Publications

NeurIPS/ICML

1. Mangalathu, S., Burton, H., "Machine Learning based Damage Detection of Residential Buildings", Neural Information Processing Systems 2018 (NeurIPS 2018), Montreal, Canada.

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REFEREED JOURNAL ARTICLES  

        [github : https://github.com/sujithmangalathu]

1. Li, Y.,  Sun, Z.,  Mangalathu, S., Li, Y.,  Yang, H.,  He,W., "Seismic damage states prediction of in-service bridges using feature-enhanced Swin Transformer without reliance on damage indicators", Engineering Applications of Artificial Intelligence, Vol  159, Part A, 111651 2025, (link).

2. Shturmin, S., Mangalathu, S., Jeon, J-S., "Application of latent variable models for hidden pattern identification and machine learning prediction improvement in structural engineering", Engineering Applications of Artificial Intelligence, Vol  156, Part C, 111282, 2025, (link).

3. Agarwal, H., Shariff, M. N.,  Mangalathu, S., "Prediction of Flexural Crack Width of Reinforced Concrete Beams using Interpretable Machine Learning Algorithms",Construction and Building Materials, Vol  485, 141628, 2025, (link).

4. Sreedevi, V. M., Anisha, A., Jithin, K. V., Jyothika, S., Shilpa, T., , Mangalathu, S., Negi, P., Davis, R., "Interpretable Machine Learning based Tsunami Bridge Fragility Assessment", International Journal of Disaster Risk Reduction, Vol  123, 105507, 2025, (link).

5. He, Y., Guo, J., Ping, H., Zhu, M., Mangalathu, S., "A robust force-finding framework for tensegrity structures using gradient-boosting decision trees and Latin hypercube sampling", Structures,  Vol  76, 108863, 2025, (link).

6. Aditya, A. P., Navvar, K. M., Bahubali, K. M., Pandikkadavath, M. S., Mangalathu, S.,"Near-fault seismic vulnerability assessment of corrosion inflicted steel moment resisting frames", Journal of Constructional Steel Research, Vol  229, 109527 , 20245, (link).

7. Tusher, T. H., Moneim, A. A., Ahmed, S. K., Billah, AHM. M., Mangalathu, S., "Predicting shear strength of hollow pretensioned spun precast concrete pile using machine learning models", Structures,  Vol  74, 108547, 2025, (link).

8. Anand, T. P., Pandikkadavath, M. S., Mangalathu, S.,  Sahoo, D. R., " Machine learning models for seismic analysis of buckling-restrained braced frames", Journal of Building Engineering, Vol. 98, 11398 (link).

9. Chen, M., Park, Y.,  Mangalathu, S., Jeon, J-S., "Effect of Data Drift on the Performance of Machine-Learning Models: Seismic Damage Prediction for Aging Bridges ", Earthquake Engineering and Structural Dynamics, Vol. 53, No. 15, pp 4541 - 4561 (link).

10. Yazdanpanah, O., Chang, M., Park, M., Mangalathu, S., "Smart bridge bearing monitoring: predicting seismic responses with a multi-head attention-based CNN-LSTM network", Earthquake Engineering and Structural Dynamics, Vol. 53, No. 14, pp 4379 -4403(link).

11. Chen, M., Mangalathu, S., Jeon, J-S., "Rapid damage state identification of structures using generalized zero-shot learning method. ", Earthquake Engineering and Structural Dynamics,  Vol. 53, No. 14, pp 4269 -4486(link).

12. Sirisena, G., Jayasinghe, T., Gunawardena , T., Zhang , L., Mendis, P., Mangalathu, S., "Machine learning-based framework for predicting the fire-induced spalling in concrete tunnel linings", Tunneling and Underground Space Technology, Vol. 153, 2024, pp 106000 (link).

13. Mansouri, A., Mangalathu, S., "Interpretable Data-Driven Shear Strength Estimation of Concrete-Filled Steel Tubes", The Structural Design of Tall and Special Buildings, Vol  33, No 12, 2024  (link).

14. Anand, T. P., Pandikkadavath, M. S., Mangalathu, S., "  Near-field Seismic Response Assessment of BRBFs for different Engineering Demand Parameters", Journal of Constructional Steel Research, Vol  216, 108583 , 2024 (link).

