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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. (

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

  • Soleimani, F. (2022, April). Probabilistic seismic analysis of bridges through machine learning approaches. In Structures (Vol. 38, pp. 157-167). Elsevier. (

  • Soleimani F, Liu X. (2021). Artificial neural network application in predicting probabilistic seismic demands of bridge components. Earthquake Engng Struct Dyn. 2021;1–18. (

  • 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. (

  • Macedo, J., Liu, C., Soleimani, F. (2021). Machine-learning-based predictive models for estimating seismically-induced slope displacements. Soil Dynamics and Earthquake Engineering, 106795. (

  • 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. (

Sample Conference Proceedings:

  • Soleimani, F., Hajalizadeh, D. (2022). Predictive Models of Bridge Seismic Demands: Application of Machine Learning. SEI-ASCE Structures Congress.

  • 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). (

  • 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.

  • 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. (

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. (

  • 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). (

  • 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). (

  • 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. (

  • 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). (

  • 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). (

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