Sujith Mangalathu 
- (2024): Effect of data drift on the performance of machine‐learning models: Seismic damage prediction for aging bridges. In: Earthquake Engineering and Structural Dynamics, v. 53, n. 15 (5 November 2024).
- (2024): Data driven models for capacity prediction of CFS lipped channel flexural members. In: Structures, v. 62 (April 2024).
- (2023): Condition Assessment of Highway Bridges Using Textual Data and Natural Language Processing- (NLP-) Based Machine Learning Models. In: Structural Control and Health Monitoring, v. 2023 (Februar 2023).
- (2023): Data-driven shear strength predictions of recycled aggregate concrete beams with /without shear reinforcement by applying machine learning approaches. In: Construction and Building Materials, v. 387 (Juli 2023).
- (2023): Betweenness Centrality-Based seismic risk management for bridge transportation networks. In: Engineering Structures, v. 289 (August 2023).
- (2022): Effect of interior gravity framing system and composite floor slab action on the lifetime seismic risk assessment of steel frame buildings subjected to mainshock–aftershock sequence. In: Structures, v. 45 (November 2022).
- (2022): Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns. In: Construction and Building Materials, v. 356 (November 2022).
- (2022): Effectiveness Assessment of TMDs in Bridges under Strong Winds Incorporating Machine-Learning Techniques. In: Journal of Performance of Constructed Facilities (ASCE), v. 36, n. 5 (Oktober 2022).
- (2022): Data‐driven rapid damage evaluation for life‐cycle seismic assessment of regional reinforced concrete bridges. In: Earthquake Engineering and Structural Dynamics, v. 51, n. 11 (September 2022).
- (2018): Adjustment Factors to Account for the Effect of Bridge Deck Horizontal Curvature on the Seismic Response of Concrete Box-Girder Bridges in California. In: Earthquake Spectra, v. 34, n. 2 (Mai 2018).
- (2022): Basin effects on tall bridges in Seattle from M9 Cascadia scenarios. In: Engineering Structures, v. 260 (Juni 2022).
- (2022): Diaphragm abutment Californian bridges subjected to UCERF2 rupture scenarios: Complete damage state evolution with improvements to seismic codes. In: Soil Dynamics and Earthquake Engineering, v. 155 (April 2022).
- (2022): Estimation of economic seismic loss of steel moment-frame buildings using a machine learning algorithm. In: Engineering Structures, v. 254 (März 2022).
- (2021): The effect of ground motion characteristics on the fragility analysis of reinforced concrete frame buildings in Australia. In: Structures, v. 34 (Dezember 2021).
- (2021): Explainable Machine learning on New Zealand strong motion for PGV and PGA. In: Structures, v. 34 (Dezember 2021).
- (2021): The effect of rupture directivity, distance and skew angle on the collapse fragilities of bridges. In: Bulletin of Earthquake Engineering, v. 19, n. 14 (September 2021).
- (2021): Control equation of feasible pre-stresses and feasibility of new types of rotating surface cable domes. In: Engineering Structures, v. 246 (November 2021).
- (2021): Bridge fragilities to network fragilities in seismic scenarios: An integrated approach. In: Engineering Structures, v. 237 (Juni 2021).
- (2021): Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement. In: Journal of Building Engineering, v. 39 (Juli 2021).
- (2021): Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach. In: Engineering Structures, v. 233 (April 2021).
- (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).
- (2020): Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls. In: Engineering Structures, v. 208 (April 2020).
- (2018): Displacement-Dependent Lateral Earth Pressure Models. In: Journal of Engineering Mechanics (ASCE), v. 144, n. 6 (Juni 2018).
- (2017): Compressive and flexural behaviour of reinforced concrete permeable piles. In: Engineering Structures, v. 147 (September 2017).
- (2016): ANCOVA-based grouping of bridge classes for seismic fragility assessment. In: Engineering Structures, v. 123 (September 2016).
- (2017): Bridge classes for regional seismic risk assessment: Improving HAZUS models. In: Engineering Structures, v. 148 (Oktober 2017).
- (2017): A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes. In: Engineering Structures, v. 153 (Dezember 2017).
- (2018): Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. In: Engineering Structures, v. 160 (April 2018).
- (2018): Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes. In: Engineering Structures, v. 162 (Mai 2018).
- (2018): Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression. In: Earthquake Engineering and Structural Dynamics, v. 47, n. 3 (März 2018).
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