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The prestressed strengthening of structures via use of bonded iron-based shape memory alloys (Fe-SMAs) has proven promising, albeit with concerns regarding the temperature dependency of the adhesive properties. In this study, the effect of activation temperature and generated prestress are investigated experimentally. Six Fe-SMA-to-steel adhesively bonded joints, comprising different Fe-SMA strips (non-prestrained and prestrained) and activation strategies (full activation and partial activation), were prepared, activated via electrical resistance heating, and tested under quasi-static loading. It is found that the bond–slip behavior of a joint with activation can be modeled by that of an equivalent non-activated joint. The generated prestress influences the full-range behavior by raising the base tensile stress level of the Fe-SMA strip, with negligible effects on further aspects of the full-range behavior. With the increasing activation temperature, the fracture energy is initially increased and eventually reduced, while the bond capacity and effective bond length are retained almost constant. • The bond–slip behaviors of joints with and without activation are interchangeable. • Bonded anchorage zones are required to hold the generated prestress. • Excessive local heating may introduce damage to the adhesive bond. • The fracture energy of the adhesive bond changes with the activation temperature. • The change of bond capacity due to the activation is negligible. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The robustness of online Bayesian Identification algorithms has been illustrated for a wide range of physical problems. The successful convergence of such algorithms for problems of highly nonlinear nature is tied to the precision of the approximation of the observed system via the employed state-space model. More sophisticated approximations, result in an increase of both the convergence rate and the associated computational cost. Nonetheless, the latter is a price worth paying for ensuring the former in the case of highly nonlinear problems. The assumption placed by most Bayesian filtering algorithms is that the parameters to be estimated are identifiable at each updating step. This however is a property that does not necessarily hold for systems involving non-smooth nonlinearities, i.e., systems whose state-space or measurement equations are not differentiable. Such systems are linked to the modelling of damage-related phenomena such as plasticity, impact and sliding amongst other. Hence, a separate approach is proposed herein, namely the modification of algorithms to account for the lack of identifiability encountered for parameters of a non-smooth system at a specific step. This modification is termed by the authors as, the Discontinuous, D - modification and relies on the idea that unidentifiable parameters should remain invariant in the corresponding updating steps. This work will illustrate the benefits of the D - modification on the convergence of the Unscented Kalman Filter for non-smooth problems. An example from the dynamics of rocking bodies will be used to demonstrate the advantages of the method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study proposes a computational framework for the online estimation of fatigue damage using operational vibration measurements from a limited number of sensors. To infer the stress response time histories required for fatigue prediction, the measured structural response is driven to a high fidelity finite element (FE) model, which is reconciled using appropriate model updating techniques that minimize the discrepancy between the experimental and analytical frequency response functions (FRFs). Fatigue is accordingly estimated via the Palmgren-Miner rule, while the available FE model allows for stress estimation at unmeasured spots. The method is successfully validated and assessed through an experimental study that pertains to a linear steel substructure supporting the entire body of a pre-beater assembly at a PPC power plant. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The application of adhesively-bonded joints for strengthening of structures using iron-based shape memory alloys (Fe-SMAs) has recently emerged in construction. Fe-SMAs and the majority of structural adhesives exhibit a pronounced nonlinear material behavior, which may result in a favorable ductile failure mechanism. The development, however, of a mechanical model to predict the structural behavior of the joint is non-trivial due to the presence of nonlinearity in the adherent and adhesive. This study aims to propose a semi-analytical and semi-numerical model for describing the mechanical behavior of Fe-SMA-to-steel adhesively bonded joints. The developed model serves three main functions: (i) estimating the bond capacity for a given interfacial fracture energy, and vice versa; (ii) processing the bond–slip (τ − s) behavior directly from the load–displacement (F − Δ) curve, and vice versa; and (iii) delivering a numerical method to simulate the full-range mechanical behavior of the bonded joints, namely the behavior at different loading stages. The model is validated using the experimental testing of 26 Fe-SMA-to-steel lap-shear joints, as well as 24 further bonded joints subject to shear with different adherents (e.g., stainless steel strips and Nickel–Titanium SMA wires) and base materials (e.g., concrete and composite polymer). An experimental data processing protocol, on the basis of the experimentally measured force–displacement (F − Δ) behavior and the distributed displacement along the bond line (s − x) via the Digital Image Correlation (DIC) technique, is further proposed to assess the full-range behavior of bonded joints. [Display omitted] • Debonding behavior of lap-shear joints comprising nonlinear adherents and adhesives. • An analytical model for bond capacity of joints with nonlinear adherents. • Inference of the bond–slip behavior directly from the load–displacement curve. • A numerical solution for the full-range debonding behavior. • A novel protocol for processing experimentally tested lap-shear behavior. [ABSTRACT FROM AUTHOR]