15.  Sreedevi V.M., Parvathi V.E., Rajneesh K., Aswin S., Aswin V., Anisha A., Usman Arshad P. J., Mangalathu, S., Davis, R., "Data-driven Models for Capacity Prediction of CFS Lipped Channel Flexural Members", Structures,  Vol  62, 106193, 2024, (link).

16. Pal, A., Ahmed, S. K.,  Mangalathu, S. "Data-Driven Machine Learning Approaches for Predicting Slump of Fiber-Reinforced Concrete Containing Waste Rubber and Recycled Aggregate", Construction and Building Materials, Vol  417, 135369, 2024, (link) 

17. Srivastava, C., Pandikkadavath, M. S., AlHamaydeh, M., Mangalathu, S., "Seismic Response of RC Bridges under Near-Fault Ground Motions: A Parametric Investigation", Structures, Vol  61, 106033, 2024, (link) 

18.  Jas, K., Mangalathu, S., Dodagoudar, G. R., "Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model", Computers and Geotechnics, Vol  167, 106051 , 2024, (link) 

19. Somala,  S. N., Chanda, S., AlHamaydeh, M., Mangalathu, S., "Explainable XGBoost-SHAP Machine-Learning Model for Prediction of Ground Motion Duration in New Zealand", ASCE Natural Hazards Review, Vol  25, No 2, 2024  (link).

20. Ahmed, B., Mangalathu, S., Jeon, J-S., "Unveiling Out-of-Distribution Data for Reliable Structural Damage Assessment in Earthquake Emergency Situations  ", Automation in Construction,  Vol  156, 105142 , 2023, (link)

21. Lee, S. C., Mangalathu, S., Jeon, J-S.,  "Machine-learning-assisted drift-capacity prediction models for reinforced concrete columns with shape memory alloy bars", Computer-Aided Civil and Infrastructure Engineering, Vol  39, pp 595 - 616 , 2023, (link).

22. Feng, D-C., Wang., W-J., Mangalathu, S., Sun, Z., "Condition assessment of highway bridges using textual data and natural language processing (NLP)-based machine learning models", Structural Control and Health Monitoring, Vol 2023, ID: 9761154, (link).

23. Nadia, S., Ahmed, B., Mangalathu, S., Jeon, J-S., "Machine Learning-Based Peak Ground Acceleration Models for Structural Risk Assessment Using Spatial Data Analysis", Earthquake Engineering and Structural Dynamics,  2023, Vol 53, pp 152-178, (link)

24. Zhong, J., Zhu, Y., Mangalathu, S., Gao, K., "Probabilistic curvature-and-drift limit states predictive models of high-strength bridge columns", Engineering Structures, Vol  295, 116859, 2023, (link)

25. Anisha, A., Malavika, K.,  Erfana, K.,  Dheeraj, K., Subahan, A., Shinsha Raj, A K., Mangalathu, S., Davis, R., "2018 Kerala Flood Damage: Survey, Identification and Damage Prediction Models using Machine Learning", ASCE Open, Vol  1, 04023002-2, 2023, (link)

26. Jayasinghe , T., Chen, W, B., Zhang, Z., Meng., X., Li, Y., Gunawardena, T.,  Mangalathu, S., Mendis, P., "Data-driven shear strength predictions of recycled aggregate concrete beams with /without shear reinforcement by applying machine learning approaches", Construction and Building Materials, Vol  387, 131604, 2023, (link)

27. Chen, M., Mangalathu, S., Jeon, J-S., "Betweenness Centrality-Based Seismic Risk Management for Bridge Transportation Networks  ",  Engineering Structures, Vol  289, 116301, 2023, (link)

28. Ahmed, B., Mangalathu, S., Jeon, J-S., "Generalized stacked LSTM for the seismic damage evaluation of ductile reinforced concrete buildings", Earthquake Engineering and Structural Dynamics, Vol 52, pp 3477-3503, 2023, (link).

29. Anisha, A., Sahu, D. K.,  Sarkar, P., Mangalathu, S., Davis, R.," High dimensional model representation for flood fragility analysis of highway bridges", Engineering Structures,  Vol  281, 115817, 2023, (link)

30. Rajneesh, K ., Parvathi,  V. E.,  Aswin,  S., Aswin V., Anisha A., Usman Arshad P. J., Mangalathu, S., Davis, R., "Prediction of axial load capacity of cold from lipped channel section using machine learning, Structures, Vol  50, pp 1429-1446 , 2023, (link)

31. Alothman, A., Mangalathu, S.,  Alam, M. MD,  Allawi, A., "The influence of earthquake characteristics on the seismic performance of reinforced concrete buildings in Australia with varying heights", Journal of Building Engineering, Vol  67, 105957, 2023,(link).

