首页|Research Results from Georgia Institute of Technology Update Understanding of Ma chine Learning (Ensemble Machine Learning Classification Models for Predicting P avement Condition)
Research Results from Georgia Institute of Technology Update Understanding of Ma chine Learning (Ensemble Machine Learning Classification Models for Predicting P avement Condition)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Atlanta, Georgia, by NewsRx correspondents, research stated, "Forecasting pavement performance condi tion is essential within the pavement management system to optimize decisions wi th regard to planning maintenance and rehabilitation projects. Accurate forecast s facilitate timely interventions and assist in formulating cost-effective asset management plans." The news correspondents obtained a quote from the research from Georgia Institut e of Technology: "Data-driven machine learning models that utilize historical da ta to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regressionbased models to f orecast pavement condition, transportation asset management often operates accor ding to condition index ranges rather than exact values. Therefore, classificati on models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on d eveloping five machine learning classification models to predict pavement condit ion: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classi fication models are developed using a dataset from the Georgia Department of Tra nsportation that documented the condition of asphalt pavements for predefined ma intenance sections between 2017 and 2021. A voting ensemble model constructed wi th the two bestperforming individual classification models reached the highest accuracy rate at 83%."
Georgia Institute of TechnologyAtlantaGeorgiaUnited StatesNorth and Central AmericaCyborgsEmerging Technolog iesFinanceMachine Learning
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