Abstract: The extended finite element formulation (XFEM) combined with genetic algorithms (GAs) have previously been shown to be very effective in the detection of flaws in structures. By this approach, the XFEM is used to model the forward problem and a GA is used as the optimization scheme, converging to the true flaw. The convergence is obtained by minimizing the error between sensor measurements and data obtained by solving the forward problem. The current study proposes several advances of this XFEM–GA algorithm, more specifically: (i) a novel genetic algorithm that accelerates the convergence of the scheme and alleviates entrapment in local optima, (ii) a generic XFEM formulation of an elliptical hole which is utilized to detect any type of flaw (cracks or holes) of any shape, and (iii) experimental verification of the approach for an arbitrary crack in a 2D plate. Convergence studies on various benchmark problems including the experimental verification clearly show the potential of this approach to detection of arbitrary flaws. [Copyright &y& Elsevier]

Abstract: This study presents a methodology for the on-line identification of nonlinear hysteretic systems where not only the parameters of the system are unknown but also the nature of the analytical model describing the system is not clearly established. To this end a Bayesian approach using the Unscented Kalman Filter (UKF) method has been applied in order to investigate the effects of model complexity and parametrization. The latter can be especially challenging in the case of realistic applications involving limited information availability. The state space formulation incorporates a Bouc–Wen type hysteretic model properly modified with additional polynomial or exponential-type nonlinear terms that are properly weighted throughout the identification procedure. The parameters associated with the candidate models might be subjected to constraints that can affect the stability of the estimation process when violated. An adaptive gain technique is introduced in order to tackle the problem of parameter boundaries. In addition, a twofold criterion based on the smoothness of the parameter prediction and the accuracy of the estimation is introduced in order to investigate the required model complexity as well as to potentially rule out ineffective terms during the identification procedure (on-line). Previous work, Smyth et al. (1999) , has dealt with the adaptive on-line identification of nonlinear hysteretic systems using a least-squares based algorithm. The current work explores the case of more severe nonlinearities that call for the expansion of the hysteretic models commonly used in literature. The method is validated through the identification of the highly nonlinear hysteretic behavior produced by the experimental setup described in Tasbihgoo et al. (2007) involving displacement and strain (restoring force) sensor readings. [Copyright &y& Elsevier]

While the concept of structural monitoring has been around for a number of decades, it remains under-exploited in practice. A main driver for this shortcoming lies in the difficulty to robustly and autonomously interpret the information that is extracted from dynamic data. This hindrance in properly deciphering the collected information may be attributed to the uncertainty that is inherent in i) the finite set of measured data, ii) the models employed for capturing the manifested dynamics, and more importantly, iii) the susceptibility of these systems to variations in Environmental and Operational Parameters (EOPs). In previous work of the authors, a Gaussian Process (GP) time-series approach has been introduced, which serves as a hierarchical input–output method to account for the influence of EOPs on structural response. This in turn enables a robust structural identification. In this scheme, the short-term dynamics are modeled by means of linear-in-the-parameters time-series models, while EOV dependence – acting on a long-term time scale – is achieved via GP regression of the model coefficients on measured EOPs. This work corresponds to a further advancement on this modeling approach, corresponding to its generalization to the vector response case. Particularly, the problem of global identification here is solved via an Expectation–Maximization algorithm tailored to the GP time-series model structure. Moreover, an EOP-dependent innovations covariance matrix is integrated in the model, which helps to capture variation in the vibration power. The resulting model does not only have the capability to represent the long-term response of a structure under variable EOPs, but also facilitates the enhanced tracking of modal quantities in contrast to traditional operational modal analysis techniques. The proposed approach is exemplified on the identification of the vibration response of a simulated wind turbine blade at different points along the blade axis in the flap-wise direction, under variability of both the acting wind speeds and ambient temperatures. [ABSTRACT FROM AUTHOR]

