| Nome |
# |
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Simplified modeling of beam vibrations induced by a moving mass by regression analysis, file e0c31c0e-813f-4599-e053-1705fe0aef77
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447
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An optimal sensor placement method for SHM based on Bayesian experimental design and polynomial chaos expansion, file e0c31c0a-236e-4599-e053-1705fe0aef77
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322
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On the relationship between force reduction, loading rate and energy absorption in athletics tracks, file e0c31c0b-1ee2-4599-e053-1705fe0aef77
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320
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Numerical modeling of the interaction of pressurized large diameter gas buried pipelines with normal fault ruptures, file e0c31c0b-9e4d-4599-e053-1705fe0aef77
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282
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Modeling of shock absorption in athletics track surfaces, file e0c31c08-4794-4599-e053-1705fe0aef77
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265
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Modelling the cushioning properties of athletic tracks, file e0c31c0c-a59d-4599-e053-1705fe0aef77
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252
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Reduced order modeling of composite laminates through solid-shell coupling, file e0c31c0b-aa5e-4599-e053-1705fe0aef77
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204
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Smart sensing of damage in flexible plates through MEMS, file e0c31c08-2652-4599-e053-1705fe0aef77
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174
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Model order reduction and domain decomposition strategies for the solution of the dynamic elasto-plastic structural problem, file e0c31c0e-0219-4599-e053-1705fe0aef77
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169
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Cost–benefit optimization of structural health monitoring sensor networks, file e0c31c0d-42fc-4599-e053-1705fe0aef77
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156
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Optimal design of sensor networks for damage detection, file e0c31c0b-e51c-4599-e053-1705fe0aef77
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147
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Coupled domain decomposition–proper orthogonal decomposition methods for the simulation of quasi-brittle fracture processes, file e0c31c0a-29bc-4599-e053-1705fe0aef77
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143
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Micromechanical characterization of polysilicon films through on-chip tests, file e0c31c0a-2490-4599-e053-1705fe0aef77
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133
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Mechanical characterization of polysilicon MEMS: A hybrid TMCMC/POD-kriging approach, file e0c31c0c-2209-4599-e053-1705fe0aef77
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131
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Polysilicon MEMS Sensors: Sensitivity to Sub-Micron Imperfections, file e0c31c0d-6c71-4599-e053-1705fe0aef77
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131
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Resilienza ai cambiamenti climatici, file 3a2616dd-b3d0-4df2-b34b-93041426d7b9
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130
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Assessment of overetch and polysilicon film properties through on-chip tests, file e0c31c09-330a-4599-e053-1705fe0aef77
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129
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Towards Safer Helmets: Characterisation, Modelling and Monitoring, file e0c31c09-ca82-4599-e053-1705fe0aef77
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127
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Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection, file e0c31c12-bcda-4599-e053-1705fe0aef77
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124
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A multiscale approach to the smart deployment of micro-sensors over flexible plates, file e0c31c0a-2bcb-4599-e053-1705fe0aef77
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123
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Assessment of micromechanically-induced uncertainties in the electromechanical response of MEMS devices, file e0c31c0a-2f3f-4599-e053-1705fe0aef77
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119
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Statistical investigation of the mechanical and geometrical properties of polysilicon films through on-chip tests, file e0c31c0b-9910-4599-e053-1705fe0aef77
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119
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A 3D Numerical Model for the Optimization of Running Tracks Performance, file e0c31c09-c987-4599-e053-1705fe0aef77
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111
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Health monitoring of composite structures via MEMS sensor networks: numerical and experimental results, file e0c31c0b-eb43-4599-e053-1705fe0aef77
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109
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Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks, file e0c31c12-bbe3-4599-e053-1705fe0aef77
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105
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Uncertainty quantification of microstructure-governed properties of polysilicon MEMS, file e0c31c0b-74a1-4599-e053-1705fe0aef77
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103
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Damage detection in flexible plates through reduced-order modeling and hybrid particle-Kalman filtering, file e0c31c0a-29b9-4599-e053-1705fe0aef77
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98
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Effect of imperfections due to material heterogeneity on the offset of polysilicon MEMS structures, file e0c31c0e-9993-4599-e053-1705fe0aef77
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97
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Stochastic effects on the dynamics of the resonant structure of a Lorentz force MEMS magnetometer, file e0c31c0e-99a1-4599-e053-1705fe0aef77
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97
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A multiscale approach to the smart deployment of micro-sensors over lightweight structures, file e0c31c0b-6197-4599-e053-1705fe0aef77
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96
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Cost-Benefit Optimization of Sensor Networks for SHM Applications, file e0c31c0b-d2e2-4599-e053-1705fe0aef77
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92
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Estimation of air damping in Out-of-plane comb-drive actuators, file e0c31c0e-6d2a-4599-e053-1705fe0aef77
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86
