The new roadway design comprises of one-half major thoroughfare, conventional drainage, a 600-ft long bridge over Willow Fork Bayou, Retaining walls and intersection improvements at FM 1463 (including traffic signals and illumination).
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 performed preliminary drainage area delineations for nine creek crossings and calculated approximate 100-year flows for each culvert crossing. Culvert structures were sized for each of the six crossings, ranging from 48” round pipe culverts up to dual 5’x5’ box culverts.
EHRA was selected as one of two firms to provide professional surveying services under contract to Houston Community College System.
EHRA completed preliminary engineering, phase one environmental site assessment and schematic development for the widening of Northpark Dr. between US 59 and Woodland Hills Dr. EHRA also provided program management, drainage analysis and design, traffic engineering, environmental documentation and schematic design for the roadway, as well as grade separation at the Loop 494/UPRR railroad crossing.
EHRA conducted traffic operations and access management studies for the Northpark Dr. corridor. This corridor is approximately 2.2 miles long and has major signalized and unsignalized intersections and driveways that access various subdivisions and industrial developments. These studies laid the groundwork for the widening of Northpark Dr. from a four-lane boulevard cross-section to a six-lane boulevard complete street. The new street design includes low impact development drainage, conventional drainage, a grade separation at the UPRR crossing with mechanically stabilized earth retaining walls, two at-grade crossings for bi-directional frontage access, reconstruction of two concrete bridges over a diversion channel, intersection improvements, a roadway-adjacent multiuse path and traffic signal improvements.
Drainage analysis and design included hydrologic and hydraulic studies of both existing and proposed conditions to demonstrate that proposed project components would not adversely affect the 100-year floodplain in the area. The roadway and traffic designs contained horizontal and vertical alignments, cross-sections, plan and profile, sidewalk and bicycle accommodations, intersection layouts, traffic control plans and signing and pavement markings.
As the program management firm, EHRA coordinated with TxDOT, UPRR, the City of Houston Council District E, COH Planning and Development Department, COH Public Works and Engineering Department, Montgomery County, Harris County, HCFCD and area residents throughout the project.
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