Sujith Mangalathu 
- : Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes. Dans: The Structural Design of Tall and Special Buildings.
- (2024): Near-field seismic response assessment of buckling-restrained braced frames for different engineering demand parameters. Dans: Journal of Constructional Steel Research, v. 216 (mai 2024).
- (2023): Machine learning‐based peak ground acceleration models for structural risk assessment using spatial data analysis. Dans: Earthquake Engineering and Structural Dynamics, v. 53, n. 1 (28 octobre 2023).
- (2023): Machine learning–assisted drift capacity prediction models for reinforced concrete columns with shape memory alloy bars. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 39, n. 4 (novembre 2023).
- (2023): Probabilistic curvature-and-drift limit states predictive models of high-strength bridge columns. Dans: Engineering Structures, v. 295 (novembre 2023).
- (2023): Generalized stacked LSTM for the seismic damage evaluation of ductile reinforced concrete buildings. Dans: Earthquake Engineering and Structural Dynamics, v. 52, n. 11 (juin 2023).
- (2023): The influence of earthquake characteristics on the seismic performance of reinforced concrete buildings in Australia with varying heights. Dans: Journal of Building Engineering, v. 67 (mai 2023).
- (2023): High dimensional model representation for flood fragility analysis of highway bridge. Dans: Engineering Structures, v. 281 (avril 2023).
- (2023): Prediction of axial load capacity of cold formed lipped channel section using machine learning. Dans: Structures, v. 50 (avril 2023).
- (2022): Seismic fragility assessment of bridges with as-built and retrofitted splice-deficient columns. Dans: Bulletin of Earthquake Engineering, v. 21, n. 1 (octobre 2022).
- (2022): High-Dimensional Model Approach for Stochastic Response of Multispan Box Girder Bridges. Dans: Journal of Bridge Engineering (ASCE), v. 27, n. 9 (septembre 2022).
- (2022): Machine Learning–Based Seismic Reliability Assessment of Bridge Networks. Dans: Journal of Structural Engineering (ASCE), v. 148, n. 7 (juillet 2022).
- (2022): Fragility functions for highway RC bridge under various flood scenarios. Dans: Engineering Structures, v. 260 (juin 2022).
- (2022): Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks. Dans: Journal of Building Engineering, v. 46 (avril 2022).
- (2022): Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems. Dans: Engineering Structures, v. 250 (janvier 2022).
- (2021): Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls. Dans: Journal of Structural Engineering (ASCE), v. 147, n. 11 (novembre 2021).
- (2021): Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. Dans: Engineering Structures, v. 235 (mai 2021).
- (2019): Rapid seismic damage evaluation of bridge portfolios using machine learning techniques. Dans: Engineering Structures, v. 201 (décembre 2019).
- (2021): Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames. Dans: Journal of Building Engineering, v. 34 (février 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. Dans: Earthquake Engineering and Structural Dynamics, v. 50, n. 5 (25 avril 2021).
- (2018): Fragility analysis of continuous pipelines subjected to transverse permanent ground deformation. Dans: Soils and Foundations, v. 58, n. 6 (décembre 2018).
- (2020): Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: Active Learning Approach. Dans: Journal of Structural Engineering (ASCE), v. 146, n. 12 (décembre 2020).
- (2020): Ground Motion-Dependent Rapid Damage Assessment of Structures Based on Wavelet Transform and Image Analysis Techniques. Dans: Journal of Structural Engineering (ASCE), v. 146, n. 11 (novembre 2020).
- (2019): Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study. Dans: Journal of Structural Engineering (ASCE), v. 145, n. 10 (octobre 2019).
- (2019): Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-Girder Bridges Using Bayesian Parameter Estimation. Dans: Journal of Earthquake Engineering, v. 23, n. 6 ( 2019).
- (2014): Numerical Study on the Peak Strength of Masonry Spandrels with Arches. Dans: 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. Dans: Earthquake Engineering and Structural Dynamics, v. 46, n. 14 (novembre 2017).
- (2012): Review of strength models for masonry spandrels. Dans: Bulletin of Earthquake Engineering, v. 11, n. 2 (novembre 2012).
- (2018): Probabilistic Seismic Vulnerability Assessment of Tall Horizontally Curved Concrete Bridges in California. Dans: Journal of Performance of Constructed Facilities (ASCE), v. 32, n. 6 (décembre 2018).
- (2018): Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement. Dans: Tunnelling and Underground Space Technology, v. 76 (juin 2018).
Structurae coopère avec
