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Information on didactic, research and institutional assignments on this page are certified by the University; more information, prepared by the professor, are available on the personal web page and in the curriculum vitae indicated on this webpage.
Information on professor
ProfessorMariani Stefano
QualificationAssociate professor full time
Belonging DepartmentDipartimento di Ingegneria Civile e Ambientale
Scientific-Disciplinary SectorICAR/08 - Structural Mechanics
Curriculum VitaeDownload CV (221.62Kb - 14/10/2019)

Professor's office hours
Dipartimento di Ingegneria Civile e Ambientale1 (edificio 4)100ThursdayFrom 14:30
To 17:00
Professor's personal website--

Data source: RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano

List of publications and reserach products for the year 2022 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Contributions on scientific books
Combined Model Order Reduction Techniques and Artificial Neural Network for Data Assimilation and Damage Detection in Structures (Show >>)
Mechanics of Microsystems: A Recent Journey in a Fascinating Branch of Mechanics (Show >>)
Conference proceedings
A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring (Show >>)
Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate (Show >>)
Two-Scale Deep Learning Model for Polysilicon MEMS Sensors (Show >>)
Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics (Show >>)
Journal Articles
Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology (Show >>)
SHM under varying environmental conditions: an approach based on model order reduction and deep learning (Show >>)
Structural health monitoring of civil structures: A diagnostic framework powered by deep metric learning (Show >>)

List of publications and reserach products for the year 2021 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS (Show >>)
A Piezo-MEMS Device for Fatigue Testing of Thin Metal Layers (Show >>)
A deep learning approach to metric-based damage localization in structural health monitoring (Show >>)
A physics-informed neural network approach to stochastic homogenization of polycrystalline materials (Show >>)
A two-scale multi-physics deep learning model for smart MEMS sensors (Show >>)
An MCMC approach powered by a multi-fidelity deep neural network surrogate for damage localization in civil structures (Show >>)
An on-chip MEMS testing device for uncertainty quantification at the microscale (Show >>)
Assessment of the seismic bearing capacity of strip footings over a void in heterogeneous soils: a Machine Learning-based approach (Show >>)
Dealing with uncertainties in structural damage localization by reduced order modeling and deep learning-based classifiers (Show >>)
On-Chip Assessment of Scattering in the Response of Si-Based Microdevices (Show >>)
Piezoelectric Ultrasonic Micromotor (Show >>)
Stochastic Homogenization and Uncertainty Quantification: A Data-Driven Approach (Show >>)
Contributions on scientific books
Early Damage Detection for Partially Observed Structures with an Autoregressive Spectrum and Distance-Based Methodology (Show >>)
Conference proceedings
A Deep Learning Approach for Polycrystalline Microstructure-Statistical Property Prediction (Show >>)
A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS (Show >>)
Learning the Link between Architectural Form and Structural Efficiency: A Supervised Machine Learning Approach (Show >>)
Parametric reduced order modelling and deep learning to accomplish pattern recognition and regression tasks (Show >>)
SHM and Efficient Strategies for Reduced-Order Modeling (Show >>)
Journal Articles
A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors (Show >>)
An autoencoder-based deep learning approach for load identification in structural dynamics (Show >>)
Health monitoring of large‐scale civil structures: An approach based on data partitioning and classical multidimensional scaling (Show >>)
Machine learning-based prediction of the seismic bearing capacity of a shallow strip footing over a void in heterogeneous soils (Show >>)
Online structural health monitoring by model order reduction and deep learning algorithms (Show >>)

List of publications and reserach products for the year 2020 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
A Hybrid Structural Health Monitoring Approach Based on Reduced-Order Modelling and Deep Learning (Show >>)
A time series autoencoder for load identification via dimensionality reduction of sensor recordings (Show >>)
An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) Data (Show >>)
Health Monitoring of Flexible Structures Via Surface-mounted Microsensors: Network Optimization and Damage Detection (Show >>)
Low Cost Self-assembling Kinetic façade system for Shading and Water harvesting (Show >>)
Mechanical Characterization of Polysilicon MEMS Devices: A Stochastic, Deep Learning-based Approach (Show >>)
Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach (Show >>)
The impacts of different façade types on energy use in residential buildings (Show >>)
Editorship of scientific books
Selected Papers from the 5th International Electronic Conference on Sensors and Applications (Show >>)
Journal Articles
Assessment of the shock adsorption properties of bike helmets: a numerical/experimental approach (Show >>)
Big data analytics and structural health monitoring: A statistical pattern recognition-based approach (Show >>)
Combined effects of temperature and humidity on the mechanical properties of polyurethane foams (Show >>)
Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection (Show >>)
Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks (Show >>)
Fully convolutional networks for structural health monitoring through multivariate time series classification (Show >>)
Seismic reliability assessment of RC tunnel-form structures with geometric irregularities using a combined system approach (Show >>)

List of publications and reserach products for the year 2019 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
A novelty detection method for large-scale structures under varying environmental conditions (Show >>)
Low-order feature extraction technique and unsupervised learning for SHM under high-dimensional data (Show >>)
Conference proceedings
Structural Health Monitoring for Condition Assessment Using Efficient Supervised Learning Techniques (Show >>)
Journal Articles
Effect of imperfections due to material heterogeneity on the offset of polysilicon MEMS structures (Show >>)
Estimation of air damping in Out-of-plane comb-drive actuators (Show >>)
Identification of strength and toughness of quasi-brittle materials from spall tests: a Sigma-point Kalman filter approach (Show >>)
Stochastic effects on the dynamics of the resonant structure of a Lorentz force MEMS magnetometer (Show >>)

List of publications and reserach products for the year 2018 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
A thermodynamically consistent model for shape-memory ionic polymers (Show >>)
Conference proceedings
Polysilicon MEMS Sensors: Sensitivity to Sub-Micron Imperfections (Show >>)
Soft actuation of origami-inspired reconfigurable structures through shape memory ionic polymers (Show >>)
Scientific Books
Mechanics of Microsystems (Show >>)
Journal Articles
Cost–benefit optimization of structural health monitoring sensor networks (Show >>)
Mechanical characterization of polysilicon MEMS: A hybrid TMCMC/POD-kriging approach (Show >>)
Modelling the cushioning properties of athletic tracks (Show >>)
On the relationship between force reduction, loading rate and energy absorption in athletics tracks (Show >>)
On-chip testing: A miniaturized lab to assess sub-micron uncertainties in polysilicon MEMS (Show >>)
Online damage detection in structural systems via dynamic inverse analysis: A recursive Bayesian approach (Show >>)
Optimal placement of MEMS sensors for damage detection in composite plates (Show >>)
Statistical investigation of the mechanical and geometrical properties of polysilicon films through on-chip tests (Show >>)
Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design (Show >>)
manifesti v. 3.5.2 / 3.5.2
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