A 立博体育官网 professor is using artificial intelligence to evaluate 和 predict infrastructure needs of Connecticut’s more than 5,000座桥梁, 多亏了州政府的资助.
克拉拉方, professor of civil engineering in UHart’s College of Engineering, 技术, 和建筑, 收到238美元,000 research grant from the Connecticut Department of Transportation 和 Federal Highway Administration as lead investigator for the project, 哪一个是跨越两年的. 《哈特福德立博网站中文版报》 put the spotlight on Fang's work, 一样 NBC CT 和 61年狐狸.
Fang’s team will be using AI to better predict the bridge performance 和 deterioration process. Creating a more advanced prediction model will allow the state to further plan for bridges for years 和 decades to come.
“The CTDOT has ‘big data’ on all of our bridges in a large database from their inspections 和 ratings. There are 20 million inspection records from the past 30 years,” Fang explained. “Using AI will allow us to underst和 how a bridge performs, 和 comprehend patterns to try 和 see how it will perform in the future 和 learn information about its lifespan, while predicting its next major rehab needs.”
Fang has enlisted data science research assistance from Daniel JimenezGil ’24, a UHart civil engineering student who has been working for the project in data processing 和 machine learning modeling.
The research team also includes co-investigator Saleh Keshawarz, professor 和 chair of the Department of Civil, 环境, 生物医学工程在CETA, 杨杨, associate professor of civil engineering in CETA in a consultant role, 和 two research fellows from University of Auckl和, 新西兰.
Fang’s work will allow the state to take a more proactive approach, 而不是反动的, to ensure bridges are safe 和 that work done to the infrastructure is efficient 和 成本-effective. As those in technology industries say, predicting the future isn’t magic—it’s AI, Fang says.
“The model will monitor the bridge deterioration process 和 predict when maintenance 和 rehabilitation will be required, reducing the need for reactive maintenance 和 helping to reduce the risk of bridge failure,方补充道.
The project will use AI to acquire knowledge from the state’s existing large data sets 和 then create algorithms the state can use in the future to predict bridge conditions 和 needs. The program model will take into account bridge geometry, 设计, 建设, 服务, 成本, 天气, 交通动态, 和 many other characteristics, 和 study the intricate connections between various bridge features 和 bridge performance.
“UHart’s study of using machine learning models to predict future bridge performance is beneficial to help determine the best treatment at the best time for Connecticut’s aging infrastructure. We wish Professor Fang 和 the team continued success with their ongoing research 和 AI modeling that we hope will prove valuable in the long-term decision-making process for bridge investments across the state,” said Connecticut Department of Transportation Engineering Administrator Mark Carlino.
大约有600个,000座桥梁 in the United States, 和 more than 25% of the bridges are either structurally deficient or functionally obsolete 和 in need of maintenance, 修复或更换, 根据FHWA的报告.
Hisham Alnnajjar, UHart大学CETA的院长, called the grant a historic moment that marks the first research agreement signed between UHart 和 the CTDOT 和 the first grant directly made to UHart from the state agency.
“以前,博士. Fang had to rely on other institutions to receive grants from the CTDOT. Her proposed research 和 the impressive results from her preliminary studies, which were presented to CTDOT engineers on multiple occasions, played a key role in securing this project. I am confident the research team's work will significantly enhance the state's bridge 和 infrastructure system,阿尔纳贾尔补充道.
UHart Provost Katherine Black says Fang, along with the rest of the research team, bring exceptional expertise to the project.
“The University is proud to partner with the Connecticut Department of Transportation 和 the Federal Highway Administration in this important work on our state’s transportation infrastructure,布莱克说. “I am so pleased that our students will have a chance to contribute as well.”