首页|Study Findings on Machine Learning Discussed by a Researcher at Tongji Universit y (Assessment of Factors Affecting Pavement Rutting in Pakistan Using Finite Ele ment Method and Machine Learning Models)
Study Findings on Machine Learning Discussed by a Researcher at Tongji Universit y (Assessment of Factors Affecting Pavement Rutting in Pakistan Using Finite Ele ment Method and Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Shanghai, Peop le's Republic of China, by NewsRx editors, research stated, "This study research es environmental factors, vehicle dynamics, and loading conditions on pavement s tructures, aiming to comprehend and predict their impact." Our news journalists obtained a quote from the research from Tongji University: "The susceptibility of asphalt pavement to temperature variations, vehicle speed , and loading cycles is explored, with a particular focus on the lateral distrib ution of wheel tracks in driving and passing lanes. Utilizing video analysis and finite element modelling (FEM) through ABAQUS 2022 software, multiple input fac tors, such as speed (60, 80 and 100 km/h), loading cycles (100,000 to 500,000), and temperature range (0 ℃ to 50 ℃), are applied to observe the maximum ruttin g (17.89 mm to 24.7 mm). It is observed that the rut depth exhibited is directly proportional to the loading cycles and temperature, but the opposite is true in the case of vehicle speed. Moreover, interpretable machine learning models, par ticularly the Bayesian-optimized light gradient boosting machine (LGBM) model, d emonstrate superior predictive performance in rut depth. Insights from SHAP inte rpretation highlight the significant roles of temperature and loading frequency in pavement deformation."
Tongji UniversityShanghaiPeople's Re public of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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