Researchers at the Massachusetts Institute of Technology have developed a computational model that makes sense of the ambient vibrations that travel up a structure as trucks and other forces rumble by. By picking out specific features in the noise that give indications of a building’s stability, the model may be used to continuously monitor a building for signs of damage or mechanical stress.

“The broader implication is, after an event like an earthquake, we would see immediately the changes of these features, and if and where there is damage in the system,” said Oral Buyukozturk, a professor in MIT’s Department of Civil and Environmental Engineering (CEE).

The team tested its computational model on MIT’s Green Building — a 21-story research building made completely from reinforced concrete. The building was designed in the 1960s by architect and MIT alum I.M. Pei ’40, and stands as the tallest structure in Cambridge, Massachusetts.

In 2010, Toksöz and a team at MIT worked with the United States Geological Survey to outfit the Green Building with 36 accelerometers that record vibrations and movements on selected floors, from the building’s foundation to its roof.

To create an "embedded nervous system" for the structure, the team first built a computer simulation of the Green Building, in the form of a finite element model — a numerical simulation that represents a large physical structure, and all its underlying physics, as a collection of smaller, simpler subdivisions.

The researchers built a high-fidelity finite element model of the Green Building, then plugged various parameters into the model, including the strength and density of concrete walls, slabs, beams, and stairs in each floor.

To more accurately predict a building’s response to ambient vibrations, the group mined data from the Green Building’s accelerometers, looking for essential features that correspond directly to a building’s stiffness or other indicators of health.

As the model is designed, a user can introduce an excitation in the simulation — for example, a truck-like vibration — and the model would predict how the building and its various elements should respond.

“I would envision that, in the future, such a monitoring system will be instrumented on all our buildings, city-wide,” said researcher and lead author of the study Hao Sun. “Outfitted with sensors and central processing algorithms, those buildings will become intelligent, and will feel their own health in real time and possibly be resilient to extreme events.”

Source