A Picture of Structural Health

A Picture of Structural Health

A Picture of Structural Health


This article appeared originally in the Department of Civil and Environmental Engineering's 2018 alumni magazine, Civil Remarks. Find more stories at go.umd.edu/civilremarks

Yunfeng Zhang has a vision for structural health monitoring, and it embraces the same method that powers facial recognition and speech-to-text software. He’s created a model that ranks the integrity of steel seismic fuses from a single photo.

“You can’t out-strength an earthquake, but you can contain much of the damage to a single structural component that engineers can simply remove and replace,” said Zhang, a professor in the Department of Civil and Environmental Engineering. “Seismic fuses absorb the energy from the violent shaking of an earthquake so that the rest of the building structures remain undamaged.”

And his approach can be “trained” to recognize failings in other integral structural components too.

Zhang’s model is driven by deep learning—a machine learning method that uses layered algorithms to extract structured information from immense data sets. He and graduate student Heng Liu have since 2014 meticulously trained the algorithms to distinguish healthy fuses from ones needing repair using hundreds of thousands of labeled images depicting a range of damage.

“Deep learning requires millions of training data points. Instead of starting from scratch, we were able to refine existing computer vision algorithms, which significantly reduced the number of images we needed to effectively train the algorithms,” Zhang said.

The final model, which Zhang hopes to see incorporated into software available to building owners and government agencies, assigns fuses a score of 1-5, with five indicating severe damage.

“Our research shows that the model has a 95 percent success rate,” Zhang added. “Building owners and others could reliably use our model in place of time-intensive and costly visual inspections.”

Zhang and Liu have already begun to explore other applications for their deep learning-powered model. They have also trained the algorithms to recognize deficiencies in bridge components from acoustic data collected by a sensor system that directs high-frequency pulses at the bridge.

“Deep learning could transform how we monitor and maintain structures, saving millions of dollars and human lives,” Zhang said.

Related Articles:
How tech can fill gaps in mental health care
A new way to monitor mental health conditions
He Lab Taps Machine Learning to Improve Cell-Based Medicine
UMD Awarded $4.6 Million Rehabilitation Engineering Research Center
Maryland Engineers Open Door to Big New Library of Tiny Nanoparticles
Clark School faculty 'AIM-HI' to address major health challenges
Clark School Faculty Receive CAREER Awards
Fuge Receives NSF CAREER Award
AlphaGo family of AI programs grew from AMS simulation-based algorithms developed at UMD
Measuring Snow From Space

November 9, 2018


Prev   Next

Current Headlines

Alumnus David Bader Receives 2021 Sidney Fernbach Award

In Memoriam: John William Fritz (‘17, electrical engineering)

UMD research team creates ‘switchable’ adhesive for repairing cuts and tears in tissue

Torrents Awarded Ben Dyer Centennial Chair

Improving Access to Cervical Cancer Diagnostic and Therapeutics Tech

Kollár Receives NSF MRI Grant to Enhance Micro/Nanofabrication Initiatives 

UMD Mechanical Engineering Advances in Rankings

Liangbing Hu’s HighT-Tech Wins 2021 Spinoff Prize

News Resources

Return to Newsroom

Search News

Archived News

Events Resources

Events Calendar