April 29 | 1:30 PM KSA (UTC+3)

In every construction project, ground behaviour has a major impact on the final structural design. For this reason, soil characterisation is crucial. Traditionally, soil parameters are characterised by laboratory and in-situ testing. However, a high degree of uncertainty is associated to those tests.

Taking a step forward along the path of parameters estimation, there is a methodology, called backanalysis (also known as inverse analysis), with a strong potential to identify soil parameters based on the interaction of a conceptual model and the physical system. The conceptual model (i.e. PLAXIS model) is evaluated and compared with the physical system (i.e. IoT data) while changing the model's input parameters (soil parameters) until the model matches the physical system.

In this talk, we will introduce a novel solution called DAARWIN, which leverages machine learning algorithms to automate the backanalysis process. The presentation will showcase the application of DAARWIN in real construction projects, demonstrating its ability to effectively estimate soil parameters and reduce geotechnical uncertainty. Making construction industry a more sustainable, efficient, and safer industry.

The webinar consists of:

• Geotechnical Uncertainty
• Limitations of the Traditional Approach
• DAARWIN: Connecting PLAXIS Models with IoT Sensors
• Backanalysis
• Parametrical Analysis (Sensitivity)
• Parallel High-Performance Cloud-Computing
• Case Studies: Large Underground Excavations and Static Pile Load Testing
• Q&A Session

Take advantage of this opportunity to enhance your geotechnical analysis skills. Register now to secure your spot!

Register Now