首页|New Machine Learning Study Results Reported from School of Civil Engineering (Machine Learning and Statistical Test-Based Culvert Condition Impact Factor Analysis)
New Machine Learning Study Results Reported from School of Civil Engineering (Machine Learning and Statistical Test-Based Culvert Condition Impact Factor Analysis)
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NETL
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Hindawi Publishing Group
Fresh data on artificial intelligence are presented in a new report. According to news originating from the School of Civil Engineering by NewsRx editors, the research stated, "For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public." Funders for this research include Key Area Dedicated Project of Guangdong General Universities And Colleges. The news reporters obtained a quote from the research from School of Civil Engineering: "The efficiency of the cost- and labor-intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann- Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials." According to the news editors, the research concluded: "In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development."
School of Civil EngineeringCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Hindawi Publishing Group