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. • Predicting loads on unmeasured wake-affected WT from sensors on an adjacent WT. • Gaussian Process Regression model with Bayesian hyperparameters calibration. • Propagation of uncertain inflow, aerodynamic, layout and fatigue random variables. • Recommended minimal set of input to predict the DEL on a wake-affected WT. • The overall prediction error of DELs of the wake-affected WT is less than 4%. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated a great potential of prestressed strengthening of structures employing iron-based shape memory alloys (Fe-SMAs). A bonded Fe-SMA strengthening solution with partial activation has been proposed. However, an analytical model for assessing the strengthening efficiency was lacking, due to the unique nature of the employed prestressing mechanism involving heating. In this study, a symmetric strengthening model and an asymmetric strengthening model are developed to analyze the prestress level in steel and glass beams and plates strengthened by bonded Fe-SMA strips. The asymmetric strengthening model is then modified to analyze reinforced concrete (RC) beams strengthened by embedded Fe-SMA rebars. Recovery stress at different activation temperatures, the influence of the activation temperature on the adhesive bond, as well as the prestress loss resulting from the deformation of substrate elements and adhesive joints are taken into account. The predicted strains and deflections in the parent structure closely approximate the experimental measurements that appear in current literature. A parametric study and a sensitivity analysis are then conducted to assess the impact of the four influential features on the final prestress level, and their impact is ranked in the following order: recovery stress ≈ Fe-SMA width > activation length > bonded anchorage length. Based on these findings, a design strategy, in line with Eurocode 0, for the bonded/embedded Fe-SMA strengthening system is proposed. Finally, some perspectives on potential areas for future research are offered. [Display omitted] • Two models proposed to analyze structural members strengthened by bonded Fe-SMAs. • Validation conducted on experimentally strengthened steel, glass, and RC members. • Strains and deflections of strengthened members can be predicted with a high accuracy. • The impact of influential features on the final strengthening effect is quantified. • A design guideline for bonded Fe-SMA strengthening is proposed. [ABSTRACT FROM AUTHOR]

Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions. The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-based estimation of the long-run expected maintenance cost per unit time, using monitored run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. We employ the metric as an objective function for optimizing heuristic PdM policies and algorithms' hyperparameters. The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms. • A novel decision-oriented metric for assessing data-driven prognostic algorithms. • The metric is defined in conjunction with a predictive maintenance (PdM) policy. • We investigate and improve certain PdM policies for two common decision settings. • The metric also serves for optimizing PdM policies and algorithms' hyperparameters. • Numerical investigations are performed on the CMAPSS run-to-failure dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This work is the first systematic study on the static behavior of adhesively-bonded Fe-SMA-to-steel joints in applications adopting iron-based Shape Memory Alloys (SMAs). In order to provide a better understanding on the mechanical behavior of the adhesively bonded joint, an experimental campaign was established, involving 24 lap-shear tests in a displacement-controlled loading regime. The test series includes two types of Fe-SMAs (non-prestrained and prestrained), three types of adhesives (SikaDur 30, Araldite 2015, and SikaPower 1277), and three different thickness values (0.5, 1, and 2 mm) for the adhesive. A digital image correlation (DIC) technique was employed to measure the full-field displacement and strain, which were then used to infer the shear behavior. The mechanical behavior was analyzed on the basis of the experimentally derived load–displacement curves, the shear stress profiles along the bond line, and the bond–slip curves; three stages were observed during the loading process of a bonded joint: (i) a linear stage, (ii) a damage accumulation stage, and (iii) a debonding propagation stage. The test results indicate that a more ductile adhesive or a thicker adhesive layer possess a higher fracture energy, leading to a greater bond capacity. The results were also compared against those from lap-shear tests on carbon fiber reinforced polymer (CFRP) bonded joints. It is found that an Fe-SMA bond and a CFRP bond behave similarly when a linear adhesive is utilized; a nonlinear adhesive, however, results in significant mechanical differences between the two bonded joints, which merit individual analysis. • Debonding behavior of Fe-SMA-to-steel bonded joints, comprising linear and nonlinear adhesives. • The adherent type (Fe-SMA prestrained or not) has an impact on the bond behavior. • The adhesive type and adhesive thickness affect the bond behavior as well. • A comparison between the Fe-SMA bond and CFRP bond is delivered. [ABSTRACT FROM AUTHOR]

The strengthening and repair of existing infrastructures, a large portion of which is comprised of steel structures, is essential for sustainable material use and energy resource management. Bonded strengthening using Carbon Fiber Reinforced Polymers (CFRPs) offers great potential toward a sustainable infrastructure management. In establishing CFRP retrofitting as a reliable solution for steel strengthening, a solid understanding of the mechanical behavior of the CFRP-to-steel bonded joints is essential. Given the variability in the evidence attained by experiments, in this study, we tackle this challenge from an uncertainty quantification perspective by proposing a model based on Polynomial Chaos Expansion (PCE) to predict the load capacity of the bonded joints. A stochastic bond–slip model, featuring a parsimonious representation with one deterministic coefficient and one probabilistic coefficient, is further proposed. A Monte-Carlo (MC) simulation is used to demonstrate the efficacy of the bond–slip model in predicting the mechanical behavior such as load–displacement behavior, shear stress profile, and effective bond length of strengthened specimens. Results are compared with existing deterministic models. • A PCE model predicts the bond capacity of CFRP-to-steel bonded joints. • A bond–slip model with MC simulation predicts possible range of mechanical behavior. • Quantification of the uncertainties in the mechanical behavior of the bonded joints. • The models are data-driven, with strong dependency on existing experimental data. [ABSTRACT FROM AUTHOR]

The KDamper absorber is a recently proposed innovative vibration mitigation solution based essentially on the optimal combination of appropriate stiffness and damping elements, which include a negative stiffness element. Previous studies have developed the mathematical framework of the system, as well as design and optimization algorithms that account for the negative stiffness effect on specific use cases and further discuss limitations. The KDamper has been implemented numerically and analytically for seismic protection of bridges, buildings, wind turbines, and noise mitigation panels, demonstrating its effectiveness in vibration attenuation. In this work, we offer for the first time an assessment and experimental validation of a modified KDamper concept, namely the extended KDamper mechanism (EKD). An optimization procedure is used for its design and the EKD is tested under horizontal harmonic and seismic shaking. The results confirm the vibration attenuation efficacy of the investigated system and validate previous numerical and analytical studies, while further corroborated by a suitably designed numerical (finite element) model. This prototype device serves as a proof of concept for the KDamper absorber and showcases its benefits as a robust vibration attenuation system. [Display omitted] • A novel extended KDamper (EKD) prototype is designed and experimentally tested. • The EKD comprises a feasible setup avoiding complex geometrical configurations. • No additional damping elements are required for the efficiency of the EKD • Comparison between the EKD and other competing devices illustrates its superiority [ABSTRACT FROM AUTHOR]

