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In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlinearity. Recent advances in computation have rendered previously computationally infeasible analyses readily executable on common computer hardware. However, in certain use cases, such as uncertainty quantification or high precision real-time simulation, the computational cost remains a challenge. This necessitates the adoption of reduced-order modeling methods, which can reduce the computational toll of such nonlinear analyses. In this work, we propose a reduction scheme relying on the exploitation of an autoencoder as means to infer a latent space from output-only response data. This latent space, which in essence approximates the system’s nonlinear normal modes (NNMs), serves as an invertible reduction basis for the nonlinear system. The proposed machine learning framework is then complemented via the use of long short-term memory (LSTM) networks in the reduced space. These are used for creating a nonlinear reduced-order model (ROM) of the system, able to recreate the full system’s dynamic response under a known driving input. © 2021 American Society of Civil Engineers ISSN:0733-9399 ISSN:1943-7889

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and Wöhler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines. ISSN:0960-1481 ISSN:1879-0682

Current best practice in monitoring earthquake strong motion are dense networks comprising strong motion accelerometers that measure acceleration over a broad frequency and amplitude range. These instruments are capable of measuring translational motions of large earthquakes, but lack sensitivity to very low frequencies or permanent displacements. However, it is widely accepted that during earthquakes the rotational component of the ground motion, both static and dynamic, is large enough to contaminate the derived displacements from these sensors. Modern rotational sensors, are also very broadband, have a large dynamic range, and are not sensitive to translational motions. We explore the value of complementing accelerometers with these rotational sensors at seismic strong motion monitoring stations.The assessment of the errors introduced into accelerometer records from rotational ground motions is only possible with co-located rotational instruments sensitive enough to record the small rotation rates accompanying the translational motion. Operating accelerometers alongside gyros and additionally GNSS instrumentation should allow us to record the full 6 components (6C) of near-field earthquake motions, with increasing fidelity across a very broad frequency band for the strongest motions.We aim to demonstrate how, using a combination of the three sensor types, we can recover the full 6C ground motion, and hence also more reliable displacement records, using a versatile industrial six-axis robot that can produce controlled and repeatable 6C motion across a broad frequency band. Through the precise feedback loop used by the robot to stabilize its precise trajectory, we get a 6C recording of the driven motion represented by Euler rotations and displacements, which we use as ground truth. By simulating a combination of translational and rotational motions on the robot, we show that the 6C Kalman filter can accurately reproduce the clean simulated translational motion. By using a Kalman filter, we attempt to combine the different data sets using prediction and weighting of the observation data for an optimal solution. Our methodology tries to take into account the strengths and weaknesses of the individual instruments that are providing partly redundant and partly complementary ground motion information.

Small-strain foundation response has mostly been studied analytically, with limited experimental verification against 1g physical model tests. This paper revisits the problem of small-strain foundation response, conducting a series of centrifuge model tests, aiming to eliminate the limitations of 1g testing. A centrifuge modelling technique is developed, combining static pushover and dynamic impulse testing for similar systems. To allow for derivation of meaningful insights, a novel procedure for in-flight measurement of the distribution of shear modulus with depth is also developed. The latter combines spectral analysis of surface waves (SASW) measurement of the shear modulus G0 at the surface, and estimation of the distribution of the shear modulus G with depth using acceleration measurements in shaking tests. A novel centrifuge tube–actuator is developed and employed to discharge spherical projectiles against single-degree-of-freedom models lying on shallow foundations on sand. This allows generating dynamic impulse excitation, which is used to measure the small-strain dynamic rocking stiffness. The developed actuator is versatile, and was also used for in-flight SASW testing. The centrifuge model tests are shown to confirm the widely used and well-known formulas. This good agreement can also be seen as a confirmation of the validity of the developed experimental techniques.

In this work, a computational framework is proposed for fatigue damage estimation in structural systems by integrating operational experimental measurements in a high-fidelity, large-scale finite e...

Summary phane Bordas thanks funding provided by the European Research Council Starting Independent Research Grant (ERC Stg grant agreement No. 279578)“RealTCut towards real time multiscale simulation of cutting in non-linear materials with applications to surgical simulation and computer guided surgery”. Stephane Bordas also thanks the financial support of the Fonds National de la Recherche Luxembourg FWO-FNR grant INTER/FWO/15/10318764. Eleni Chatzi would like to acknowledge the support of the Swiss National Science Foundation under research grant # 200021_153379 “A Multiscale Hysteretic XFEM Scheme for the Analysis of Composite Structures". The authors would like to thank D.Sutula for many helpful discussions on the numerical treatment of crack propagation problems. This article is protected by copyright. All rights reserved.

