A Genetic Algorithm Procedure for the Automatic Updating of FEM Based on Ambient Vibration Tests

Bianconi, Francesca; Salachoris, Georgios Panagiotis; Clementi, Francesco; Lenci, Stefano

doi:10.3390/s20113315

Cited by 53 publications

(18 citation statements)

References 41 publications

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“…The optimization problem can be solved using continuous design variables

accounting for the location of the sensors over the physical domain of the structure or discrete design variables

accounting for the discrete locations (e.g., DOF at nodes for placing displacement/acceleration sensors or Gauss integration points for placing strains sensors in a finite element mesh). Global optimization algorithms [ 87 , 88 ] as well as stochastic optimization algorithms, such as CMA-ES [ 89 ] and genetic algorithms [ 45 , 90 , 91 , 92 , 93 ] can be employed in order to avoid premature convergence to a local optimum. Alternative heuristic forward and backward sequential sensor placement (FSSP/BSSP) algorithms [ 54 , 57 ] are effective in solving the optimization problem.…”

Section: Optimal Sensor Placement Formulationmentioning

confidence: 99%

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Tulay

Papadimitriou

2021

Sensors

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A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

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“…The optimization problem can be solved using continuous design variables

accounting for the location of the sensors over the physical domain of the structure or discrete design variables

Section: Optimal Sensor Placement Formulationmentioning

confidence: 99%

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Tulay

Papadimitriou

2021

Sensors

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“…RM, as described in Residual Minimization, is suitable for tasks with low systematic bias and low magnitude of uncertainty using models with medium complexity (Behmanesh and Moaveni 2016). While medium complexity models are computationally more expensive than low complexity models, efficient application of RM for model updating is possible using adaptive sampling methods (Bianconi et al, 2020).…”

Section: Medium Model Complexitymentioning

confidence: 99%

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Pai¹,

Smith

2022

Front. Built Environ.

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With increasing urbanization and depleting reserves of raw materials for construction, sustainable management of existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge of infrastructure behavior and improve engineering decision making for asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) and error-domain model falsification (EDMF) have been proposed to interpret monitoring data and support asset management. Application of these methodologies requires approximations and assumptions related to model class, model complexity and uncertainty estimations, which ultimately affect the accuracy of data interpretation and subsequent decision making. This paper introduces methodology maps in order to provide guidance for appropriate use of these methodologies. The development of these maps is supported by in-house evaluations of nineteen full-scale cases since 2016 and a two-decade assessment of applications of model-based methodologies. Nineteen full-scale studies include structural identification, fatigue-life assessment, post-seismic risk assessment and geotechnical-excavation risk quantification. In some cases, much, previously unknown, reserve capacity has been quantified. RM and BMU may be useful for model-based data interpretation when uncertainty assumptions and computational constraints are satisfied. EDMF is a special implementation of BMU. It is more compatible with usual uncertainty characteristics, the nature of typically available engineering knowledge and infrastructure evaluation concepts than other methodologies. EDMF is most applicable to contexts of high magnitudes of uncertainties, including significant levels of model bias and other sources of systematic uncertainty. EDMF also provides additional practical advantages due to its ease of use and flexibility when information changes. In this paper, such observations have been leveraged to develop methodology maps. These maps guide users when selecting appropriate methodologies to interpret monitoring information through reference to uncertainty conditions and computational constraints. This improves asset-management decision making. These maps are thus expected to lead to lower maintenance costs and more sustainable infrastructure compared with current practice.

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“…Once the survey is obtained, the linear parameters of the used materials are varied to minimize the difference between experimental and numerical results. Most of the studies in literature on this topic use manual calibrations, even thought the researchers have been heading towards its automatization over the past few years (Bianconi et al, 2020)…”

Section: Structural Health Monitoring Of Historical Structuresmentioning

confidence: 99%

Point Cloud Exploitation for Structural Modeling and Analysis: A Reliable Workflow

Lucidi

Giordano

Clementi

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The digitization and geometric knowledge of the historical built heritage is currently based on point cloud, that rarely or only partially is used as digital twin for structural analysis. The present work deals with historical artefacts survey, with particular reference to masonry structures, aimed to their structural analysis and assessment. In detail, the study proposes a methodology capable of employing semi-directly the original data obtained from the 3D digital survey for the generation of a Finite Element Model (FEM), used for structural analysis of masonry buildings.The methodology described presents a reliable workflow with twofold purpose: the improvement of the transformation process of the point cloud in solid and subsequently obtain a high-quality and detailed model for structural analyses.Through the application of the methodology to a case study, the method consistency was assessed, regarding the smoothness of the whole procedure and the dynamic characterization of the Finite Element Model. The main improvement in respect with similar or our previous workflows is obtained by the introduction of the retopology in data processing, allowing the transformation of the raw data into a solid model with optimal balancing between Level of Detail (LOD) and computational weight. Another significant aspect of the optimized process is undoubtedly the possibility of faithfully respecting the semantics of the structure, leading to the discretization of the model into different parts depending on the materials. This work may represent an excellent reference for the study of masonry artefacts belonging to the existing historical heritage, starting from surveys and with the purpose to structural and seismic evaluations, in the general framework of knowledge-based preservation of heritage.

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A Genetic Algorithm Procedure for the Automatic Updating of FEM Based on Ambient Vibration Tests

Cited by 53 publications

References 41 publications

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

Point Cloud Exploitation for Structural Modeling and Analysis: A Reliable Workflow

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