Recently, five research results published by Associate Professor Li Huajin at the School of Architecture and Civil Engineering in international authoritative academic journals of geoscience were selected as ESI highly cited papers (data from ESI database in March 2023). The five papers are Detection and segmentation of loess landslides via satellite images: a two-phase framework, SCADA Data Based Wind Power Interval Prediction Using LUBE-Based Deep Residual Networks, Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts, Short-Term Wind Power Prediction via Spatial Temporal Analysis and Deep Residual Networks, and Short-Term Nacelle Orientation Forecasting Using Bilinear Transformation and ICEEMDAN Framework.

These research results are focused on the hot areas of geotechnical engineering disaster mitigation and prevention and green building engineering respectively. Some of the research results focus on intelligent landslide identification and hazard prediction, analyzing the "textural features" of landslide image data, and constructing a model with the ability of accurate identification of loess landslides and intelligent extraction of geomorphological parameters based on AI algorithms and geometric theories. In addition, the research is based on deep learning and statistical learning methods for wind direction, wind power, wind efficiency and other issues, as well as monitoring and forecasting research.