Sujith Mangalathu 
- (2024): Machine learning models for seismic analysis of buckling-restrained braced frames. In: Journal of Building Engineering, v. 98 (December 2024).
- : Rapid damage state identification of structures using generalized zero‐shot learning method. In: Earthquake Engineering and Structural Dynamics.
- (2024): Machine learning-based framework for predicting the fire-induced spalling in concrete tunnel linings. In: Tunnelling and Underground Space Technology, v. 153 (November 2024).
- (2024): Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes. In: The Structural Design of Tall and Special Buildings, v. 33, n. 12 (8 July 2024).
- (2024): Near-field seismic response assessment of buckling-restrained braced frames for different engineering demand parameters. In: Journal of Constructional Steel Research, v. 216 (May 2024).
- (2023): Machine learning‐based peak ground acceleration models for structural risk assessment using spatial data analysis. In: Earthquake Engineering and Structural Dynamics, v. 53, n. 1 (28 October 2023).
- (2023): Machine learning–assisted drift capacity prediction models for reinforced concrete columns with shape memory alloy bars. In: Computer-Aided Civil and Infrastructure Engineering, v. 39, n. 4 (November 2023).
- (2023): Probabilistic curvature-and-drift limit states predictive models of high-strength bridge columns. In: Engineering Structures, v. 295 (November 2023).
- (2023): Generalized stacked LSTM for the seismic damage evaluation of ductile reinforced concrete buildings. In: Earthquake Engineering and Structural Dynamics, v. 52, n. 11 (June 2023).
- (2023): The influence of earthquake characteristics on the seismic performance of reinforced concrete buildings in Australia with varying heights. In: Journal of Building Engineering, v. 67 (May 2023).
- (2023): High dimensional model representation for flood fragility analysis of highway bridge. In: Engineering Structures, v. 281 (April 2023).
- (2023): Prediction of axial load capacity of cold formed lipped channel section using machine learning. In: Structures, v. 50 (April 2023).
- (2022): High-Dimensional Model Approach for Stochastic Response of Multispan Box Girder Bridges. In: Journal of Bridge Engineering (ASCE), v. 27, n. 9 (September 2022).
- (2022): Focal Mechanism Influence with Azimuth Using Near-Field Simulated Ground Motion: Application to a Multispan Continuous Concrete Single-Frame Box-Girder Bridge. In: Journal of Bridge Engineering (ASCE), v. 27, n. 6 (June 2022).
- (2022): Machine Learning–Based Seismic Reliability Assessment of Bridge Networks. In: Journal of Structural Engineering (ASCE), v. 148, n. 7 (July 2022).
- (2022): Fragility functions for highway RC bridge under various flood scenarios. In: Engineering Structures, v. 260 (June 2022).
- (2022): Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems. In: Engineering Structures, v. 250 (January 2022).
- (2021): Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls. In: Journal of Structural Engineering (ASCE), v. 147, n. 11 (November 2021).
- (2021): Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. In: Engineering Structures, v. 235 (May 2021).
- (2021): Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames. In: Journal of Building Engineering, v. 34 (February 2021).
- (2021): Quantifying the effects of long‐duration earthquake ground motions on the financial losses of steel moment resisting frame buildings of varying design risk category. In: Earthquake Engineering and Structural Dynamics, v. 50, n. 5 (25 April 2021).
- (2018): Fragility analysis of continuous pipelines subjected to transverse permanent ground deformation. In: Soils and Foundations, v. 58, n. 6 (December 2018).
- (2020): Ground Motion-Dependent Rapid Damage Assessment of Structures Based on Wavelet Transform and Image Analysis Techniques. In: Journal of Structural Engineering (ASCE), v. 146, n. 11 (November 2020).
- (2020): Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. In: Engineering Structures, v. 219 (September 2020).
- (2019): Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study. In: Journal of Structural Engineering (ASCE), v. 145, n. 10 (October 2019).
- (2019): Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-Girder Bridges Using Bayesian Parameter Estimation. In: Journal of Earthquake Engineering, v. 23, n. 6 ( 2019).
- (2014): Numerical Study on the Peak Strength of Masonry Spandrels with Arches. In: Journal of Earthquake Engineering, v. 18, n. 2 ( 2014).
- (2017): Performance-based grouping methods of bridge classes for regional seismic risk assessment: Application of ANOVA, ANCOVA, and non-parametric approaches. In: Earthquake Engineering and Structural Dynamics, v. 46, n. 14 (November 2017).
- (2018): Probabilistic Seismic Vulnerability Assessment of Tall Horizontally Curved Concrete Bridges in California. In: Journal of Performance of Constructed Facilities (ASCE), v. 32, n. 6 (December 2018).
- (2018): Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement. In: Tunnelling and Underground Space Technology, v. 76 (June 2018).
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