1,720,966 research outputs found
A multi-objective optimization approach for FE model updating based on a selection criterion of the preferred Pareto-optimal solution
No full textMulti-objectives optimization problems are often solved constructing the Pareto front and applying a decision-making strategy to select the preferred solution among the Pareto-optimal solutions. With the aim to reduce the computational effort in multi-objective optimization problems, this paper presents a procedure for the direct evaluation of the preferred updated model, without the need to evaluate the whole Pareto front. For this purpose, the objective function to minimize is defined as the distance between a candidate point and the equilibrium point in the objective function space. The choice of the criterion of the minimum distance from the equilibrium point comes from a preliminary study carried out to assess the performances of different selection criteria. The robustness and the efficiency of the proposed procedure are assessed through the comparison with the results obtained from the estimation of the Pareto-optimal solutions and the subsequent selection of the preferred one for two numerical case studies. The proposed procedure is finally applied to the calibration of a complex FE model with respect to experimental modal data. Results show that the proposed procedure is effective and considerably reduces the computational effort. Moreover, the procedure is able to directly estimate the optimal weighting factor that allows to know the relative importance between the selected objectives and can be used to solve the multi-objective optimization with the weighed sum method
Bayesian and deterministic surrogate-assisted approaches for model updating of historical masonry towers
No full textMulti-sensor and Multi-frequency Data Fusion for Structural Health Monitoring
No full textThe increasing need to evaluate the health state of existing bridges has pushed the researchers towards the study and development of innovative monitoring approaches. Among these, the high frequency GNSS (Global Navigation Satellite Systems) receivers have the potential to be a valuable support for the monitoring of structural displacement. Displacement data obtained from GNSS receivers can be combined and integrated with data measured from other sensors according to data fusion techniques in order to achieve a deeper knowledge of the structural behavior. In this context, the present paper investigates the potential of data fusion for the structural health monitoring by combining GNSS data with measures acquired with a traditional accelerometer-based monitoring system. The adopted data fusion approach is based on the Kalman filter. Structural displacements can be estimated from measured accelerations through a double integration procedure which, however, can introduce non-removable errors. Displacements measured by the GNSS receiver, although acquired with sampling rates lower than those of traditional monitoring systems, can be employed to adjust the post processed displacements and remove the uncertainties introduced with the integration procedure. Furthermore, the integration of measured accelerations and GNSS data holds the potential to identify residual displacements, which are often challenging to detect through acceleration post-processing alone. The effectiveness of this data fusion approach is examined with reference to the case study of a steel footbridge
A computationally efficient procedure for calibrating model parameters of multiple specimens
Full text linkParameter estimation and uncertainty quantification of a fiber-reinforced concrete model by means of a multi-level Bayesian approach
Full text linkThe paper presents a procedure for the stochastic calibration of a cracked hinge model on the basis of an extensive experimental campaign performed on a large group of nominally identical fiber-reinforced specimens. The calibration is carried out in a multi-level Bayesian framework that allows to quantify and separate several uncertainty contributions affecting model parameters. Indeed, the variability in the experimental response for nominally identical specimens due to the material heterogeneity represents a significant uncertainty contribution as well as model error. The former can be quantified at the hyper-parameter level of the multi-level framework. The presented results highlight the good agreement of the numerical predictions with the experimental data and the superior performance of the multi-level framework compared to that of the classical single-level framework. We also perform analyses to explore the impact of the prior parameter model conditioned on hyper-parameters and assess the minimum number of specimen datasets needed to quantify the inherent variability of model parameters
Mitigation of model error effects in neural network-based structural damage detection