Mechanical Systems and Signal Processing, 145, ISSN:0888-3270, ISSN:1096-1216

In this article the physics of horizontally polarized shear waves travelling across a locally resonant metasurface in an unconsolidated granular medium is experimentally and numerically explored. The metasurface is comprised of an arrangement of sub-wavelength horizontal mechanical resonators embedded in silica microbeads. The metasurface supports a frequency-tailorable attenuation zone induced by the translational mode of the resonators. The experimental and numerical findings reveal that the metasurface not only backscatters part of the energy, but also redirects the wavefront underneath the resonators leading to a considerable amplitude attenuation at the surface level, when all the resonators have similar resonant frequency. A more complex picture emerges when using resonators arranged in a so-called graded design, e.g., with a resonant frequency increasing/decreasing throughout the metasurface. Unlike Love waves propagating in a bi-layer medium, shear waves localized at the surface of our metasurface are not converted into bulk waves. Although a detachment from the surface occurs, the depth-dependent velocity profile of the granular medium prevents the mode-conversion, steering again the horizontally polarized shear waves towards the surface. The outcomes of our experimental and numerical studies allow for understanding the dynamics of wave propagation in resonant metamaterials embedded in vertically inhomogeneous soils and, therefore, are essential for improving the design of engineered devices for ground vibration and seismic wave containment., Manuscript submitted to Phys. Rev. Appl

The concept of post-tensioned timber structures has been developed and implemented since 2005. The technology relies on unbonded post-tensioned timber elements to create a moment-resisting ...

In this paper, system identification is coupled with optimization-based damage detection to provide accurate localization of cracks in thin plates, under dynamic loading. Detection relies on exploitation of strain measurements from a network of sensors deployed onto the plate structure. The data-driven approach is based on the detection of discrepancies between healthy and damaged modal strain curvatures, while the model-based method exploits an enriched finite element method coupled to an optimization algorithm to minimize discrepancies between the measured and modelled response of the structure. It is demonstrated, through a series of numerical experiments, that the fusion of data-driven and model-based approaches can be beneficial both in terms of accuracy and localization, as well as in terms of computational requirements. © 2021 Elsevier Ltd ISSN:0045-7949 ISSN:1879-2243

This paper investigates the effects of different factors on the performance of nonlinear model updating for a seven-story shear wall building model. The accuracy of calibrated models using different data features and modeling assumptions is studied by comparing the time and frequency responses of the models with the exact simulated ones. Simplified nonlinear finite element models of the shear wall building are calibrated so that the misfit between the considered response data features of the models and the structure is minimized. A refined FE model of the test structure, which was calibrated manually to match the shake table test data, is used instead of the real structure for this performance evaluation study. The simplified parsimonious FE models are composed of simple nonlinear beam-column fiber elements with nonlinearity infused in them by assigning generated hysteretic nonlinear material behaviors to uniaxial stress–strain relationship of the fibers. Four different types of data features and their combinations are used for model calibration: (1) time-varying instantaneous modal parameters, (2) displacement time histories, (3) acceleration time histories, and (4) dissipated hysteretic energy. It has been observed that the calibrated simplified FE models can accurately predict the nonlinear structural response in the absence of significant modeling errors. In the last part of this study, the physics-based models are further simplified for casting into state-space formulation and a real-time identification is performed using an Unscented Kalman filter. It has been shown that the performance of calibrated state-space models can be satisfactory when reasonable modeling assumptions are used.

This research paper presents the implementation of Bayesian updating to an innovative post-tensioned timber frame structure, which has recently been erected at ETH Zürich as the main lateral load carrying system of the ETH House of Natural Resources (HoNR). In this innovative timber frame, the columns and beams are solely connected via a straight tendon running through the beams at mid-height, in this way creating a moment-resisting frame structure. A numerical model of the post-tensioned frame structure is set up and updated on the basis of modal vibration data offering a deeper insight into the system’s behavior and corresponding parameters. The modal testing data is derived from laboratory testing of a 2D-frame set-up, investigated under different support conditions. The dynamic acceleration response of the structure was processed by means of subspace identification methods for inferring the modal characteristics of the structure (frequencies, mode shapes, and damping ratios). Parallel to the modal vibration tests, an a priori numerical model of the structure has been established, which nonetheless relies on a number of assumptions on defining structural properties, including material, support, and rigidity parameters. Bayesian model updating is adopted for the updating of these parameters by exploiting the value of information contained in the testing data. The finally rendered updated model features parameters of reduced uncertainty, which is attested by the superior fit to the experimental data. The updated model may now be employed for investigating further the dynamic behaviour of the existing frame system as implemented in the HoNR., Procedia Engineering, 199, X International Conference on Structural Dynamics, EURODYN 2017