32. Eyitayo, O., Mangalathu, S., "Seismic Fragility Assessment of Bridges with As-built and Retrofitted Splice-Deficient Columns", Bulletin of Earthquake Engineering, Vol  21, pp: 583-603 2023, (link).

33. Hwang, S-H., Shokrabadi, M.,  Mangalathu, S., Jeon, J-S., "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", Structures, Vol  45, pp: 2332-2342, 2022 , (link).

34. Cakiroglu, C., Islam, K.,  Bekdaş,  G., Isikdag, U., Mangalathu, S., "Explainable Machine Learning Models for Predicting the Axial Compression Capacity of Concrete Filled Steel Tubular Columns", Construction and Building Materials, Vol  356, 129227, 2022, (link).

35. Pandikkadavath, M. S., Jithiya, K.K., Nagarajan, P., Mangalathu, S., "Seismic MainShock-AfterShock Response Assessment of Reinforced Concrete Bridges Pre-Exposed to Flood Induced Local Scouring", Bulletin of Earthquake Engineering, 2022 (link).

36. Chen, M., Mangalathu, S., Jeon, J-S., "Seismic Reliability Assessment of Bridge Networks Accounting for Travel Time and Connectivity Reliabilities",  Earthquake Engineering and Structural Dynamics, Vol  51, No. 13, pp: 3097-3110, 2022 (link).

37. Xu, J. G.,  Feng, D-C., Mangalathu, S., Jeon, J-S.,  "Data-driven rapid damage evaluation for life-cycle seismic assessment of regional reinforced concrete bridges",  Earthquake Engineering and Structural Dynamics,  Vol  51, No. 11, pp: 2730-2751, 2022   (link).

38. Sahu, D. S., Sarkar, P., Davis, R., Mangalathu, S., "High Dimensional Model Approach for Stochastic Response of Multi-Span Box-girder Bridges ", ASCE Journal of Bridge Engineering, 27 (9), 04022074, 2022 (link).

39. Somala,  S. N.,  Parla, R.,  Mangalathu, S.,  "Basin Effects on Tall Bridges in Seattle from M9 Cascadia ",  Engineering Structures,  Vol  260, 114252, 2022 (link).

40. Anisha, A., Jacob, A., Davis, R.,Mangalathu, S., "Fragility Functions for Highway RC Bridges under Various Flood Scenarios", Engineering Structures,  Vol  260, 114244, 2022 (link) 

41. Sun, Z., Feng, D-C., Mangalathu, S.,  Wang, W-J.,  Su, D., "Effectiveness Assessment of TMDs in Bridges under Strong Winds Incorporating Machine Learning Techniques", ASCE Journal of Performance of Constructed Facilities, Vol  36, No. 5, 04022036, 2022 (link) 

42. Chen, M., Mangalathu, S., Jeon, J-S., "Data-driven Machine-learning based Seismic Reliability Assessment of Bridge Networks",  ASCE Journal of Structural Engineering, 148 (7), 06022002 (Iink). 

43. Pandikkadavath, M. S., Shaijal, K. M., Mangalathu, S., Davis, R., "Seismic Robustness Assessment of Steel Moment Resisting Frames Employing Material Uncertainty Incorporated Incremental Dynamic Analysis", Journal of Constructional Steel Research,  Vol 191, 107200, 2022 (link).

44. Somala,  S. N.,  Karthik Reddy, KSK., Mangalathu, S., "Diaphragm Abutment Californian Bridges Subjected to UCERF2 Rupture Scenarios: Collapse Fragility Evolution with Improvements to Seismic Codes", Soil Dynamics and Earthquake Engineering, Vol 155, 107204, 2022 (link). 

45. Somala,  S. N.,  Mangalathu, S., Chanda, S., Karthik Reddy, KSK., Parla, R.,  "Focal Mechanism Influence with Azimuth using Near-Field Simulated Ground Motion: Application to Multi-Span Continuous Concrete Single Frame Box Girder Bridge", ASCE Journal of Bridge Engineering,  Vol 27, No. 6, 2022 (link). 

46. Hwang, S-H., Mangalathu, S., Shin, J., Jeon, J-S., "Estimation of economic seismic loss of steel moment-frame buildings using a machine learning algorithm  ", Engineering Structures, Vol 254,113877. 2022 (link).