• The Value of Information and the Value of Structural Health Monitoring are quantified. • Step-wise and long-term definitions of information metrics are provided and studied. • POMDPs are shown to inherently leverage the notion of VoI for guiding observations. • It is proven that under a POMDP policy information gains are necessarily non-negative. • The effect of the observation precision on the information metrics is examined. Efficient integration of uncertain observations with decision-making optimization is key for prescribing informed intervention actions, able to preserve structural safety of deteriorating engineering systems. To this end, it is necessary that scheduling of inspection and monitoring strategies be objectively performed on the basis of their expected value-based gains that, among others, reflect quantitative metrics such as the Value of Information (VoI) and the Value of Structural Health Monitoring (VoSHM). In this work, we introduce and study the theoretical and computational foundations of the above metrics within the context of Partially Observable Markov Decision Processes (POMDPs), thus alluding to a broad class of decision-making problems of partially observable stochastic deteriorating environments that can be modeled as POMDPs. Step-wise and life-cycle VoI and VoSHM definitions are devised and their bounds are analyzed as per the properties stemming from the Bellman equation and the resulting optimal value function. It is shown that a POMDP policy inherently leverages the notion of VoI to guide observational actions in an optimal way at every decision step, and that the permanent or intermittent information provided by SHM or inspection visits, respectively, can only improve the cost of this policy in the long-term, something that is not necessarily true under locally optimal policies, typically adopted in decision-making of structures and infrastructure. POMDP solutions are derived based on point-based value iteration methods, and the various definitions are quantified in stationary and non-stationary deteriorating environments, with both infinite and finite planning horizons, featuring single- or multi-component engineering systems. [ABSTRACT FROM AUTHOR]

Accurate post-earthquake damage evaluation of real-world structures is essential to ensure the safe operation of buildings. To this end, we propose a damage evaluation method for earthquake-excited structures relying on the hysteresis curve reconstruction approach. Central to this method is the use of an equivalent single-degree of freedom (ESDOF) system, designed to model structural overall hysteresis and to facilitate an acceleration-based Damage Indicator (DI). Differently from the previously investigated hybrid damage index, a data-driven variant of this DI, configured for reduced reliance on model information and enhanced computational efficiency, is here introduced. This DI is integrated into a framework for assessing structural damage and seismic performance levels, leveraging Seismic Structural Health Monitoring data. The reliability and robustness of the DI with respect to earthquake excitation of different characteristics and for a varying number of floors is assessed by adopting a simulated five-degree-of-freedom degradation model. A large-scale RC frame shaking table test is employed for a comprehensive evaluation of the proposed scheme, allowing to illustration of the damage assessment and the damage state evaluation performance of the proposed data-driven DI. Though the utilization of the ESDOF concept precludes the evaluation of the intensity and location of structural damage, the damage state evaluation results may still provide effective physical information besides the DI values. The relatively low requirements for prior model information and nonlinear behavior render the proposed DI applicable to real-world implementation. This promising characteristic may consequently facilitate the rapid post-earthquake decision-making process. • A framework for structural damage quantification and damage state evaluation leveraging SHM data is proposed. • A large-scale RC frame shaking table test is employed to illustrate the damage evaluation performance for the proposed DI. • The relationship between DI value and damage state is established to determine thresholds for rapid post-earthquake decisions. [ABSTRACT FROM AUTHOR]

