首页|Studies from Florida State University Provide New Data on Machine Learning (Ense mble Learning Approach for Developing Performance Models of Flexible Pavement)
Studies from Florida State University Provide New Data on Machine Learning (Ense mble Learning Approach for Developing Performance Models of Flexible Pavement)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Tallahassee, Florida, by Ne wsRx editors, the research stated, “This research utilizes the Long- Term Pavemen t Performance database, focusing on devel-oping a predictive model for flexible pavement performance in the Southern United States.” The news journalists obtained a quote from the research from Florida State Unive rsity: “Analyzing 367 pavement sections, this study investigates crucial factors influencing asphaltic concrete (AC) pavement deterioration, such as structural and material components, air voids, compaction density, temperature at laydown, traffic load, precipitation, and freeze-thaw cycles. The objective of this study is to develop a predictive machine learning model for AC pavement wheel path cr acking (WpCrAr) and the age at which cracking initiates (WpCrAr) as performance indicators. This study thoroughly investigated three ensemble machine learning m odels, including random forest, extremely randomized trees (ETR), and extreme gr adient boosting (XGBoost). It was observed that XGBoost, optimized using Bayesia n methods, emerged as the most effective among the evaluated models, demonstrati ng good predictive accuracy, with an R2 of 0.79 for WpCrAr and 0.92 for AgeCrack and mean absolute errors of 1.07 and 0.74, respectively.”
Florida State University, Tallahassee, F lorida, United States, North and Central America, Cyborgs, Emerging Technologies , Machine Learning
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