47. Ahmed, B., Mangalathu, S., Jeon, J-S., "Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks", Journal of Building Engineering, Vol 46,103737, 2022 (link).

48. Mangalathu, S., Karthikeyan, K., Feng, D-C., Jeon, J-S., "Machine-Learning Interpretability Techniques for Seismic Performance Assessment of Infrastructural Systems ",  Engineering Structures, Vol 250,112883, 2022 (link).

49.  Somala,  S. N., Chanda, S., Karthikeyan, K., and  Mangalathu, S.  "Explainable Machine Learning on New Zealand Strong Motion for PGV and PGA, Structures, Vol 34, PP 4977-4985, 2021 (link).

50. Alothman, A., Mangalathu, S., Hashemi, J., Al-Mosawe, A., Alam, M.,  Allawi, A., "The effect of ground motion characteristics on the fragility analysis of reinforced concrete frame buildings in Australia", Structures, Vol. 34, PP 3583-3595, 2021 (link)

51. Somala,  S. N.,  Karthikeyan, K.,  Mangalathu, S.,  "Time Period Estimation of Masonry Infilled RC Frames using Machine Learning", Structures, Vol. 24, PP 1560-1566, 2021 (link).

52. Somala,  S. N.,  Karthik Reddy, KSK.,  Mangalathu, S.,  "The Effect of Rupture Directivity, Distance and Skew Angle on the Collapse Fragilities of Bridges",  Bulletin of Earthquake Engineering, 2021, (link).

53. Guo, J., Zhao, Y.,  Mangalathu, S., Liu, G., Zhou, G., Chen, W., "Control equation of  feasible pre-stresses and feasibility of new types of rotating surface cable domes",  Engineering Structures, Vol 246, 113000, 2021, (link). 

54. Feng, D-C., Wang, W-J., Mangalathu, S., Taciroglu, E., "Interpretable XGBoost-SHAP Machine Learning Model for Shear Strength Prediction of Squat RC Walls", ASCE Journal of Structural Engineering,  Vol 147, No.11, 2021, (link).  

55. Liang-Jun, C., Yu, H., Mangalathu, S., Hong-Ye, G., Qian-hui, P.,  "Adaptive sampling approach based on Jensen-Shannon divergence for efficient reliability analysis", Journal of Central South University, 2021, Vol 28, pp 2407－2422, (link).

56. Chen, M., Mangalathu, S., Jeon, J-S., "Bridge Fragilities to Network Fragilities in Seismic Scenarios: An Integrated Approach",  Engineering Structures, Vol 237,112212, 2021(Iink).

57. Mangalathu, S., Shin, H., Choi, E., Jeon, J-S., "Explainable Machine Learning Models for Punching Shear Strength Estimation of Flat Slabs without Transverse Reinforcement",  Journal of Building Engineering, Vol 39,102300, 2021  (Iink).

58. Feng, D-C., Wang, W-J., Mangalathu, S., Hu, G., Wu, T., "Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements",  Engineering Structures, Vol 235,111979, 2021 (Iink)

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59. Unnikrishnan, J., Mangalathu, S., Raman kutty, V., "Estimating under-reporting of Covid-19 cases in Indian states: an approach using a delay-adjusted case fatality ratio", BMJ Open,11(1), e042584, 2021 (link).

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60. Rahman, J., Ahmed, S. K., Khan, I. N., Islam, K., Mangalathu, S., "Data-Driven Shear Strength Prediction of Steel Fiber Reinforced Concrete Beams Using Machine Learning Algorithms" , Engineering Structures, Vol 233, 111743, 2021  (Iink).

61. Hwang, S-H., Mangalathu, S., Jeon, J-S., "Quantifying the Effects of Long-duration Earthquake Ground Motions on the Financial Losses of Steel Moment Resisting Frame Buildings of Varying Design Risk Category", Earthquake Engineering and Structural Dynamics, 2021, (link).

62. Hwang, S-H., Mangalathu, S., Shin, J.,  Jeon, J-S., "Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames", Journal of Building Engineering,  Vol 34,  2021, (link).

63. Mangalathu, S., Jeon J-S., "Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: An Active Learning Approach ", ASCE Journal of Structural Engineering, Vol 146, No.12 2020, (link).  

64. Mangalathu, S.,  Hwang, S-H., Jeon, J-S.,  "Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach", Engineering Structures, Vol 219, 2020, 110927 (link).

