• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Friday, August 22, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Using AI to better assess structural health of bridges

Bioengineer by Bioengineer
August 31, 2020
in Science News
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

UTA researcher combining machine learning, structural health monitoring for bridges

IMAGE

Credit: UT Arlington

A civil engineering assistant professor at The University of Texas at Arlington is working to better assess a bridge’s structural health by combining machine learning with traditional monitoring measurements.

Suyun Ham’s 18-month, $122,000 grant is part of UTA’s membership in the Transportation Consortium of South-Central States (Tran-SET), a U.S. Department of Transportation Center administered by Louisiana State University. He will test his models in Dallas and Fort Worth.

On bridges, weight-in-motion systems include sensors that measure vibrations, strain and deflection. By measuring the bridge’s response to these elements, they can estimate the gross vehicle weights of passing vehicles and their effects on a bridge’s structural health. What the sensors don’t take into account, however, are different types of trucks, multiple lanes, times of day and how heavy traffic is.

Since weight-in-motion sensors are often already in place, Ham is trying to create a system by which these traditional measurements of structural health can be refined through machine learning. With the resulting data, transportation departments could set more accurate load parameters for bridges and get a better picture of a structure’s overall integrity.

“We are combining a physics-based model with artificial intelligence, because the more a computer learns, the better information you get,” Ham said. “Ultimately, the addition of machine learning allows us to accurately determine multiple conditions.”

Ham is also working on related research with a Texas Department of Transportation grant to use a non-contact testing system to make faster, easier and more accurate determinations about when and where bridge repairs are needed.

“Dr. Ham has embraced state-of-the-art technology in his study of bridge health monitoring, and this new study has potential for wide-ranging changes for the better in how state and federal transportation departments determine the integrity of the bridges we use daily,” said Ali Abolmaali, chair of the Civil Engineering Department.

###

Tran-SET supports all phases of research, technology transfer, workforce development and outreach activities of emerging technologies that can solve transportation challenges in the region. Its focus is on improving transportation infrastructure through research into innovative materials and new technology.

In addition to Louisiana State University and UTA, consortium members include the University of New Mexico, Texas A&M, New Mexico State University, Oklahoma State University, Arkansas State University, UT San Antonio, Prairie View A&M and two community colleges.

– Written by Jeremy Agor, College of Engineering

Media Contact
Herb Booth
[email protected]

Original Source

https://www.uta.edu/news/news-releases/2020/08/28/ham-bridges

Tags: Civil EngineeringRobotry/Artificial IntelligenceTechnology/Engineering/Computer Science
Share12Tweet8Share2ShareShareShare2

Related Posts

Scientists Unveil Breakthrough Technique for Large-Scale Metabolite Analysis in Biological Samples

Scientists Unveil Breakthrough Technique for Large-Scale Metabolite Analysis in Biological Samples

August 22, 2025
Metabolic Profiling Reveals RCC Drug Response

Metabolic Profiling Reveals RCC Drug Response

August 22, 2025

Electrochemical Hybrid Flow Cell Captures CO2 Directly

August 22, 2025

CrAAVe-seq reveals key neuronal genes in vivo

August 22, 2025
Please login to join discussion

POPULAR NEWS

  • blank

    Molecules in Focus: Capturing the Timeless Dance of Particles

    141 shares
    Share 56 Tweet 35
  • New Drug Formulation Transforms Intravenous Treatments into Rapid Injections

    114 shares
    Share 46 Tweet 29
  • Neuropsychiatric Risks Linked to COVID-19 Revealed

    81 shares
    Share 32 Tweet 20
  • Modified DASH Diet Reduces Blood Sugar Levels in Adults with Type 2 Diabetes, Clinical Trial Finds

    60 shares
    Share 24 Tweet 15

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Scientists Unveil Breakthrough Technique for Large-Scale Metabolite Analysis in Biological Samples

Metabolic Profiling Reveals RCC Drug Response

Electrochemical Hybrid Flow Cell Captures CO2 Directly

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.