• Aggregate the output from multiple numerical simulators to reduce model uncertainty. • Variational Bayesian Gaussian mixture clustering to derive cluster weights. • Local cluster-weighted Bagging the output from the individual simulators. • Tangible reduction in generalization error and the 95% CI of the estimator. • Demonstration using 10 Finite Elements numerical simulators of rotor blade. In the present work, we consider the problem of combining the output from multiple stochastic computer simulators to make inference on a quantity of interest, as a means of reducing the inherent model-form uncertainty in the absence of any measurements. In most real-world situations, judging an individual stochastic simulator to be the "best" for any given point in the input space is highly doubtful. Thus, making inference by relying on the so-deemed best simulator may not be adequate, especially when the sampled data is limited. To this end, we propose an ensemble learning method based on local Clustering and bootstrap aggregation (Bagging), which rather than treating the stochastic predictions of the simulators as competing individual information sources, treats those as part of an ensemble, thus diversifying the hypothesis space. We call the proposed method: unsupervised local cluster-weighted bootstrap aggregation. Variational Bayesian Gaussian mixture clustering is the first step in this ensemble learning approach for discriminating the outputs, and deriving the probability map (weights) of the clustered simulators output. Clustering is performed on the stochastic output corresponding to the binned input space. Performing the clustering independently and deriving the probability map for each local region of the binned input space is a novelty that guarantees an adaptive solution, whereby certain simulators are potentially more fitting than others in corresponding regions of the input space. The second step consists in a local cluster-weighted Bootstrap Aggregation, which serves the purpose of weighted combination of the clustered ensemble of outputs from the individual simulators. Based on simulations, we demonstrate how the input bin size, sample size, output dispersion and level of agreement amongst the simulators affect the performance of the proposed method. We compare the unsupervised local cluster-weighted bootstrap aggregation method to classical Bagging, Bayesian Model Averaging and Stacking of predictive distributions. Finally, we demonstrate the method by evaluating the fatigue damage equivalent load on a wind turbine blade, using 10 finite element based simulators. The results point to the need for practitioners to consider this as a useful method, when model-form uncertainty is of concern and when output from multiple stochastic simulators are available. [ABSTRACT FROM AUTHOR]

The principle of influencing oscillation amplitudes of a primary system via secondary attachments can be enhanced with inclusion of nonlinear mechanisms towards energy absorption. This work exploits such a scheme, termed the NegSV device, which harnesses a geometrically nonlinear mechanism to create a negative stiffness system for vibration attenuation. The suggested device succeeds in shifting the stiffness characteristics of the primary system and, therefore, alters the overall dynamics without additional mass requirements. The top part of a structure can act as a resonator with respect to the lower by matching the respective resonant frequencies and thus directing energy at specified locations. Analytical solutions demonstrate the improvement of the dynamic performance of a system, which is modified with attachment of the proposed device. Physical testing on a 3 dimensional frame structure is further performed, via shaking table tests, with the proposed nonlinear mechanism mounted at the top storey of the experimental structure. The experiment reveals reduction of acceleration and inter-storey drift response at all levels below the retrofit, while the requirement of increased top-storey drifts is identified. Nonlinear finite element analyses are finally performed on a detailed numerical model, which demonstrate agreement with the experimental measurements and are exploited for additional improvement of the mechanism's design. The proposed nonlinear device shows significant potential in attenuating structural vibration, while further offering the benefit of ease of installation. [Display omitted] • The top part of a building can act as a resonator with respect to the lower part. • Negative stiffness can be explored for adjusting the stiffness of a building's storey. • Design of a device that can be conveniently mounted to new or existing buildings. • Reduced dynamic response under seismic input in use of the protective device. [ABSTRACT FROM AUTHOR]

