Table of Contents
Wilson Tang Lecture
| Geological Uncertainty: A Missing Element in Geotechnical Reliability Analysis | 1 – 12 |
Suzanne Lacasse Lecture
| Managing Risk in Geotechnical Engineering – from Data to Digitalization | 13 – 34 |
Keynote Lectures
| Challenges in Determining Rock Mass Properties for Reliability – Based Design | 35 – 44 |
| Quantitative Risk Assessment of Individual Landslides | 45 – 54 |
| 55 – 61 | |
| Honing Safety and Reliability Aspects in the Evolution of Eurocode 7 | 62 – 62 |
| Bayesian Perspective on Ground Property Variability for Geotechnical Practice | 63 – 74 |
| Evaluating Metropolitan Hazard Risks under Extreme Rainstorms | 75 – 85 |
ISSMGE Bright Spark Lectures
| Values of Monte Carlo Samples for Geotechnical Reliability – Based Design | 86 – 95 |
| Influence of Spatially Variable Soil Permeability on Backward Erosion Piping | 96 – 101 |
| Sparse Modeling in Geotechnical Engineering | 102 – 111 |
Honour and Memory of Professor Tien H. Wu
| Special Session in Honour and Memory of Professor Tien H. Wu | 112 – 113 |
Mini-Symposium on Performance-Based Design Codes and Practice Honouring Prof. Yusuke Honjo
Statistics for Soil & Rock Properties and Applications
Spatial Variability: Modelling Spatial Variability in Geotechnical Engineering & Numerical Techniques for Integrating the Spatial Variability of Soil and Groundwater Parameters into Designing and Environmental Management
Probabilistic Site Characterization
Design: Advances in Geotechnical Reliability – Based Design & Robust Geotechnical Design in the Face of Uncertainty
Uncertainty & Reliability Analysis in Rock Engineering
Machine Learning for Big Data: Algorithms and Applications
| Bayesian Learning of Gaussian Mixture Model of Geotechnical Data | 547 – 552 |
| Bayesian Learning of Site – Specific Spatial Variability Using Sparse Geotechnical Data | 553 – 558 |
| Deep Neural Networks for Prediction of Undrained Shear Strength of Clays | 559 – 564 |
| Predicting Land Subsidence by Combining In Situ Testing and Remote Sensing Data | 565 – 570 |
| Data – Fusion Based Vulnerability Analysis of Shield Driven Tunnel Suffering from Extreme Soil Surcharging | 571 – 576 |
| Study on Optimization of Mars Model for Prediction of Pile Drivability Based on Cross – Validation | 577 – 582 |
| Similarity Measure for Soil Properties between Different Sites | 583 – 588 |
Bayesian Methods: Bayesian Method for Processing Geotechnical Data & Bayesian Updating: Formalizing the Observational Method
Inverse Analysis in Geotechnical Engineering
Landslides: Landslide Risk Assessment and Management & Risk Assessment of Rainfall-Induced Geo-Hazards
Dams, Levees and Flood Risk
Seismic Hazard and Seismic Performance: Probabilistic Seismic Hazard Assessment and Engineering Seismology & Effect of Spatial Variability on Seismic Performance of Soil and Rock and Associated Reliability
Engineering Risk Sensing by Monitoring and Inspection
| Dynamic Soil Parameters Updating and Response Prediction with Multi – Sensor Monitoring Information by WSN | 876 – 881 |
| Application of Wireless Sensing in Shanghai Utility Tunnel | 882 – 887 |
| On – Site Visualization as a New Strategy for Geotechnical Safety and Risk Management | 888 – 893 |
| Performance Evaluation Method for Slope Monitoring System Based on On – Site Visualization | 894 – 899 |
| On – Site Visualization for Risk Management of Mountain Tunnel Construction | 900-904 |

