Alireza Entezami 
- (2024): Removal of freezing effects from modal frequencies of civil structures for structural health monitoring. In: Engineering Structures, v. 319 (November 2024).
- (2024): Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids. In: Buildings, v. 14, n. 4 (27 März 2024).
- (2024): Machine learning techniques for diagrid building design: Architectural–Structural correlations with feature selection and data augmentation. In: Journal of Building Engineering, v. 86 (Juni 2024).
- (2023): Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching. In: Structural Health Monitoring, v. 22, n. 6 (April 2023).
- (2023): On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method. In: Structure and Infrastructure Engineering, v. 20, n. 12 (Oktober 2023).
- (2023): Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning. In: Engineering Structures, v. 279 (März 2023).
- (2022): Robust decision-making by a new statistical feature extraction method reliable to noise and uncertainty. In: Journal of Civil Structural Health Monitoring, v. 13, n. 1 (Oktober 2022).
- (2022): Partially online damage detection using long-term modal data under severe environmental effects by unsupervised feature selection and local metric learning. In: Journal of Civil Structural Health Monitoring, v. 12, n. 5 (Juli 2022).
- (2022): Non-parametric empirical machine learning for short-term and long-term structural health monitoring. In: Structural Health Monitoring, v. 21, n. 6 (März 2022).
- (2021): Bridge health monitoring in environmental variability by new clustering and threshold estimation methods. In: Journal of Civil Structural Health Monitoring, v. 11, n. 3 (Mai 2021).
- (2017): An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification. In: Structural Health Monitoring, v. 17, n. 2 (Januar 2017).
- (2018): Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods. In: Structural Health Monitoring, v. 18, n. 2 (Januar 2018).
- (2018): Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods. In: Structural Health Monitoring, v. 18, n. 5-6 (September 2018).
- (2019): Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks. In: Structural Health Monitoring, v. 19, n. 6 (Dezember 2019).
- (2020): Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics. In: Structural Control and Health Monitoring, v. 28, n. 2 (Oktober 2020).
- (2020): Condition Assessment of Civil Structures for Structural Health Monitoring Using Supervised Learning Classification Methods. In: Iranian Journal of Science and Technology, Transactions of Civil Engineering, v. 44 (Oktober 2020).
- (2020): An innovative hybrid strategy for structural health monitoring by modal flexibility and clustering methods. In: Journal of Civil Structural Health Monitoring, v. 10, n. 5 (September 2020).
- (2020): A sensitivity‐based finite element model updating based on unconstrained optimization problem and regularized solution methods. In: Structural Control and Health Monitoring, v. 27, n. 5 (Mai 2020).
- (2014): Damage localization in shear buildings by direct updating of physical properties. In: International Journal of Advanced Structural Engineering, v. 6, n. 2 (April 2014).
- (2017): iterative order determination method for time-series modeling in structural health monitoring. In: Advances in Structural Engineering, v. 21, n. 2 (August 2017).
- (2014): Damage detection by updating structural models based on linear objective functions. In: Journal of Civil Structural Health Monitoring, v. 4, n. 3 (Mai 2014).
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