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SCHOLARLY WORK

Journals and Conference Publications

Magazine Stack

Sample Journal Publications:

  • Soleimani, F., Hajalizadeh, D. (2022, April). Bridge Seismic Hazard Resilience Assessment with Ensemble Machine Learning. In Structures (Vol. 38, pp. 719-732). Elsevier. (https://doi.org/10.1016/j.istruc.2022.02.013). 

  • Soleimani, F., Hajalizadeh, D. (2022). State-of-the-Art Review on Probabilistic Seismic Demand Models of Bridges: Machine-Learning Application. Infrastructures, 7, 64. (https://doi.org/10.3390/infrastructures7050064)

  • Soleimani, F. (2022, April). Probabilistic seismic analysis of bridges through machine learning approaches. In Structures (Vol. 38, pp. 157-167). Elsevier. (https://doi.org/10.1016/j.istruc.2022.02.006)

  • Soleimani F, Liu X. (2021). Artificial neural network application in predicting probabilistic seismic demands of bridge components. Earthquake Engng Struct Dyn. 2021;1–18. (https://doi.org/10.1002/eqe.3582)

  • Soleimani, F. (2021). Analytical seismic performance and sensitivity evaluation of bridges based on random decision forest framework. In Structures (Vol. 32, pp. 329-341). Elsevier. (https://doi.org/10.1016/j.istruc.2021.02.049)

  • Macedo, J., Liu, C., Soleimani, F. (2021). Machine-learning-based predictive models for estimating seismically-induced slope displacements. Soil Dynamics and Earthquake Engineering, 106795. (https://doi.org/10.1016/j.soildyn.2021.106795)

  • Soleimani, F. (2021). Pattern Recognition of the Seismic Demands for Tall Pier Bridge Systems. Journal of Earthquake Engineering, 1-19. (https://doi.org/10.1080/13632469.2021.1927894)

  • Soleimani, F. (2020). Propagation and quantification of uncertainty in the vulnerability estimation of tall concrete bridges. Engineering Structures, 202, 109812. (https://doi.org/10.1016/j.engstruct.2019.109812)

  • Soleimani, F., Mangalathu, S., DesRoches, R. (2017). A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes. Engineering Structures, 153, 460-478. (https://doi.org/10.1016/j.engstruct.2017.10.036)

  • Mangalathu, S., Soleimani, F., Jeon, J. S. (2017). Bridge classes for regional seismic risk assessment: Improving HAZUS models. Engineering Structures, 148, 755-766. (https://doi.org/10.1016/j.engstruct.2017.07.019)

  • Soleimani, F., Vidakovic, B., DesRoches, R., Padgett, J. (2017). Identification of the significant uncertain parameters in the seismic response of irregular bridges. Engineering Structures, 141, 356-372. (https://doi.org/10.1016/j.engstruct.2017.03.017)

  • Soleimani, F. (2017). Fragility of California bridges-development of modification factors (Doctoral dissertation, Georgia Institute of Technology).

  • Soleimani, F., McKay, M., Yang, C. S. W., Kurtis, K. E., DesRoches, R., Kahn, L. F. (2016). Cyclic testing and assessment of columns containing recycled concrete debris. ACI Structural Journal, 113(5), 1009. (https://doi.org/10.14359/51689024)

Sample Conference Proceedings:

  • Liu, C., Macedo, J., Soleimani, F. (2022) Using Machine Learning for the Performance-based Seismic Assessment of Slope Systems. In Geo-Congress 2022 (pp. 649-658). (https://doi.org/10.1061/9780784484043.063)

  • Soleimani, F., Macedo, J., Liu, C. (2022, February). Machine Learning-based Selection of Efficient Parameters for the Evaluation of Seismically-Induced Slope Displacements. ASCE Lifelines Conference. (https://doi.org/10.1061/9780784484449.017)

  • Aedo Maluje, S., M ́alaga-Chuquitaype, Ch., Macedo, J., Soleimani, F. (2021). Efficiency of Intensity Measures for Seismic Response Prediction in CLT Buildings via Data Science Methods. World Conference on Timber Engineering.

  • Soleimani, F., Mangalathu, S., and DesRoches, R. (2017). Seismic Resilience of Concrete Bridges with Flared Columns. Procedia engineering, 199, 3065- 3070. (https://doi.org/10.1016/j.proeng.2017.09.417)

  • Soleimani, F., Yang, C. S. W., and DesRoches, R. (2017). The Effect of Superstructure Curvature on the Seismic Performance of Box-Girder Bridges with In-Span Hinges. In Structures Congress 2017 (pp. 469-480). (https://doi.org/10.1061/9780784480403.040)

  • Mangalathu, S., Soleimani, F., Jiang, J., DesRoches, R., and Padgett, J. E. (2017). Sensitivity of fragility curves to parameter uncertainty using Lasso regression. In Proceedings of the 16th world conference on earthquake engineering, Santiago de Chile, Chile, Paper (Vol. 135).

  • Mangalathu, S., Jeon J-S., Soleimani, F., DesRoches, R., Padgett, J., and Jiang, J. (2015). Seismic vulnerability of multi-span bridges: An analytical perspective. In 10th Pacific Conference on Earthquake Engineering.

Conference presentation
College Lecture

Learning Analytics Research Collaborations:

  • Soleimani, F., Lee, J., & Yilmaz Soylu, M. (2022). Analyzing learners engagement in a micromasters program compared to non-degree MOOC. Journal of Research on Technology in Education, 1-15. (https://doi.org/10.1080/15391523.2022.2121342)

  • Soleimani, F., Lee, J., Yilmaz Soylu, M., & Chatterjee, S. (2022, June). Influential Text-Based Features in Predicting Admission Status of Online Degree Applicants. In Proceedings of the Ninth ACM Conference on Learning@ Scale (pp. 360-363). (https://doi.org/10.1145/3491140.3528318)

  • Lee, J., Soleimani, F., Hosmer IV, J., Soylu, M. Y., Finkelberg, R., & Chatterjee, S. (2022). Predicting Cognitive Presence in At-Scale Online Learning: MOOC and For-Credit Online Course Environments. Online Learning, 26(1). (https://doi.org/10.24059/olj.v26i1.3060)

  • Lee, J., Soleimani, F., & Harmon, S. W. (2022). Reflecting on a Year of Emergency Remote Teaching. In Global Perspectives on Educational Innovations for Emergency Situations (pp. 169-178). Springer, Cham.

  • Lee, J., Soleimani, F., & Harmon, S. W. (2021). Emergency Move to Remote Teaching: A Mixed-Method Approach to Understand Faculty Perceptions and Instructional Practices. American Journal of Distance Education, 35(4), 259-275. (https://doi.org/10.1080/08923647.2021.1980705)

  • Soleimani, F., & Lee, J. (2021, June). Comparative Analysis of the Feature Extraction Approaches for Predicting Learners Progress in Online Courses: MicroMasters Credential versus Traditional MOOCs. In Proceedings of the Eighth ACM Conference on Learning@ Scale (pp. 151-159). (https://doi.org/10.1145/3430895.3460143)

  • Staudaher, S., Lee, J., & Soleimani, F. (2020, August). Predicting Applicant Admission Status for Georgia Tech's Online Master's in Analytics Program. In Proceedings of the Seventh ACM Conference on Learning@ Scale (pp. 309-312). (https://doi.org/10.1145/3386527.3406735)

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