Structural health monitoring offers an attractive tool for condition assessment, fault prognosis and life-cycle management of wind turbine components. However, owing to the intense loading conditions, geometrical nonlinearities, complex material properties and the lack of real-time information on induced structural response, damage detection and characterization of structural components comprise a challenging task. This study is focused on the problem of damage detection for a small-scale wind turbine (Sonkyo Energy Windspot 3.5 kW) experimental blade. To this end, the blade is dynamically tested in both its nominal (healthy) condition and for artificially induced damage of varying types and intensities. The response is monitored via a set of accelerometers; the acquired signals serve for damage detection via the use of appropriate statistical and modal damage detection methods. The former rely on extraction of a characteristic statistical quantity and establishment of an associated statistical hypothesis test, while the latter rely on tracking of damage-sensitive variations of modal properties. The results indicate that statistical-based methods outperform modal-based ones, succeeding in the detection of induced damage, even at low levels.

An extended finite element method (XFEM) for three dimensional (3D) non-planar linear elastic fracture is introduced, which provides optimal convergence through the use of enrichment in a fixed area around the crack front, while also improving the conditioning of the resulting system matrices. This is achieved by fusing a novel form of enrichment with existing blending techniques. Further, the adoption of higher order terms of the Williams expansion is also considered and the effects in the accuracy and conditioning of the method are studied. Moreover, some problems regarding the evaluation of stress intensity factors (SIFs) and element partitioning are dealt with. The accuracy and convergence properties of the method as well as the conditioning of the resulting stiffness matrices are investigated through the use of appropriate benchmark problems. It is shown that the proposed approach provides increased accuracy while requiring, for all cases considered, a reduced number of iterations for the solution of the resulting systems of equations. The positive impact of geometrical enrichment is further demonstrated in the accuracy of the computed SIFs where, for the examined cases, an improvement of up to 40% is achieved.

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, th...

Model updating based on system identification (SI) results is a well-established procedure to evaluate the reliability of a developed numerical model. In this inverse assessment problem, soil-foundation compliance is often not explicitly considered rigorously during design and/or purely numerical assessment. The present work aims to investigate the correlation between subsoil-foundation stiffness and modal characteristics of bridges, as a means to identify a threshold beyond which rigorous subsoil modelling is a prerequisite for reliable model updating. The second Kavala Ravine Bridge, in Greece, serves as the case study for this purpose for which a reasonably reliable finite element (FE) model is developed and updated based on ambient vibration measurements. Alternative soil profiles and subsequently redesigned foundation systems are then used to examine the effect that the correspondingly variable soil compliance would have on the natural frequencies of the bridge. It is shown that soil stiffness alone is not an adequate proxy to decide on the necessity for subsoil modelling, as the foundation stiffness (particularly in cases of softer soil profiles) tends to balance the dynamic properties of the holistic soil-foundation system. The soil-foundation stiffness is therefore the key parameter that dictates the need for refined modelling of soil–structure interaction in the framework of SI-based model updating.

Recent advances in computer vision techniques allow to obtain information on the dynamic behaviour of structures using commercial grade video recording devices. The advantage of such schemes lies in the non-invasive nature of video recording and the ability to extract information at a high spatial density utilizing structural features. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications., Proceedings, 4 (1), ISSN:2504-3900, 5th International Electronic Conference on Sensors and Applications

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.

This paper presents an interactive framework for the design of truss structures with incorporation of the subjective influence of user feedback in the design process. In the devised framework, the truss chords are described using Non-Uniform Rational Basis Splines (NURBS), a representation typically used in computer aided design (CAD) for describing free-form geometry. This allows for a convenient interface between the optimization scheme, namely a particle swarm optimizer (PSO), and the user. Based on the assumption that aesthetic design goals are not straightforwardly quantifiable, key elements for an interactive optimization framework are derived, and implemented for the design of truss structures subject to an additional set of structural criteria/constraints. Setting off from an initial design the user can visually assess interesting solutions that arise during the optimization process, save them for later assessment, actively drive the optimization towards individual goals, re-initialize the process from a set of preferred solutions, or restart the design. For translating the user's perception into quantifiable terms, a criterion is introduced to measure the similarity of candidate solutions with respect to reference designs. The framework is then applied in the design of 2D and 3D truss structures. The effectiveness of the similarity criteria, as well as the ability of the user to drive the optimization towards specific design goals is demonstrated.