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Selected Papers from the 5th International Electronic Conference on Sensors and Applications, file e0c31c0f-d461-4599-e053-1705fe0aef77
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84
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Microsystems and Mechanics, file e0c31c0d-9456-4599-e053-1705fe0aef77
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83
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An efficient earth magnetic field MEMS sensor: modeling, experimental results and optimization., file e0c31c0e-0220-4599-e053-1705fe0aef77
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82
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A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors, file e0c31c12-7eac-4599-e053-1705fe0aef77
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81
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Low-order feature extraction technique and unsupervised learning for SHM under high-dimensional data, file e0c31c0e-b57f-4599-e053-1705fe0aef77
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75
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The impacts of different façade types on energy use in residential buildings, file e0c31c11-0f51-4599-e053-1705fe0aef77
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75
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A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks, file da6efbbe-e58b-425b-b734-902578546c34
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73
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Online damage detection in structural systems via dynamic inverse analysis: A recursive Bayesian approach, file e0c31c11-a574-4599-e053-1705fe0aef77
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71
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Experimental assessment of ductile damage in P91 steel at high temperature, file e0c31c0d-aad3-4599-e053-1705fe0aef77
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67
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Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters, file e0c31c11-6485-4599-e053-1705fe0aef77
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65
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A Multi-stage Machine Learning Methodology for Health Monitoring of Largely Unobserved Structures Under Varying Environmental Conditions, file 865d0b07-c5bc-4730-b382-cff4494646d9
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60
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A Hybrid Structural Health Monitoring Approach Based on Reduced-Order Modelling and Deep Learning, file e0c31c11-0d0c-4599-e053-1705fe0aef77
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59
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Fully convolutional networks for structural health monitoring through multivariate time series classification, file e0c31c11-5394-4599-e053-1705fe0aef77
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57
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Investigation of the effectiveness and robustness of a MEMS-based structural health monitoring system for composite laminates, file e0c31c0d-ed6d-4599-e053-1705fe0aef77
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55
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Assessment of the shock adsorption properties of bike helmets: a numerical/experimental approach, file e0ea1b08-5a37-472a-afc4-14bbc93f3012
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53
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A time series autoencoder for load identification via dimensionality reduction of sensor recordings, file e0c31c11-0192-4599-e053-1705fe0aef77
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52
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Big data analytics and structural health monitoring: A statistical pattern recognition-based approach, file e0c31c11-3d5c-4599-e053-1705fe0aef77
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51
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A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS, file e0c31c11-4b3a-4599-e053-1705fe0aef77
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51
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SHM and Efficient Strategies for Reduced-Order Modeling, file e0c31c11-3ce7-4599-e053-1705fe0aef77
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50
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Geometry optimization of a Lorentz force, resonating MEMS magnetometer, file e0c31c0d-2fde-4599-e053-1705fe0aef77
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49
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A numerical study of the pressurized gas pipeline-normal fault interaction problem, file e0c31c09-3381-4599-e053-1705fe0aef77
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48
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Structural Health Monitoring for Condition Assessment Using Efficient Supervised Learning Techniques, file e0c31c11-40c7-4599-e053-1705fe0aef77
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47
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An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) Data, file e0c31c11-2558-4599-e053-1705fe0aef77
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45
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Machine learning-based prediction of the seismic bearing capacity of a shallow strip footing over a void in heterogeneous soils, file e0c31c12-5186-4599-e053-1705fe0aef77
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45
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Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach, file e0c31c11-5864-4599-e053-1705fe0aef77
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44
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A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring, file e0c31c12-57c2-4599-e053-1705fe0aef77
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43
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Towards real-time health monitoring of structural systems via recursive Bayesian filtering and reduced order modelling, file e0c31c0e-c4e2-4599-e053-1705fe0aef77
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39
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PU-FE approach to quasi-brittle fracture, A, file e0c31c0b-fca8-4599-e053-1705fe0aef77
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36
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A Non-Parametric Mixed Learning Technique for Mitigating Environmental Effects on Structural Modal Frequencies, file 6e5132b9-d600-4c36-b75b-19531a539997
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35
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Optimal sensor placement through Bayesian experimental design: effect of measurement error and number of sensors, file e0c31c0a-2e04-4599-e053-1705fe0aef77
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34
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A variational approach to cohesive-damaging crack propagation in a bar, file e0c31c0b-ca4b-4599-e053-1705fe0aef77
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33
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An extended finite element strategy for the analysis of crack growth in damaging concrete structures, file e0c31c0b-e444-4599-e053-1705fe0aef77
