Identified as a top priority during the development of the District’s Parks Master Plan, this portion of trail was the first phase of over two miles of planned trails to provide connectivity and recreation for District residents.
In 2006, Caldwell Companies sought to create Towne Lake as a community where residents and services could be connected by water. Their vision included boat docks and marinas augmenting traditional walking trails to navigate a vibrant residential community. EHRA was the perfect partner to take Caldwell Companies’ vision and create this livable suburban oasis.
EHRA worked with the District to create a comprehensive Parks Master Plan, which included recommendations for the development of over two miles of hike/bike trails adjacent to local streets, and within flood control and utility pipeline easements. The District began implementation of the Plan by prioritizing the beautification of West Road, a major arterial street that runs through the District.
EHRA was selected by the client to provide engineering design and to serve as District Engineer for the 2,400 acre Towne Lake Development. Our survey department retraced the overall boundary and performed a topographic survey of the site.
EHRA was selected as one of two firms to provide professional surveying services under contract to Houston Community College System.
Researchers at the University of Waterloo have found a better way to identify atomic structures, an essential step in improving materials selection in the construction industry among others. The findings of the study could result in greater confidence when determining the integrity of metals. Devinder Kumar, a PhD candidate in systems design engineering at Waterloo, collaborated with the Fritz Haber Institute (FHI) in Berlin, to develop a powerful AI model that can accurately detect different atomic structures in metallic materials. The system can find imperfections in the metal that were previously undetectable.
FHI came up with a new scenario that can artificially create data which relates to the real world. Kumar along with his collaborators was able to use this to generate about 80,000 images of the different kind of defects and displacements to produce a very effective AI model to identify various types of crystal structures in practical scenarios. This data has been released to the public so people can actually learn their own algorithms. In theory, all metallic materials have perfect symmetry, and all the items are in the correct place, but in practice because of various reasons such as cheap manufacturing there are defects. All these current methods fail when they try to match actual ideal structures, most of them fail when there is even one per cent defect. Thus, they have made an AI-based algorithm or model that can classify these kinds of symmetries even up to 40 per cent of defect.
Story Source: University of Waterloo via Science Daily