Algorithm Leads to Two Patents

CEE faculty members were awarded patents recently for two innovations:  a significantly faster way to model the behavior of materials and an improved method for eye doctors to accurately measure intraocular pressure.

Both patents are based on an autoprogressive algorithm developed by CEE Professor Emeritus Jamshid Ghaboussi to determine the properties of materials by measuring the response of the structural system of which they are a part.  There are many situations in which engineers and others need to know the properties of a material, but some materials are difficult, if not impossible, to analyze, Ghaboussi said.  In many of those

cases, though, it is possible to measure the response of the system, he said.  He first developed the idea while working with CEE Professor Emeritus David Pecknold to model fiber-reinforced laminated composite materials.  It was not possible to test the lamina directly, but it was possible to perform experiments on a sample of the composite material and use the autoprogressive algorithm method to determine the lamina’s properties.

Using the same method, Ghaboussi later collaborated with Pecknold and CEE Professor Youssef Hashash to develop the new material modeling system, U.S. Patent No. 7,447,614, “Methods and systems for modeling material behavior.”  CEE alumnus Tae-Hyun Kwon (MS 02, PhD 06) collaborated with the three to apply the algorithm to the problem of accurately measuring eye pressure, U.S. Patent No. 8,070,679 B2, “Method for accurate determination of intraocular pressure and characterization of mechanical properties of cornea.”

Any problem that requires a characterization of material behavior can be solved efficiently using the algorithm, which enables a direct link between the physical and the simulation that was never before possible, said Hashash.

“This new approach truly allows us to connect the physical with the computational, so that you can do much fewer tests and get a direct algorithm that will give you the material behavior,” Hashash said. “There’s a significant savings in that.  Instead of having to do many tests to properly characterize the material behavior, you do far fewer—maybe even as little as one.  With this approach, the minute you finish with the experiment, your model is ready.”

The eye pressure patent is an example of the method’s broad potential. The team used the same algorithm to develop the new method for determining intraocular pressure (IOP)—an important indicator of eye health and disease progression—and determining the material properties of the cornea, which can help optimize the results of laser surgery. 

The new patents demonstrate just two of the autoprogressive algorithm method’s applications in engineering and medicine—the tip of the iceberg in terms of its versatility, Ghaboussi said.

“The method has important applications in civil engineering,” Ghaboussi said. “In collaboration with Professor Hashash, we extended the method and applied it in determining the soil properties in deep excavations in urban areas, where ground movements need to be controlled to protect the adjacent buildings.”

The algorithm has also proved useful in the development of new, more comprehensive soil testing methods, and in modeling the cyclic behavior of beam-column connections in buildings subjected to earthquake ground motions.  Ghaboussi is currently working to apply the method to model the components of complex adaptive systems, such as socioeconomic and biological systems.  He is also collaborating with Professor Michael Insana from the Department of Bioengineering to apply the first patent to palpation imaging in breast cancer diagnosis.