Highlights • OMA modal identification through ambient vibration testing and FEM modelization of a historic centennial reinforced concrete bridge. • Automated Latin Hypercube Sampling implementation. • FEM model parametrization based on Sensitivity Analysis with analytical derivatives. • Global optimization procedure for model updating. • Effective estimation of modal bridge characteristics after model updating. Abstract In this paper, a self-implemented model updating global optimization procedure is successfully applied to a remarkable case study concerning a historic centennial Reinforced Concrete (RC) bridge with parabolic arches, based on recorded experimental vibrational data and arising identification of modal properties. In order to boost the degree of confidence and robustness of the developed model updating procedure, appropriate computational strategies are proposed at the level of both Sensitivity Analysis (SA) and global optimization. In particular, Latin Hypercube Sampling (LHS) is employed in drawing up both strategies, as a systematic automated way to determine appropriate multi-start sets of initiation points, optimally distributed throughout the parametric domain. The procedure involves a gradient-based method and proposes an interaction algorithm between mechanical FEM solver and numerical computing environment. Moreover, the gradient of the objective function involved in the model updating is analytically derived, instead of by often-used Finite Differences (FD), toward better accuracy and computational efficiency. Comprehensive updating results starting from a first FEM base model are achieved, for the considered case study, and show that the relative eigenfrequency and mode shape estimations are considerably improved, for all the structural modes accounted for within the updating process, with a very good final matching between experimentally extracted and FEM modelled modal properties. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) describes a process for inferring quantifiable metrics of structural condition, which can serve as input to support decisions on the operation and maintenance of infrastructure assets. Given the long lifespan of critical structures, this problem can be cast as a sequential decision making problem over prescribed horizons. Partially Observable Markov Decision Processes (POMDPs) offer a formal framework to solve the underlying optimal planning task. However, two issues can undermine the POMDP solutions. Firstly, the need for a model that can adequately describe the evolution of the structural condition under deterioration or corrective actions and, secondly, the non-trivial task of recovery of the observation process parameters from available monitoring data. Despite these potential challenges, the adopted POMDP models do not typically account for uncertainty on model parameters, leading to solutions which can be unrealistically confident. In this work, we address both key issues. We present a framework to estimate POMDP transition and observation model parameters directly from available data, via Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM) conditioned on actions. The MCMC inference estimates distributions of the involved model parameters. We then form and solve the POMDP problem by exploiting the inferred distributions, to derive solutions that are robust to model uncertainty. We successfully apply our approach on maintenance planning for railway track assets on the basis of a "fractal value" indicator, which is computed from actual railway monitoring data. • A framework for POMDP inference and robust solution is proposed. • Transition and observation models are estimated via MCMC sampling. • Solutions merged with Bayesian decision making for robustness to model uncertainty. • A real-world problem of railway optimal maintenance with continuous data is solved. [ABSTRACT FROM AUTHOR]

This paper presents findings from a series of experimental investigations on the long-term resistance of the gradient anchorage, a purely epoxy-based non-mechanical anchoring technique for prestressed carbon fiber reinforced polymer (CFRP) strips, after exposure to accelerated ageing conditions. A segment of the complete anchorage solution is simulated by anchoring a prestressed CFRP strip to a concrete block. A custom-designed clamping system on one end allows for maintaining the prestress force constant during exposure to accelerated ageing. Upon such an exposure, the specimens are tested in a conventional lap-shear test setup. Several exposure scenarios and their effect on the residual load carrying capacity are considered, namely the effect of carbonated concrete (CC), freeze-thaw cycles (FTC), as well as their combination. Forces and full-field displacements, the latter by means of a 3D-DIC system, were measured during the prestress-force-release and lap-shear tests. Results indicate a higher anchorage resistance for CC compared to the reference specimens. For both groups a debonding in the concrete substrate was observed. Specimens subjected to FTC exposure suffer from a significant reduction in residual anchorage resistance, as well as a shift in failure mode from a concrete substrate dominated to an epoxy/concrete interface failure. The current knowledge on the residual resistance of gradient anchorage has to be adapted accordingly. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) enables the rapid assessment of structural integrity in the immediate aftermath of strong ground motions. Data-driven techniques, often relying on damage-sensitive features (DSFs) derived from vibration monitoring, may be deployed to attribute a specific damage class to a structure. In practical applications, individual features are sensitive to specific levels of damage, and therefore combining multiple DSFs is required to formulate robust damage indicators. However, the combination of DSFs typically involves empirical thresholds that are often structure-specific and hinder generalization to different structural configurations. This work evaluates the predictive performance of a large ensemble of DSFs, computed on an extensive dataset of nonlinear simulations of frame structures with varying geometrical and material configurations. Gradient-boosted decision trees and convolutional neural networks are deployed to fuse multiple DSFs into damage classifiers, improving the predictive accuracy compared to best-practice methods and individual DSFs. A Domain Adversarial Neural Network (DANN) architecture enables the transfer of knowledge obtained from numerical simulations to real data from a large-scale shake-table test. After exposure to limited data, exclusively from the healthy state, the DANN framework yields satisfactory performance in predicting unseen damage states in the experimental data. The results demonstrate the potential of DANN in transferring knowledge from simulations to real-world monitoring applications, where only limited data characterizing exclusively the current, typically healthy, structural state is available. Overall, this work comprises the definition of multiple DSFs, their fusion through ML approaches, and the generalization of the knowledge obtained from simulations to real data through domain adaptation. • Acceleration-based DSFs outperform PGA in damage characterization in masonry buildings. • Fusing DSFs through ML algorithms significantly increases the damage prediction accuracy. • A domain adaptation framework successfully generalized knowledge from simulations to real data. [ABSTRACT FROM AUTHOR]