Chain-link wire nets are used for slope stabilization, natural hazard protection systems, mine and tunnel safety and many other important applications. In rockfall protection barriers the nets are designed to withstand dynamic, impulsive loadings. As they are composed of ultra-high strength steel wires with loose three-dimensional connections, the high resistance nets are very flexible and serve to efficiently distribute loads throughout the structure. Rockfall barrier design requires accurate numerical simulations. In this work, a Finite Element model of chain-link nets is developed. To treat the complex contact interactions among chain-link elements and rockfall barrier components we develop a computational scheme relying on a general contact algorithm. The non-linear force displacement response of the net obtained in tensile quasi-static laboratory tests is successfully reproduced by the numerical model. The model parameters are obtained by optimization techniques. The calibrated chain-link model with contact is shown to successfully simulate a full-scale test of a flexible rockfall protection barrier. The computational schemes allow us to accurately model the mechanical behaviour of chain-link wire nets with loose connections.

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 applicab...

Derivation of a reduced order metamodel of increased computational efficiency.Incorporation of model uncertainty via expansion on a Polynomial Chaos (PC) basis.Coupling of NARX and PC into a new metamodeling approach, termed the PC-NARX method.The excitation (load) is additionally parameterized as a model input.Efficient replacement of refined and computationally costly models is achieved. This paper introduces a metamodeling strategy able to account for the uncertainties in simulating nonlinear, dynamically evolving engineering systems. Polynomial Chaos (PC) expansion is implemented for the development of stochastic metamodels capable of representing the response of numerical models with uncertain input variables. The models employed are of the Nonlinear AutoRegressive with eXogenous (NARX) input form, comprising parameters that are random variables themselves. By expanding these parameters onto an appropriately selected PC basis, the resulting PC-NARX metamodel achieves vast reduction in computational time with sufficient accuracy, yielding a suitable tool for implementations where replacement of computationally costly models is sought.

In the context of the development and promotion of innovative and efficient hybrid timber structures, a pilot building, the ETH House of Natural Resources, has been designed. The building comprises three innovative structural systems, a post-tensioned timber frame, a composite beech laminated veneer lumber–concrete floor and a biaxial timber slab made of cross-laminated timber and beech laminated veneer lumber. The building will serve as an office building for the laboratory of hydraulics, hydrology and glaciology of ETH Zurich; as a ‘living lab’ for the implementation of a permanent sensor network for long-term monitoring purposes; and as a setup for a field testing campaign of in situ tests at different construction stages. Results of the first two series of this testing campaign and the derived modal characteristics of the structure are presented in this paper. The obtained modal data will extend the knowledge base on innovative hybrid timber structures and help to predict structural response for the case of significant loads such as high wind loads or earthquakes, through an extended model element updating procedure.

Safety and Reliability – Safe Societies in a Changing World, ISBN:978-0-8153-8682-7, ISBN:978-1-351-17466-4, ISBN:0-8153-8682-6

This review chapter outlines the outcomes of a combined experimental-numerical investigation on the retrofitting of masonry structures by means of polymeric textile reinforcement. Masonry systems comprise a significant portion of cultural heritage structures, particularly within European borders. Several of these systems are faced with progressive ageing effects and are exposed to extreme events, as for instance intense seismicity levels for structures in the center of Italy. As a result, the attention of the engineering community and infrastructure operators has turned to the development, testing, and eventual implementation of effective strengthening and protection solutions. This work overviews such a candidate, identified as a full-coverage reinforcement in the form of a polymeric multi-axial textile. This investigation is motivated by the EU-funded projects Polytect and Polymast, in the context of which this protection solution was developed. This chapter is primarily concerned with the adequate simulation and verification of the retrofitted system, in ways that are computationally affordable yet robust in terms of simulation accuracy. To this end, finite element-based mesoscopic and multiscale representations are overviewed and discussed within the context of characterization, identification and performance assessment.

