# 873018 Artificial Intelligence in Geotechnical Engineering

Type
Lecture and exercise
Semester hours
2
Lecturer (assistant)
Soranzo, Enrico
Organisation
Offered in
Wintersemester 2021/22
Languages of instruction
Deutsch

Content

1. Introduction to Artificial Intelligence (AI)
1.1 General description of AI
1.2 Main definitions: Machine Learning, Deep Learning, Reinforcement Learning
1.3 Subsets of the algorithms: Classification/Regression, Supervised/Unsupervised Learning
1.4 Methodology: Train/Test, cross-validation
1.5 Performance: performance metrics, overfitting

2. AI application in Geotechnical Engineering
2.1 Parameter correlation: shear parameters, saturated and partially saturated soils, undrained cohesion, compressibility index, SWCC, Proctor curve, permeability coefficient
2.2 Soil classification based on in-situ tests: SPT, CPT, DLM
2.3 Safety prediction: overall stability of (reinforced/anchored) slopes, tunnel face stability, bearing capacity of shallow and deep foundations, liquefaction, internal forces in tunnel lining
2.4 Displacement prediction: rotations in retaining walls, tunnel settlement
2.5 Machine performance: TBM excavation speed
2.6 Constitutive models

3. Computational implementaion of AI
3.1 Crash Course on AI with Python: installation, first steps
3.2 Data collection for geotechnical engineering: literature data, test data, surrogate models, machines
3.3 Relevant AI-Algorithms for geotechnical engineering: Artificial Neural Networks, Support Vector Machine, etc.
3.4 Joint handling of example applications for geotechnical engineering
3.4.1. Parameter correlation
3.4.2 Soil classification
3.4.3 Overall stability
3.4.4 Displacement prediction
3.4.5 Machine performance

4. Homework project
4.1 Project introduction: Data source and analysis objective
4.2 Content description of the individual project

Previous knowledge expected

Basics of Geomechanics and Geotechnical Engineering, Basics of statistics and scientific programming

Objective (expected results of study and acquired competences)

1. After this course, the students will be able to list the various applications of AI in geotechnical engineering
2. They will be able to describe the various AI techniques, their pros and cons.
3. After this course, the students will be able to independently apply these AI techniques to geotechnical engineering problems
4. They will acquire the basics of Python programming for data science with a special focus to geotechnics thanks to an individual projekt
5. They will manage to collect and preprocess geotechnical data
6. Based on these data, they will train, test and improve the performance of the AI algorithms.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.