The recent development of machine learning (ML) and Deep Learning (DL) increases the opportunities in all the sectors. ML is a significant tool that can be applied across many disciplines, but its direct application to civil engineering problems can be challenging. ML for civil engineering applications that are simulated in the lab often fail in real-world tests. This is usually attributed to a data mismatch between the data used to train and test the ML model and the data it encounters in the real world, a phenomenon known as data shift.
However, a physics-based ML model integrates data, partial differential equations (PDEs), and mathematical models to solve data shift problems. Physics-based ML models are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear equations. Physics-based ML, which takes center stage across many science disciplines, plays an important role in fluid dynamics, quantum mechanics, computational resources, and data storage. Below is a Federal Government funded research working with PhD Graduate students to a new type of concrete now being used around the country.
Performing strain pattern modeling and visualizations using StrainNet, FlowNet & the Digital Image Correlation system to employ statistical analysis and hypothesis testing on the concrete beams to calculate & predict optical flow displacement & tensile strength in order to verify significant superiority.