Alireza Entezami

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Assistant Professor (RTDa)

Politecnico di Milano

Italy

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Alireza Entezami is an Assistant Professor at Politecnico di Milano, Italy. His research focuses on advanced methods for predicting and normalizing the responses of long-span bridges, particularly under environmental variabilities such as temperature and windstorm effects. Recent works include the application of machine learning techniques for structural health monitoring and damage detection in bridge systems.

Recent Grants

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Health Monitoring of Large Structures under Earth Observation and Data Mining

Open Date: 2021-05-01

Close Date: 2024-04-01

Alireza Entezami

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Recent Grants

Articles (17)

Improvements in forecasting and normalizing limited displacement responses of long-span steel bridges subjected to seasonal temperature variability

Displacement prediction for long-span bridges via limited remote sensing images: An adaptive ensemble regression method

Experimental Investigation on the Seismic Behavior of RC Double‐Column Medium‐Height Bents Retrofitted With SCEB‐Us

Machine learning‐aided prediction of windstorm‐induced vibration responses of long‐span suspension bridges

Early warning of structural damage via manifold learning-aided data clustering and non-parametric probabilistic anomaly detection

A kernelized deep regression method to simultaneously predict and normalize displacement responses of long-span bridges via limited synthetic aperture radar images

Short-term damage alarming with limited vibration data in bridge structures: A fully non-parametric machine learning technique

A systematic correlation analysis for regression model selection: Application to bridge response prediction using contact and remote sensor systems

An innovative predictive method based on supervised teacher-student learning for forecasting limited structural responses of long-span bridges from satellite images

Application of regression and remote sensing technology for determining sufficiency of contact-based sensors in long-term SHM of civil structures

Mitigation of unmeasured environmental and operational variability in long-term bridge health monitoring by unsupervised statistical learning

Machine learning techniques for diagrid building design: Architectural–Structural correlations with feature selection and data augmentation

Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids

On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method

Review on smartphone sensing technology for structural health monitoring

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Collaborators (2)

Carlo De Michele

Aldo Ghisi

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