Abstract: An ultra-high excavated slope in Shenzhen has the characteristics of long excavation duration, complex construction process and variable climatic conditions, the prediction of slope deformation trend can provide a basis for the study of the overall stability of the slope. Combined with the design, geological data and on-site construction conditions, the orthogonal test method was used to sort out the database of geotechnical parameters and deformation trend of the excavated slope. Through the deep learning of the model, the geotechnical parameters of the slope are inverted, the elastic modulus is 115.6MPa, the cohesion is 28kPa, and the friction angle is 22°, and the finite element deformation analysis software is used to predict the deformation value of the next step excavation of the slope, and the relative error between the predicted deformation value and the actual monitoring deformation value is 6%~20%, indicating that the prediction model has high accuracy.

Key words: Excavated Slope, Machine Learning, Deformation Prediction, Numerical Simulation