65. Mangalathu, S., Jeon J-S., "Ground Motion Dependent Rapid Damage Assessment of Structures Based on Wavelet Transform and Image Analysis Techniques", ASCE Journal of Structural Engineering, Vol 146, No. 11 (link).  

66. Mangalathu, S., Jang, H., Hwang, S-H., , Jeon, J-S.,  "Data-driven Machine Learning based Seismic Failure Mode Identification of Reinforced Concrete Shear Walls", Engineering Structures, Vol 208, 2020, 110331 (link).

67. Mangalathu, S., Sun, H., Yi. Z., Nweke, C.,  Burton, H., "Classifying Earthquake Damages to Buildings using Machine Learning", ,Earthquake Spectra, Feb 2020, (link).

68. Ni, P., Mangalathu, S.,  “Enhanced Fragility Relations of Buried Pipelines through Lasso Regression”,  Acta  Geotechnica Vol. 15, 2020 (link).

69. Mangalathu, S., Hwang, S-H., Choi, E., Jeon, J-S.,  "Rapid Seismic Damage Evaluation of Bridge Portfolios Using Machine Learning Techniques", Engineering Structures, Vol. 201, 2019, 109785 (link).

70. Mangalathu, S., Jeon J-S., "Stripe-Based Fragility Analysis of Multi-Span Concrete Bridge Classes using Machine Learning Techniques", Earthquake Engineering and Structural Dynamics, Vol 48, No.11, 2019, pp 1238-1255  (link). [Top downloaded article in EESD 2018-2019].

71. Jeon J-S., Mangalathu, S., Lee, S-Y.,  "Seismic Fragility Curves for California Concrete Bridges with Flared Two-Column Bents",  Bulletin of Earthquake Engineering, Vol 17, No.7, 2019, pp 4299-4319 (link).

72. Mangalathu, S., Jeon J-S., "Machine Learning based Failure Mode Identification of Circular Bridge Columns: A Comparative Study", ASCE Journal of Structural Engineering, Vol 145, No.10, 2019,  (link). [Editors Selection: Special Collection on Data Analytics in Structural Engineering], [Editors choice, October 2019]

73. Mangalathu, S., Burton, H., "Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions", International Journal of Disaster and Risk Reduction, Vol. 36,  2019, (link).

74. Mangalathu, S., Jeon J-S., Jiang, J., "Skew Adjustment Factors for Fragilities of  California Box-girder Bridges Subjected to Near-Fault and Far-Field Ground Motions", ASCE Journal of Bridge Engineering, Vol. 24,  No. 1 2019, (link).

75. Sahu, D., Nishanth, M., Dhir, P., Sarkar, S., Davis, R.,Mangalathu, S., "Stochastic Response Analysis of Reinforced Concrete Buildings using High Dimensional Model Representation", Engineering Structures, Vol. 159, 2019, pp 412-422 (link).

76. Mangalathu, S., Jeon J-S., "Adjustment Factors to Account the Effect of Bridge-deck Horizontal Curvature on the Seismic Response of Concrete Box-girder Bridges in California", Earthquake Spectra, Vol. 34, No. 2, 2018 (link).

77. Ni, P., Mangalathu, S., Song, L., Mei, G., Zhao, Y., "Displacement-dependent Lateral Earth Pressure Models", ASCE Journal of Engineering Mechanics,Vol. 144, 2018,  (link).

78. Mangalathu, S., Choi, E., Park, H., Jeon J-S., "Probabilistic Seismic Vulnerability Assessment of Tall, Horizontally Curved Box-girder Bridges in California , ASCE Journal of Performance of Constructed Facilities, Vol. 36, No. 6, 2018 (link). [Editors choice, December 2018].

79. Dukes, J., Mangalathu, S., Padgett, J. E., DesRoches, R., "Development of bridge specific fragility methodology to improve the seismic resilience of bridges", Earthquakes and Structures, Vol. 15, No. 3, 2018 (link).

80. Ni, P., Mangalathu, S.,  "Simplified Evaluation of Pipe Strains Crossing a Normal Fault through the Dissipated Energy Method", Engineering Structures, Vol. 167, 2018, pp 393-406 (link).

81. Ni, P., Mangalathu, S.,  “Fragility Analysis of Gray Iron Pipelines Subjected to Tunneling induced Ground Settlement”,  Tunneling and Underground Space Technology, Vol. 76, 2018, pp 133-144 (link).