The monitoring of rigid structures of modal frequencies greater than 5 Hz and sub-mm displacement is mainly based so far on relative quantities from accelerometers, strain gauges etc. Additionally geodetic techniques such as GPS and Robotic Total Stations (RTS) are constrained by their low accuracy (few mm) and their low sampling rates. In this study the application of QDaedalus is presented, which constitutes a measuring system developed at the Geodesy and Geodynamics Lab, ETH Zurich and consists of a small CCD camera and Total Station, for the monitoring of the oscillations of a rigid structure. In collaboration with the Institute of Structural Engineering of ETH Zurich and EMPA, the QDaedalus system was used for monitoring of the sub-mm displacement of a rigid prototype beam and the estimation of its modal frequencies up to 30 Hz. The results of the QDaedalus data analysis were compared to those of accelerometers and proved to hold sufficient accuracy and suitably supplementing the existing monitoring techniques.

Summary Recent events of significant seismic activity (L' Aquila 2009, Christchurch 2011), have highlighted the importance of accurately assessing the properties of structures, for estimating their risk in future earthquakes as well as detecting and quantifying damage following an event. This demand may be fulfilled through implementation of SHM methodologies, which offer a means of assessing structural condition through the use of sensor data. Furthermore, the emergence of various sensor technologies has improved the quality of collected signals and made the idea of fusing heterogeneous data more appealing. Similar steps were achieved in advancing system identification algorithms. Several methods such as the Autoregressive Moving Average, Least Squares Estimation, Eigensystem Realization Algorithm, and Subspace State-Space System Identification (SSID) perform well in identifying elastic properties. However, the resulting matrices are often not obtained with respect to the physical basis, which is often desirable. Such a representation is obtained by default in Bayesian estimation methods, as the Unscented Kalman Filter and Particle Filters, which have recently been applied in the literature for data fusion problems. In this work, an experimental structure mounted on a shake table is monitored using accelerometers and displacement sensors. A method is developed (T-SSID) to transform the results of the SSID onto the physical basis. The introduced method and the Unscented Kalman Filter are employed identifying the structural properties and detecting an artificially induced in the original system damage. The performance of various sensor setups, which include fusion of heterogeneous sensors, are investigated to illustrate the performance of both methods, their ability to localize damage, and the benefits of data fusion. Copyright © 2014 John Wiley & Sons, Ltd.

Rockfall barriers are key protection systems in mountainous regions worldwide. They are designed to intercept and capture falling rocks. Most systems are composed of flexible steel wire-nets connected to wire-rope cables which are in turn attached to steel posts and anchored to the ground. Additional energy dissipating devices can be connected to the cables to enhance the performance of the barriers. The composition and geometrical arrangement of wires forming net components and cables are a severe limitation to realistic finite element (FE) modeling of rockfall barriers. Detailed models are computationally expensive. Therefore, macroscopic models reflecting the system behavior on a coarser scale, i.e., the global behavior, need to be derived. In this work, macroscopic FE models pertaining to the modeling of the wire-ring net and the spiral cables have been developed and investigated. Material plasticity, damage initiation and ductile damage are taken into account. The non-linear force displacement response obtained in laboratory tests has been successfully reproduced by introducing additional parameters. An inverse optimization process based on the multi-island genetic algorithm is used for the determination of model parameters. The developed numerical approach succeeds in modeling rockfall barrier connections in a manner that is more refined than previous attempts, yet of low computational expense. The accuracy of this approach is verified by the match between simulation and test results of a full scale rockfall barrier prototype that was tested according to the ETAG027 standard.

This work reports the outcomes of an extensive experimental campaign on the retrofitting of masonry walls by means of carbon fiber reinforced polymer (CFRP) sheets, carried out at University of Applied Sciences (UAS) Fribourg. In the first stage, static-cyclic shear tests were conducted on the masonry walls, followed by a second stage of tensile tests on alternative configurations of mechanical anchorage so as to assess the effects on the structural response and to identify the associated limits. In the static-cyclic shear tests, it was found that the resistance of masonry walls retrofitted with CFRP sheets was improved by up to 70%, and the deformability was improved by up to 10% in comparison to the un-retrofitted specimens. The experimental tests conducted on alternate configurations of mechanical anchorages indicate that the tested materials and configurations rely heavily on details. The sensitivity of CFRP sheets to edges, non-uniformities on any adherend, and bonding defects can cause premature CFRP failure and, hence, pose problems for the efficient design of a retrofitting scheme. As indicated by the results of this investigation, effective anchorage can be achieved when eccentric loading of the mechanical anchorage is avoided and a smooth bonding surface is guaranteed., Polymers, 6 (7), ISSN:2073-4360

