PhD, Pennsylvania State University, 2023

BS, Oklahoma State University, 2017

BS, Southwest Jiaotong University, 2017

Soil Mechanics (CE 34500)

Soil Mechanics is a fundamental course in civil engineering that delves into the scientific principles and methodologies governing the physical properties, behavior, and performance of soil materials under various environmental and loading conditions. Students engage with concepts such as soil classification, permeability, compaction, consolidation, shear strength, and the effective stress principle, all of which are crucial for the design and analysis of foundations, earthworks, and retaining structures. Through a combination of theoretical understanding and practical laboratory experiments, this course equips aspiring engineers with the essential skills to assess and solve geotechnical problems, ensuring the stability and longevity of constructed infrastructure.

Peer-Reviewed Journal Publications:

• Pei, T., Qiu, T., and Shen, C. (2024). “Closure to “Applying Knowledge-Guided Machine Learning to Slope Stability Prediction”.” Journal of Geotechnical and Geoenvironmental Engineering, (Accepted).
• Li, Z., Pei, T., Ying, W., Zhang, R., Yoon, J., Dabo, I., Srubar, W., and Radlinska, A. (2024). “Can domain knowledge benefit machine learning for concrete property prediction?” Journal of the American Ceramic Society, 1-21.
• Pei, T., and Qiu, T. (2023). “Machine Learning with Monotonic Constraint for Geotechnical Engineering Applications: An Example of Slope Stability Prediction.” Acta Geotechnica.
• Pei, T., Qiu, T., and Shen, C. (2023). “Applying Physics-Guided Machine Learning to Slope Stability Prediction.” Journal of Geotechnical and Geoenvironmental Engineering, 149(10),04023089.
• Palese, M., Pei, T., Qiu, T., Zarembski, A. M., Shen, C., and Palese, J. W. (2023). “Hazard assessment framework for statistical analysis of cut slopes using track inspection videos and Geospatial Information.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 17(4), 771–786.
• Pei, T., and Qiu, T. (2022). “A Numerical Investigation of Laterally Loaded Steel Fin Pile Foundation in Sand.” International Journal of Geomechanics, 22(7), 04022102.
• Pei, T., and Qiu, T. (2022). “DEM Investigation of Energy Dissipation at Particle Contacts in Granular Soil Under Cyclic Torsional Shear.” International Journal of Geomechanics, 22(4), 04022016.
• Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S., Li, W., Nguyen, H., Qiu, T., Tran, S. and Shen, C. (2022). “Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, pp. 4349-4370.
• Pei, T., and Yang, X. (2018). “Compaction-Induced Stress in Geosynthetic-Reinforced Granular Base Course – A Discrete Element Model.” Journal of Rock Mechanics and Geotechnical Engineering, 10(4), 669–677.

Conference Publications/Presentations:

• Xiong, J., Pei, T., and Qiu, T. (2024). “Spatiotemporal prediction of rainfall-induced landslides using machine learning techniques.” Accepted for publication in Proc. of Geo-Shanghai International Conference 2024.
• Li, Z., Pei, T., Ying, W., Zhang, R., Yoon, J., Dabo, I., Srubar, W., and Radlinska, A. (2024). “Simulation-assisted transfer learning for concrete strength prediction.” in Proc. of 77th RILEM Annual Week and the 1st Interdisciplinary Symposium on Smart & Sustainable Infrastructures (ISSSI 2023).
• Pei, T., Liu, J., Shen, C., and Kifer, D. (2023). “Impact of Cross-Validation Strategies on Machine Learning Models for Landslide Susceptibility Mapping: A Comparative Study.” in Proc. AGU Fall Meeting Abstr., vol. 2023, 2023, Art. no. NH13D-0717.
• Pei, T., and Qiu, T. (2023). “Landslide susceptibility mapping in Colorado Front Range, USA: a comparison of physics-based and data-driven approaches.” Accepted for publication in Proc. of 8th International Conference on Debris Flow Hazard Mitigation.
• Palese, M., Pei, T., Zarembskia, A., Qiu, T., Shen, C., and Palese, J. (2023). “Risk assessment framework for statistical analysis of cut slopes using track inspection videos and satellite imagery.” in Proc. of 2023 Georisk Conference.
• Pei, T., and Qiu, T. (2023). “Landslide susceptibility mapping using machine learning methods: a case study in Colorado Front Range, USA.” in Proc. of 2023 Geo-Congress Conference.
• Xiong, J., Pei, T., and Qiu, T. (2023). “A machine learning-based method with integrated physics knowledge for predicting bearing capacity of pile foundations.” in Proc. of 2023 Geo-Congress Conference.
• Pei, T., Nagendra, S., Banagere Manjunatha, S., He, G., Kifer, D., Qiu, T., and Shen, C. (2021). “Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA.” in Proc. of EGU Gen. Assem. Conf. Abstr., 2021, Art. no. EGU21- 13974.
• Liu, J., Shen, C., Pei, T., Lawson, K., Kifer, D., Nagendra, S., and Manjunatha, B. (2021). “A new rainfall-induced deep learning strategy for landslide susceptibility prediction.” in Proc. of AGU Fall Meeting Abstr., vol. 2021, 2021, Art. no. NH35E-0504.
• Pei, T., Qiu. T., and Laman, J. (2020) “A numerical investigation of laterally loaded steel fin pile foundations.” in Proc. of 2020 Joint-Rail Conference, V001T08A012. New York: ASME.
• Pei, T., Nagendra, Manjunatha, B., S., He, G., Kifer, D., Qiu, T., and Shen, C. (2020). “Cloud-based interactive database management suite integrated with deep learning-based annotation tool for landslide mapping.” in Proc. AGU Fall Meeting Abstr., vol. 2020, 2020, Art. no. NH030-0011.
• Nagendra, S., Manjunatha, B., Shen, C., Kifer, D., Pei, T. (2020). “An efficient deep learning mechanism for cross-region generalization of landslide events.” in Proc. of AGU Fall Meeting Abstr., vol. 2020, 2020, Art. no. NH030-0010.

• Pei, Te (PI), and Tian, Yingli (co-PI). "Empowering Landslide Prediction and Community Resilience with Physics-Guided Knowledge Transfer." 2024 CUNY Interdisciplinary Research Grant, (2024-2025), $45,350, 50% to Pei.
• Pei, Te (PI). "Towards Reliable Data Science Applications in Landslide Prediction: Systematic Investigation into Data Dependencies to Create Widespread Community Awareness." PSC-CUNY Research Award Program, (2024-2025), $5,799.70.