Full text linkThis paper proposes a damage detection procedure based on neural networks that is able to account for the model error in the network training. Vibration-based damage detection procedures relied on machine learning techniques hold great promises for the identification of structural damage thanks to their efficiency even in presence of noise-corrupted data. However, it is rarely possible in the context of civil engineering to have large amount of data related to the damaged condition of a structure to train a neural network. Numerical models are then necessary to simulate damaged scenarios. However, even if a finite element model is accurately calibrated, experimental results and model predictions will never exactly match and their difference represents the model error. Being the neural network tested and trained with respect to the data generated from the numerical model, the model error can significantly compromise the effectiveness of the damage detection procedure. The paper presents a procedure aimed at mitigating the effect of model errors when using models associated to the neural network. The proposed procedure is applied to two case studies, namely a numerical case represented by a steel railway bridge and a real structure. The real case study is a steel braced frame widely adopted as a benchmark structure for structural health monitoring purposes. Although in the first case the procedure is carried out considering simulated data, we have taken into account some key aspects to make results representative of real applications, namely the stochastic modelling of measurement errors and the use of two different numerical models to account for the model error. Different networks are investigated that stand out for the preprocessing of the dynamic features given as input. Results show the importance of accounting for the model error in the network calibration to efficiently identify damage
Vision-based approach for the static and dynamic monitoring of bridges
Full text linkStructural Health Monitoring (SHM) is one of the main approaches to deal with damage identification in existing bridges. Static information together with structure modal properties allow to prevent collapses, detect damage also in the early stage, and plan maintenance works based on the bridge condition. Measurement systems are traditionally composed of a network of sensors directly installed on the structure. Despite the large diffusion of these systems, the expensive and time-consuming installation of sensors and acquisition system makes their use not always feasible. A promising approach for the characterization of bridge dynamic behavior is represented by computer vision-based techniques, which require the sole installation of one or more cameras outside the structure, along with some targets on it when necessary. This approach is totally non-invasive, low-cost and enables the direct measurement of structural displacements, providing useful and direct information about the operational conditions and possible permanent deformations. With the aim of investigating the potential of vision-based techniques for the dynamic monitoring of structures, this paper presents preliminary results of dynamic tests performed on a steel footbridge. Structural vibrations caused by a jumping pedestrian were measured from a camera placed at the riverbed as well as by an accelerometer-based monitoring system installed for validation purposes. The post-processing of video recordings is here presented and discussed, with particular emphasis on the impact of target shape and camera shaking
Surrogate-based bayesian model updating of a historical masonry tower
Full text linkThis paper presents the surrogate-based Bayesian model updating of a historical masonry bell tower. The finite element model of the structure is updated on the basis of the modal properties experimentally identified thanks to a vibration test. In a general context, model updating results are highly affected by several uncertainties, regarding both the experimental measures and the model. Stochastic approaches to model updating, as the one based on Bayes' theorem, enable to quantify the uncertainties associated to the updated parameters and, consequently, to increase the reliability of the identification. The major drawback of Bayesian model updating is the high computational effort requested to compute the posterior distribution of parameters. For this reason, the paper proposes to integrate the classical procedure with a surrogate model. A Gaussian surrogate is employed for the approximation of the posterior distribution of parameters and the performances of the proposed method are compared to those of an Bayesian numerical method proposed in literature
Dynamic Monitoring of a Steel Footbridge Based on Computer Vision Techniques
No full textThe field of Structural Health Monitoring (SHM) aims to manage the built heritage, buildings and infrastructures, to first and foremost avoid collapse, detect early sign of damage and plan maintenance. As regards bridges and footbridges, structural health conditions are tightly coupled with modal properties, attainable from structural vibrations. The assessment of vibrations is traditionally obtained with measuring systems composed of sensors directly installed on the structure. Despite the large diffusion of these systems, their use may be unfeasible due to the necessity of an expensive and time-consuming installation of the acquisition system. A promising approach to characterize the footbridge dynamic behavior is represented by computer vision-based techniques, that require the installation of only one or more cameras together with, if necessary, some targets on the monitored structure. The vision-based approach is totally non-invasive, low-cost and enables the direct measurement of structural displacements. The aim of the present paper is to investigate the potential of vision-based techniques for the dynamic identification of structures, analyzing some preliminary results of dynamic tests performed on a steel footbridge. Structural vibrations caused by a jumping pedestrian were acquired from a camera as well as by an accelerometer-based monitoring system installed for comparison purposes. The paper presents and discusses the post-processing of the video frames with particular emphasis on vertical dynamic movements related to bending and torsional modes, comparing vision-based results with data obtained from accelerometers
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