The dynamic analysis of a pneumatic beam structure, termed the Tensairity girder, is experimentally, numerically, and analytically studied. The structural concept of Tensairity relies on the combination of an airbeam with conventional struts, which leads in a light-weight structure of significant load-bearing capacity. By focusing on the analysis of the dynamic response of this structure, the objective of this work is to determine the pressure-dependent modal characteristics of the pneumatic beam and to couple these with the associated material properties. Based on the results of a modal identification procedure, relying on hammer and white noise excitation tests, a finite-element (FE) model is updated to reflect the actual system response. This procedure reveals the membrane’s shear modulus as the material property that more heavily relies upon the pressure level of the Tensairity girder. The experimental and numerical investigations indicate that the dynamic behavior of the beam can be expressed...

This work addresses the problem of vibration-based damage detection on structures operating under significant levels of uncertainty originating from variable environmental and operational conditions. For this purpose, a Gaussian Process Regression Vector AR (GPR-VAR) model is postulated for the representation of the vibration response of a structure at multiple measurement locations as a function of the uncertain inputs. The GPR-VAR model is a hierarchical Bayes representation associating the vibration response, the model parameters and the uncertain inputs. The Bayesian framework further helps to quantify the confidence attributed to the decision conditioned on the quality of the training set. The workings of the method are demonstrated via a simulated wind turbine blade driven by turbulent wind excitation, with uncertainty being introduced by changing temperatures and wind speeds., Procedia Engineering, 199, X International Conference on Structural Dynamics, EURODYN 2017

Aero-elasticity is a term that refers to the interaction between the aerodynamic, inertial and elastic loads when a structure is exposed to fluid flow such as turbulent wind inflow. Various commercial and research-based simulators are available to compute the wind turbine aero-elastic loads. These aero-elastic simulators are of varying complexity and might bear different underlying assumptions, pertaining to physics, mathematical and computational formulations. However, currently established practice dictates that the adopted aero-elastic simulators are verified and validated on the basis of measurements from test wind turbines. As a result, it is generally hard to establish one simulator as superior to another in terms of their predicted output. The objective in this paper is to statistically aggregate the fatigue load on a wind turbine blade when simultaneous simulations are performed using multiple simulators. The simulators of the wind turbine blade are of varying fidelity, and uncertainty in the modelling and assumptions on the model inputs are implicitly included, and taken into account in the statistical analysis. The main concept followed here is that rather than treating the output of the simulators as individual information sources, we consider them as part of an ensemble, which can be clustered and then aggregated to predict the “most likely” fatigue load, hence reducing the inherent model-form uncertainty.

The structural integrity of buildings and infrastructures can be affected by either environmental conditions or unforeseen external actions. In order to efficiently detect damage, intended as an irreversible degradation of the structural stiffness, many identification algorithms have been proposed in the literature. Nevertheless, a crucial aspect to accurately estimate and locate such damage pertains to the configuration of the deployed structural health monitoring (SHM) system. In addressing this goal, a framework is here proposed for the optimal design of sensor networks, in terms of number, type and spatial deployment of the sensors. The rationale of the method is to simultaneously maximize the information associated with the measurements, and minimize the total cost of the experimental setup; the overarching goal thus lies in the maximization of the information per unit cost, for the efficient allocation of resources. The value of the SHM system is quantified through the Shannon information gain between the a-priori knowledge of the mechanical properties and the values estimated, on the basis of measurements. The types of sensors contained into the overall SHM mix largely affects the estimation accuracy since, as a rule of thumb, the higher the sensor cost, the higher the signal-to-noise ratio and, therefore, the better the attainable estimation. In order to tackle the aforementioned multi objective optimization problem and to derive the associated Pareto front, a-posteriori solution methods relying on evolutionary algorithms are adopted. The proposed method is applied to a shear-type structure, namely the Pirelli tower in Milan, and the relevant multi-criteria optimization solutions are presented., Procedia Engineering, 199, X International Conference on Structural Dynamics, EURODYN 2017

Wind turbines operate under non-stationary dynamic loads to which they constantly adapt by regulating the orientation of the blades and the rotor, as well as the generator torque resulting in characteristic responses (i.e. operating regimes) over a range of operating conditions. We propose a method to classify the operating regimes from coarse resolution data recorded by the turbine supervisory controller (i.e. data from the SCADA system). It relies on design knowledge, and algorithms for dimensionality reduction and classification. High resolution acceleration measurements from a custom structural state monitoring system and a data set of several channels from the SCADA system are used for validation. Estimation of the level of damage accumulated on structural components based on the classification of operating regimes is shown as an application.

