Observational Method & Machine Learning Back-analysis - Data-driving design solutions in Ground Engineering

Synopsis

In Infrastructure and underground construction works, uncertainties from ground, groundwater, and construction led to inaccurate predictions of soil-structure interactions. Supported excavations are often over-designed, which underscores a significant potential for cost optimization and sustainable design. However, the conventional design approach has less room for leaner designs at the start of the project due to uncertainties. The emergence of advanced analysis tools enables the development of an Observational Method approach for a decision-making process, in which data can be best utilized to deliver real value, confidence, and control.

More digital solutions for ground engineering become available, such as Digital Twin, Big Data, Automation, and AI-assisted design, as well as newly developed tools, e.g., cloud-based machine learning algorithm method supported back analysis platforms, and monitoring data platforms. An integrated digital solution framework will significantly improve the efficiency of the project, connecting design with construction, as well as operation (e.g., asset management).…