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33
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A Deep Learning Approach for Polycrystalline Microstructure-Statistical Property Prediction, file e0c31c12-5dcb-4599-e053-1705fe0aef77
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30
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Cohesive crack propagation in damaging concrete structures discretized by extended finite elements, file e0c31c07-c20a-4599-e053-1705fe0aef77
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28
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A three-scale approach to the numerical simulation of metallic bonding for MEMS packaging., file e0c31c0d-2fe5-4599-e053-1705fe0aef77
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26
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Health monitoring of large‐scale civil structures: An approach based on data partitioning and classical multidimensional scaling, file e0c31c12-6ee4-4599-e053-1705fe0aef77
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22
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Self-adaptive Multi-purpose Modular Origami Structure, file a7c9314b-a103-429e-92cc-a66ae8d5d6c7
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21
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Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics, file e0c31c12-7a01-4599-e053-1705fe0aef77
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21
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Enhanced Bayesian model updating for structural health monitoring via deep learning, file 291ad791-0ebb-45a3-add6-d42a328410ba
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19
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A thermodynamically consistent model for shape-memory ionic polymers, file e0c31c0d-4826-4599-e053-1705fe0aef77
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19
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Dealing with uncertainties in structural damage localization by reduced order modeling and deep learning-based classifiers, file e0c31c12-869d-4599-e053-1705fe0aef77
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19
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Enabling supervised learning in structural health monitoring by simulating damaged structure responses through physics based models, file a55dd2c8-3aed-4d01-83cd-99f22c5796e2
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18
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Two-Scale Deep Learning Model for Polysilicon MEMS Sensors, file e0c31c12-7d05-4599-e053-1705fe0aef77
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18
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Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology, file 0f75c192-b7ca-430c-8273-6346531f0488
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15
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Formulazione variazionale per la frattura coesiva in una barra in trazione, file e0c31c0c-2f5d-4599-e053-1705fe0aef77
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15
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Piezoelectric Ultrasonic Micromotor, file e0c31c12-424d-4599-e053-1705fe0aef77
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15
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A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS, file e0c31c12-57c8-4599-e053-1705fe0aef77
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15
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Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate, file e0c31c12-9e49-4599-e053-1705fe0aef77
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15
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On-Chip Assessment of Scattering in the Response of Si-Based Microdevices, file e0c31c12-57c6-4599-e053-1705fe0aef77
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13
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A Piezo-MEMS Device for Fatigue Testing of Thin Metal Layers, file e0c31c12-3dbb-4599-e053-1705fe0aef77
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11
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Learning the Link between Architectural Form and Structural Efficiency: A Supervised Machine Learning Approach, file e0c31c12-9b87-4599-e053-1705fe0aef77
|
10
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A Microfluidic Device Based on Standing Surface Acoustic Waves for Sorting and Trapping Microparticles, file 571fa742-361c-4f49-8bde-6a823e5c03ab
|
9
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|
Attention Mechanism-Driven Sensor Placement Strategy for Structural Health Monitoring, file 6ea3cdb1-00df-472c-98d2-b9ff0f0a3da2
|
9
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Classification of the Structural Behavior of Tall Buildings with a Diagrid Structure: A Machine Learning-Based Approach, file 096c9522-7551-4893-b47f-6b0045737e3e
|
8
|
|
A digital twin framework for civil engineering structures, file 45927eb4-202c-451e-9bec-9aaf787ba984
|
8
|
|
An MCMC approach powered by a multi-fidelity deep neural network surrogate for damage localization in civil structures, file e0c31c12-93ae-4599-e053-1705fe0aef77
|
8
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|
Exploring structural sustainability of tall buildings subject to seismic loads, file 32ef5d07-ae3c-4683-8ee7-27262ce087f0
|
7
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|
PHOTOVOLTAIC SYSTEM FOR AN AGRI VOLTAIC FARM, COMPRISING A PROTECTIVE COVER, file 72904dbc-b1c1-40c1-b12c-6184dfebea92
|
7
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|
On-Chip Tests for the Characterization of the Mechanical Strength of Polysilicon †, file 959a93e1-a763-4f6f-b932-ca40848ac580
|
7
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A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring, file cce2cfeb-fb51-4ec3-8b49-e97ede43f90e
|
7
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A deep learning approach to metric-based damage localization in structural health monitoring, file e0c31c12-57cf-4599-e053-1705fe0aef77
|
7
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|
A Comparative Study on Structural Displacement Prediction by Kernelized Regressors under Limited Training Data, file 01e8fcfc-0b85-425d-89a8-1710e4070b37
|
4
|
|
Uncertainty Quantification at the Microscale: A Data-Driven Multi-Scale Approach, file 22cd3988-b82b-41bc-8b23-ca2d4226bc88
|
4
|
|
AI-assisted generative workflow for the early-stage design of sustainable tall buildings based on their structural behaviour, file 833191ed-c52e-4090-9524-ff541b855685
|
4
|
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Regression Tree Ensemble to Forecast Thermally Induced Responses of Long-Span Bridges, file de4e00c2-83ce-4e5a-9c3d-46f244af093e
|
4
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Investigation of computational and accuracy issues in POD-based reduced order modeling of dynamic structural systems, file e0c31c08-1334-4599-e053-1705fe0aef77
|
4
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Structural integrity assessment of a pipeline subjected to an underwater explosion, file e0c31c09-3f6f-4599-e053-1705fe0aef77
|
4
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Adaptive POD-based reduced order modeling and identification of nonlinear structural systems, file e0c31c0a-2572-4599-e053-1705fe0aef77
|
4
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| Totale |
7.371 |