Fatigue constitutes a major and highly-uncertain safety-related factor for wind turbines. In order to ensure a reliable fatigue assessment of such structures, it is essential that stress predictions be based on the actual structural behaviour. The response identification of operational wind turbines in a global framework constitutes a challenging problem due to the uncertainties associated with the variability of the wind loading and the dynamics of the rotor. In reducing these uncertainties, this study proposes a substructuring approach, which abolishes the need for modelling the intricate and time-varying dynamics of the rotor. Instead, response prediction is performed on a substructure model of the tower and the effect of wind loads and servo dynamics is accounted for via the estimated interface forces at the top of the support structure. The application is based on synthetic vibration data generated via the FAST software and an output-only Bayesian filter employing the structural model of the support structure. The effectiveness of the proposed framework is presented in terms of fatigue damage estimates at different locations on the tower.

This study tackles the challenge of operational damage identification on wind turbine blades and proposes a novel framework for damage detection and localization. A vibration-based scheme is proposed, which tracks the variability of the mode shape curvatures (MSCs) of the blade along its plane direction. The method consists of a training and a diagnostics stage. In the former, MSC information for a number of predefined modes is extracted over varying operational conditions in the healthy state of the blade and, via the implementation of the principal component analysis (PCA), a statistical characterization of each blade’s node is estimated. Then, during the diagnostics stage, the MSCs are assembled and the same PCA mapping is enforced. A corresponding damage index is established, in order to detect and localize damage, if it exists. In both stages, MSC extraction is based on the successful estimation of vector autoregressive moving average (VARX) models that rely on pressure excitation and distributed strain measurements.

A series of prototype dynamic centrifuge experiments is carried out to investigate the influence of soil properties and structural parameters on the Soil Structure Interaction (SSI) effect. Established analytical models are herein experimentally verified, and are proven accurate in estimating the system's natural frequency characteristics. It is observed that period elongation is strongly correlated to the relative superstructure-foundation stiffness. Although the present study deals exclusively with the small-strain near-linear range, the experimental response indicates occurrence of nonlinearity. The identified damping results remarkably larger than its analytical estimate and proves highly strain-dependent, raising questions on the reliability of existing analytical methods in capturing the actual dissipation mechanisms. An extended experimental dataset is formed under realistic stress and strain soil conditions, and is implemented, for the first time, for verification of existing analytical models offering valuable insight into the theory and serving as a benchmark for engineering practice. ISSN:0267-7261 ISSN:1879-341X

This research work aims at deriving an optimal sensor configuration for the modal identification of an innovative timber structure. The goal is to extract the maximum possible information from the structure, while minimizing the amount of deployed sensors. Several different optimal placement methods are implemented, including the effective independence method (EFI), the modal kinetic energy method (MKE) and the information entropy method (IEI). Additionally, a modified version of the IEI method, incorporating a prediction error correlation term is introduced. This addition alleviates sensor clustering around a single location, which often occurs if the modal input data is generated from a finite element model of a dense mesh of possible sensor positions. The study shows, that to obtain an optimal sensor setup, prediction error correlation effects should be considered in order to avoid sensor clustering, and the multiaxiality of sensors should be taken into account during the optimization process.

This study overviews the development and implementation of a stochastic framework efficiently fusing operational response data with external influencing agents, namely environmental conditions, for representing structural behavior in its complete operational spectrum. The delivered methodology results in the formulation of a structural performance indicator serving as a precursor of potential damage or deterioration. The proposed approach relies on the combined use of (1) a polynomial chaos expansion method, able to account for structural variations tied to the variation of the acting agents; and (2) an independent component analysis algorithm, for extraction of salient behavioral features. The efficacy of the introduced method is demonstrated on field data from actual large-scale systems in both operational and damaged states.

This study assesses the implementation of locally resonant metamaterials in seismic isolation applications by investigating their potential feasibility in the [0.5, 5.0] Hz frequency band. To this end, via adoption of both Bloch’s theory and classical vibration analysis, the one-dimensional mass-in-mass lattice is analyzed in three overlapping subbands and corresponding relations for the structural parameters are derived. Accordingly, a basic unit cell is proposed that incorporates a core of dense material that is suspended in a thin shell composed of a lightweight material using distributed elastomeric tendons. Initial numerical simulations on finite-dimensional lattices reveal significant potential in the proposed solution and encourage further investigation, particularly to the two-dimensional case.

