costs by optimally combining the spectro-temporal information, which is already present in the signal, with a recently surfaced compressive sensing paradigm resulting in a robust signal reconstruction technique, which allows for reliable identification of modal shapes. To this end, this work outlines a step-by-step process for response time-series recovery from partially transmitted spectro-temporal information. The framework is validated on synthetic data generated for a benchmark structure of the American Society of Civil Engineers. On the basis of this example, this work further provides a cost analysis in comparison to fully transmitting wireless and tethered sensing solutions.

A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach

The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

Nonlinear modeling of a rotational MR damper via an enhanced Bouc–Wen model

The coupling of magnetorheological (MR) dampers with semi-active control schemes has proven to be an effective and failsafe approach for vibration mitigation of low-damped structures. However, due to the nonlinearities inherently relating to such damping devices, the characterization of the associated nonlinear phenomena is still a challenging task. Herein, an enhanced phenomenological modeling approach is proposed for the description of a rotational-type MR damper, which comprises a modified Bouc–Wen model coupled with an appropriately selected sigmoid function. In a first step, parameter optimization is performed on the basis of individual models in an effort to approximate the experimentally observed response for varying current levels and actuator force characteristics. In a second step, based on the previously identified parameters, a generalized best-fit model is proposed by performing a regression analysis. Finally, model validation is carried out via implementation on different sets of experimental data. The proposed model indeed renders an improved representation of the actually observed nonlinear behavior of the tested rotational MR damper.

SoRo-Track: A two-axis soft robotic platform for solar tracking and building-integrated photovoltaic applications

We present SoRo-Track, a two-axis soft robotic actuator (SRA) for solar tracking and building-integrated photovoltaic applications. SRAs are gaining increasing popularity compared to traditional actuators, such as dc motors and hydraulic or pneumatic pistons, due to their inherent compliance, low morphological complexity, high power-to-weight ratio, resilience to external shocks and adverse environmental conditions, design flexibility, ease of fabrication, and low costs. We present the design, modelling and experimental characterisation of SoRo-Track. Finally, we demonstrate the suitability of SoRo-Track for solar tracking applications, which makes it a viable component for dynamic building facades.

Maintenance planning using continuous-state partially observable Markov decision processes and non-linear action models

The signs of deterioration in worldwide infrastructure and the associated socio-economic and environmental losses call for sustainable resource management and policy-making. To this end, this work presents an enhanced variant of partially observable Markov decision processes (POMDPs) for the life cycle assessment and maintenance planning of infrastructure. POMDPs comprise a method, commonly employed in the field of robotics, for decision-making on the basis of uncertain observations. In the work presented herein, a continuous-state POMDP formulation is presented which is adapted to the problem of decision-making for optimal management of civil structures. The aforementioned problem may comprise non-linear and non-deterministic action and observation models. The continuous-state POMDP is herein coupled with a normalised unscented transform (NUT) in order to deliver a framework able to tackle non-linearities that likely characterise action models. The capabilities of this enhanced framework and its applicability to the maintenance planning problem are presented via two applications. In a first illustrative example, the use of the NUT is demonstrated within the framework of the value iteration algorithm. Next, the proposed continuous-state framework is compared against a discrete-state formulation for implementation on a life cycle assessment problem.

Semi-active control for vibration mitigation of structural systems incorporating uncertainties

This study introduces a novel semi-active control scheme, where the linear-quadratic regulator (LQR) is combined with an unscented Kalman filter (UKF) observer, for the real-time mitigation of structural vibration. Due to a number of factors, such as environmental effects and ageing processes, the controlled system may be characterized by uncertainties. The UKF, which comprises a nonlinear observer, is employed herein for devising an adaptive semi-active control scheme capable of tackling such a challenge. This is achieved through the real-time realization of joint state and parameter estimation during the structural control process via the proposed LQR-UKF approach. The behavior of the introduced scheme is exemplified through two numerical applications. The efficacy of the devised methodology is firstly compared against the standard LQR-KF approach in a linear benchmark application where the system model is assumed known a priori, and secondly, the method is validated on a joint state and parameter estimation problem where the system model is assumed uncertain, formulated as nonlinear, and updated in real-time.

Interactive particle swarm optimization for the architectural design of truss structures

This paper presents an interactive framework for the design of truss structures with aesthetic criteria. The truss chords are described using NURBS, a tool widely used in computer aided design (CAD) programs to describe free-form geometry. This allows for a convenient interface between the optimization scheme, a particle swarm optimizer, and the user. Driven from the fact that aesthetic design goals are not easily quantifiable, key elements are introduced and implemented herein towards an interactive framework for algorithmic design of truss structures. Within this framework, the user can visually assess interesting solutions, save them for later assessment, actively drive the optimization towards individual aims, re-initialize the optimization with a set of available solutions, or restart the design process. A criterion is introduced as a means of quantifying subjective goals, expressing the similarity of the shape of candidate solutions with respect to reference designs. The framework is tested on a benchmark case and then applied to the design of a truss tower. The effectiveness of the similarity criteria, as well as the ability of the user to drive the process towards specific design goals is demonstrated.

Stationary nonimaging lenses for solar concentration

A novel approach for the design of refractive lenses is presented, where the lens is mounted on a stationary aperture and the Sun is tracked by a moving solar cell. The purpose of this work is to design a quasi-stationary concentrator by replacing the two-axis tracking of the Sun with internal motion of the miniaturized solar cell inside the module. Families of lenses are designed with a variation of the simultaneous multiple surface technique in which the sawtooth genetic algorithm is implemented to optimize the geometric variables of the optic in order to produce high fluxes for a range of incidence angles. Finally, we show examples of the technique for lenses with 60° and 30° acceptance half-angles, with low to medium attainable concentrations.