Sujith Mangalathu, Ph.D

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Jong-Su JeonAssociate Professor of Civil and Environmental Engineering, Hanyang UniversityVerified email at hanyang.ac.kr
Reginald DesRochesDean of Engineering, Rice UniversityVerified email at rice.edu
Seong-Hoon HwangAssistant Professor, Kumoh National Institute of TechnologyVerified email at kumoh.ac.kr
Pengpeng NiProfessor of Geotechnical Engineering, Sun Yat-sen UniversityVerified email at mail.sysu.edu.cn
De-Cheng FengProfessor, School of Civil Engineering, Southeast University, ChinaVerified email at seu.edu.cn
Henry V Burton Ph.D., S.E.Associate Professor, University of California, Los AngelesVerified email at seas.ucla.edu
Farahnaz SoleimaniAssistant Professor, Oregon State UniversityVerified email at gatech.edu
Robin Davis P.Assistant Professor of Civil Engineering National Institute of Technology CalicutVerified email at nitc.ac.in
Muhamed Safeer Pandikkadavath, PhDNational Institute of Technology CalicutVerified email at nitc.ac.in
Surendra Nadh SomalaAssociate Professor, Indian Institute of Technology HyderabadVerified email at iith.ac.in
Kamrul IslamPolytechnique MontrealVerified email at polymtl.ca
Khondaker Sakil Ahmed, PhD, PEng,...Associate Professor, Department of CE, MIST, BSc (BUET), PhD (National University of Singapore)Verified email at ce.mist.ac.bd
Anisha AGovernment Engineering College, PalakkadVerified email at gecskp.ac.in
Bilal AhmedPh.D. Applied Mechanics and MaterialsVerified email at nyu.edu
Mengdie ChenHanyang UniversityVerified email at hanyang.ac.kr
Zhengxiang YiApplied Scientist, AmazonVerified email at g.ucla.edu
Nafiz Imtiaz KhanPhD Student, UC DavisVerified email at ucdavis.edu
Jesika RahmanPhD Student, Department of Civil Engineering, University of Calgary, AB, CanadaVerified email at ucalgary.ca
Gang HUHarbin Institute of Technology (Shenzhen)Verified email at hit.edu.cn
Jiuk Shin, PhD, Associate ProfessorGyeongsang National UniversityVerified email at gnu.ac.kr

Sujith Mangalathu, Ph.D

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Verified email at gatech.edu - Homepage

Machine Learning Statistical Modeling Risk Assessment Probabilistic Methods


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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	437	2020
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	262	2018
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	214	2020
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	210	2018
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	199	2019
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	185	2020
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	182	2021
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	181	2021
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	172	2021
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	146	2018
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques S Mangalathu, SH Hwang, E Choi, JS Jeon Engineering Structures 201, 109785, 2019	142	2019
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	118	2018
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	111	2021
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	107	2019
PERFORMANCE BASED GROUPING AND FRAGILITY ANALYSIS OF BOX-GIRDER BRIDGES IN CALIFORNIA S Mangalathu Georgia Institute of Technology, 2017	105	2017
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	99	2021
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	89	2019
ANCOVA-based grouping of bridge classes for seismic fragility assessment S Mangalathu, JS Jeon, JE Padgett, R DesRoches Engineering Structures 123, 379-394, 2016	86	2016
Review of strength models for masonry spandrels K Beyer, S Mangalathu Bulletin of Earthquake Engineering 11, 521-542, 2013	83	2013
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	78	2019

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