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Sujith Mangalathu, Ph.D
Sujith Mangalathu, Ph.D
Mangalathu, Mylamkulam
Bestätigte E-Mail-Adresse bei gatech.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach
S Mangalathu, SH Hwang, JS Jeon
Engineering Structures 219, 110927, 2020
6742020
Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques
S Mangalathu, JS Jeon
Engineering Structures 160, 85-94, 2018
3362018
Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls
DC Feng, WJ Wang, S Mangalathu, E Taciroglu
Journal of Structural Engineering 147 (11), 04021173, 2021
2932021
Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls
S Mangalathu, H Jang, SH Hwang, JS Jeon
Engineering Structures 208, 110331, 2020
2772020
Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach
J Rahman, KS Ahmed, NI Khan, K Islam, S Mangalathu
Engineering Structures 233, 111743, 2021
2592021
Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes
S Mangalathu, G Heo, JS Jeon
Engineering Structures 162, 166-176, 2018
2552018
Classifying earthquake damage to buildings using machine learning
S Mangalathu, H Sun, CC Nweke, Z Yi, HV Burton
Earthquake Spectra 36 (1), 183-208, 2020
2532020
Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements
DC Feng, WJ Wang, S Mangalathu, G Hu, T Wu
Engineering Structures 235, 111979, 2021
2452021
Machine learning–based failure mode recognition of circular reinforced concrete bridge columns: Comparative study
S Mangalathu, JS Jeon
Journal of Structural Engineering 145 (10), 04019104, 2019
2392019
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
S Mangalathu, SH Hwang, E Choi, JS Jeon
Engineering Structures 201, 109785, 2019
1822019
Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames
SH Hwang, S Mangalathu, J Shin, JS Jeon
Journal of Building Engineering 34, 101905, 2021
1652021
Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression
S Mangalathu, JS Jeon, R DesRoches
Earthquake Engineering and Structural Dynamics 47 (3), 784-801, 2018
1622018
Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement
S Mangalathu, H Shin, E Choi, JS Jeon
Journal of Building Engineering 39, 102300, 2021
1352021
Predicting the dissolution kinetics of silicate glasses using machine learning
NM Krishnan, S Mangalathu, MM Smedskjaer, A Tandia, H Burton, ...
Journal of Non-Crystalline Solids 487, 37-45, 2018
1332018
Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques
S Mangalathu, JS Jeon
Earthquake Engineering & Structural Dynamics 48 (11), 1238-1255, 2019
1302019
PERFORMANCE BASED GROUPING AND FRAGILITY ANALYSIS OF BOX-GIRDER BRIDGES IN CALIFORNIA
S Mangalathu
Georgia Institute of Technology, 2017
1172017
Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions
S Mangalathu, HV Burton
International Journal of Disaster Risk Reduction 36, 101111, 2019
1152019
Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems
S Mangalathu, K Karthikeyan, DC Feng, JS Jeon
Engineering Structures 250, 112883, 2022
992022
Parameterized seismic fragility curves for curved multi-frame concrete box-girder bridges using Bayesian parameter estimation
JS Jeon, S Mangalathu, J Song, R Desroches
Journal of Earthquake Engineering 23 (6), 954-979, 2019
962019
ANCOVA-based grouping of bridge classes for seismic fragility assessment
S Mangalathu, JS Jeon, JE Padgett, R DesRoches
Engineering Structures 123, 379-394, 2016
932016
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