82. Krishnan, A., Mangalathu, S., Smedskjaer, M., Tandia, M., Burton, H., Bauchy, M., "Predicting the Dissolution Kinetics of Silicate Glasses using Machine Learning", Journal of Non-Crystalline Solids, Vol. 487, 2018, pp 37-45 (link).[hottest and most downloaded article].

83. Ni, P., Mangalathu, S., Yi, Y.,  “Fragility analysis of continuous pipelines subject to transverse permanent ground deformation",  Soils and Foundations, Vol. 58 ,6 2018, pp 1400-1413 (link).

84. Mangalathu, S., Heo, G., Jeon J-S., "Use of Artificial Neural Networks to Develop Fragility Curves of Skewed Concrete Box-girder Bridge Classes for Regional Risk Assessment",  Engineering Structures, Vol. 162, 2018, pp 166-176 (link).

85. Ni, P., Mangalathu, S., Mei, G., Zhao, Y., "Laboratory Investigation of Pore Pressure Dissipation in Clay around Permeable Piles", Canadian Geotechnical Journal, Vol. 59, No. 9, 2018, (link).

86. Mangalathu, S., Jeon J-S., "Classification of Failure Mode and Prediction of Shear Strength of Concrete Beam-Column Joints using Machine Learning Techniques", Engineering Structures, Vol. 160, 2018, pp 85-794 (link).

87. Mangalathu, S., Jeon J-S.,  DesRoches, R., "Critical Uncertainty Parameters Influencing Seismic Performance of Bridges Using Lasso Regression", Earthquake Engineering and Structural Dynamics, Vol. 47, 2018, pp 784-801 (link).

88. Soleimani, F., Mangalathu, S., DesRoches, R., “ A Comparative Analytical Study on the Fragility Assessment of Bridges with Various Column Shapes", Engineering Structures, Vol 153, 2017, pp 460-478 (link).

89. Mangalathu, S., Soleimani, F., Jeon J-S.,  "Bridge Classes for Regional Risk Assessment: Improving HAZUS  models", Engineering Structures, Vol. 148, 2017, pp 755-766 (link).

90. Jeon J-S., Mangalathu, S., Song, J., DesRoches, R., “Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-girder Bridges using Bayesian Parameter Estimation”, Journal of Earthquake Engineering (link).

91. Mangalathu, S., Jeon J-S.,  DesRoches, R., Padgett, J. E., “Performance-based Grouping Methods of Bridge Classes for Regional Seismic Risk Assessment: Application of ANOVA, ANCOVA, and Non-parameteric approaches”, Earthquake Engineering and Structural Dynamics, Vol. 46, 2017, pp 2587-2602 (link).

92. Ni, P., Mangalathu, S., Mei, G., Zhao, Y., “Compressive and Flexural Behavior of Reinforced Concrete Permeable Piles", Engineering Structures, Vol. 147, 2017, pp 316-327 (link).

93. Ni, P., Mangalathu, S., Mei, G., Zhao, Y., “Permeable piles: An Alternative to Improve the Performance of Driven Piles", Computers and Geotechnics, Vol. 84, 2017, pp 78-87.  (link).

94. Mangalathu, S., Jeon J-S., Padgett, J. E., DesRoches, R., "ANCOVA-based Grouping of Bridge Classes for Seismic Fragility Assessment",  Engineering Structures, Vol. 123, 2016, pp 379-395.  (link).

95. Beyer, K., Mangalathu, S., “Numerical Study on the Peak Strength of Masonry Spandrels with Arches”, Journal of Earthquake Engineering, Vol. 18, No. 2, 2014.  (link).

96. Beyer, K., Mangalathu, S., “Review of Strength Models for Masonry Spandrels”, Bulletin of Earthquake Engineering, Vol. 11, No. 2, 2013.  (link).

97.  Sujith, M. S., Menon, D., Dodagowdar, G. R., “Reliability Analysis and Design of Cantilever  RC Retaining Walls against Sliding Failure”, International Journal of  Geotechnical Engineering, Vol. 5, No. 2 , 2011, pp 131 – 141. (link).

98. Mandali, A. K. Sujith, M. S., Rao, B. N., Janardhana Maganti., “Reliability Analysis of Counterfort Retaining Walls”,  Electronic Journal of Structural Engineering, Vol. 11, 2011, pp 42 – 56. (link).