Call for Site Investigation and Monitoring Data Contributions for AGS NSW Chapter’s Annual Prediction Event

Posted

The AGS NSW Chapter is excited to announce a new initiative under the ‘Numerical Methods and AI in Practice’ subcommittee: an Annual Prediction Event designed to advance the practical application of numerical methods in geotechnical engineering.

Call for Data Contributions

To make this initiative a success, we invite contributions of site investigation data and instrumentation and monitoring datasets from the geotechnical community. Your contributions will play a critical role in:

What We Are Looking For

We are seeking:

Data from a wide range of geotechnical projects—small, medium, or large—are welcome.

An example list of an ideal data set is given at the end of this document for a fictitious project.

How Your Data Will Be Used

Benefits of Contributing

By contributing your data, you will:

How to Contribute

If you have datasets that you would like to contribute or if you would like to discuss this initiative further, please contact Dr. Ali Parsa at [email protected]. Kindly include a brief description of the data you can provide and any specific conditions for its use.

Submission Deadline: 26 May 2025

We look forward to your support and participation in this exciting new initiative.

Example: Basement Excavation

The Annual Prediction Event aims to provide participants with a unique opportunity to simulate real-world geotechnical problems using numerical analysis. Participants will be supplied with site investigation data and tasked to predict the geotechnical behaviour of a problem. The models used, and their outputs and comparisons against actual monitoring results, will be collated to identify general and industry-wide common features, examples of what worked well and knowledge/technology gaps. This comparison, and showcase of different modelling approaches, will be a valuable learning exercise for the geotechnical community.

Warm regards,

AGS NSW Chapter
Subcommittee on Numerical Methods and AI in Practice