This work proposes a graded metamaterial-based energy harvester integrating the piezoelectric energy harvesting function targeting low-frequency ambient vibrations (< 100 Hz). The harvester combines a graded metamaterial with beam-like resonators, piezoelectric patches, and a self-powered interface circuit for broadband and high-capability energy harvesting. Firstly, an integrated lumped parameter model is derived from both the mechanical and the electrical sides to determine the power performance of the proposed design. Secondly, thorough numerical simulations are carried out to optimize both the grading profile and wave field amplification, as well as to highlight the effects of spatial-frequency separation and the slow-wave phenomenon on energy harvesting performance and efficiency. Finally, experiments with realistic vibration sources validate the theoretical and numerical results from the mechanical and electrical sides. Particularly, the harvested power of the proposed design yields a five-fold increase with respect to conventional harvesting solutions based on single cantilever harvesters. Our results reveal that by bridging the advantages of graded metamaterials with the design targets of piezoelectric energy harvesting, the proposed design shows significant potential for realizing self-powered Internet of Things devices. • Broadband and high capability design targets are achieved under low frequencies. • The DC power output of the proposed design is evaluated with an integrated model. • Spatial frequency separation and slow waves in the design are analyzed in detail. • A near-milliwatt DC power output is realized with metamaterials for the first time. [ABSTRACT FROM AUTHOR]

A comprehensive numerical study on the performance of a novel nonlinear foundation consisting of alternating layers of lead rubber bearings and concrete slabs is presented in this article. The novel design combines the established load bearing capacity of commercial bearings with wave propagation inhibition by means of Bragg scattering in periodic structures. This solution substantially reduces structural demand under ground motion excitation without compromising performance during operational loading. The performance of the foundation is evaluated through nonlinear response history analyses and it is compared against conventional base isolated alternatives. By explicitly simulating the hysteretic behaviour of lead rubber bearings, we demonstrate that the response complies with the best practice standard of current design codes, as defined for base-isolated buildings. A comparative study exposes the limitations of linear analysis, leading to unrealistic and non-conservative outcomes. The results reflect the superior performance of the novel foundation in reducing base displacements, owing to Bragg scattering, as well as its remarkable robustness to input and soil variability. A sensitivity analysis over the key design parameters provides valuable insights and enables tailoring the foundation to the dynamic characteristics of the supported structure. For the first time, a framework for the assessment of global stability is formulated and deployed, highlighting the importance of considering stability as a performance metric when designing layered periodic foundations. • Practical metafoundation design for low-rise buildings. • Periodic metafoundation with layers of lead rubber bearings and concrete slabs. • Global stability assessment for periodic metafoundations. • Parametric analysis on metafoundation key parameters, soil and source variability. [ABSTRACT FROM AUTHOR]

• Crack detection based on XFEM is extended to the case of plates under dynamic loads. • System identification is used to provide an initial estimate for the crack location. • Modal strain curvatures are used as a damage index. • Accurate crack localization is consistently obtained. 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. [ABSTRACT FROM AUTHOR]