In recent years, developed societies have largely adopted smart systems operating on the basis of information extracted from data. For infrastructure systems as well, structural health monitoring (SHM) has long advocated a data-driven scheme for facilitating the operation and maintenance of infrastructure. In materializing such a goal, this paper demonstrates the procedures and outcomes of a SHM framework employed on an unconventional structure, namely, the recently built “Kaeng Krachan” Elephant Shelter at the Zurich Zoo, relying on a deployed set of Fiber Bragg Grating (FBG) strain sensors. The structure comprises an 80-m span free-form timber-composite cupola, carried by a post-tensioned reinforced concrete (RC) ring. FBG strain sensors are embedded into the ring in close vicinity to critical regions, selected in collaboration with the design engineers. The continuously acquired strain data are then exploited for extraction of performance indicators, relying on implementation of output-only identification methodologies. To this end, a non-parametric and a parametric output-only method, namely, a principal component analysis (PCA) scheme versus a Vector AutoRegressive (VAR) model, are employed and compared. Preconditioning of the predictive model is performed on the “healthy,” or undamaged, state of the structure, and the misfit between model predictions and subsequent measurements is exploited as a damage precursor. In this case, the VAR scheme proves a more robust representation of the measured strains, when compared against PCA, as a result of its inherent feature of memory., Frontiers in Built Environment, 2, ISSN:2297-3362

In this work, a study of computational and implementational efficiency is presented, on the treatment of Linear Elastic Fracture Mechanics (LEFM) problems. To this end, the Scaled Boundary Finite Element Method (SBFEM), is compared against the popular eXtended Finite Element Method (XFEM) and the standard FEM approach for efficient calculation of Stress Intensity Factors (SIFs). The aim is to examine SBFEM’s potential for inclusion within a multiscale fracture mechanics framework. The above features will be exploited to solve a series of benchmarks in LEFM comparing XFEM, SBFEM and commercial FEM software to analytical solutions. The extent to which the SBFEM lends itself for inclusion within a multiscale framework will further be assessed.

This article describes a structural system identification approach for the characterization of a novel retrofitting textile, the “Composite Seismic Wallpaper.” This polymeric textile was developed within the EU co-funded project Polytect as a full coverage method for increasing the seismic resistance of masonry structures. Recently, the wallpaper has been full-scale tested, on a two storey building, at the Eucentre (Pavia) as part of the Seismic Engineering Research Infrastructures for European Synergies (SERIES) program. In this article, an advanced multistage identification methodology is proposed for the successful simulation of this novel material based on the results of the extensive experimental campaign. The identification is essentially formulated as an inverse problem that combines a Genetic Algorithm (GA) as the optimizer and a finite element (FE) model as the physical model of the structure. The aim is material characterization and modeling of the dynamic response of the structure; an issue which is nontrivial due to the intrinsic complexities associated with both masonry and polymers. The process outlined herein is successful in yielding a calibrated model that can more accurately capture the experimentally observed behavior of this three-dimensional full-scale test case.

SUMMARY In this study, a novel method is presented for non-linear, non-Gaussian online state and parameter identification, developed for use in structural health monitoring (SHM) problems. The algorithm consists of a particle filter (PF) that combines the use of the standard PF with mutation operators. The algorithm aims at alleviating the sample impoverishment problem, which is a well-known limitation of the standard PF, yielding it inefficient for demanding non-linear identification problems. To overcome this hurdle, we introduce here an alternative approach, influenced by the principles of evolutionary computation. After the standard PF steps are performed to a point where the sample diversity drops below some threshold, the unfit particles are replaced by either the fittest particles or the current weighted estimate of the state. Next, the time-invariant components of the particles are mutated under some mutation probability, and the new sample is then propagated to the next time step. This process is well suited for joint state and parameter estimation problems, as is usually the case in SHM techniques. As a result, the loss of diversity associated with the standard PF is overcome, and the new PF with mutation is shown to outperform the standard PF and the unscented Kalman filter for the case of high process noise. The method is validated through an established benchmark problem found in the literature, lying outside of the structural identification concept, and a previously referenced 3DOF structural system with hysteresis elaborating the SHM aspect. Copyright © 2012 John Wiley & Sons, Ltd.