首页|New Machine Learning Findings from Xiangtan University Described (Machine Learni ng Prediction and Evaluation for Structural Damage Comfort of Suspension Footbri dge)
New Machine Learning Findings from Xiangtan University Described (Machine Learni ng Prediction and Evaluation for Structural Damage Comfort of Suspension Footbri dge)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting from Xiangtan, People's Repub lic of China, by NewsRx journalists, research stated, "To investigate the impact of structural damages on the comfort level of suspension footbridges under huma n-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The a im is to achieve rapid estimation and health monitoring of comfort levels during bridge operation." Financial supporters for this research include Construction of Innovative Provin ces in Hunan Province. The news journalists obtained a quote from the research from Xiangtan University : "To accomplish this, the study combines finite-element simulation results to e stablish a data-driven library and introduces three distinct machine learning al gorithms. Through comparative analysis, a machine learning-based method is propo sed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong S uspension Bridge, the study evaluates and researches the comfort level of the st ructure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when consider ing the mass of individuals, peak acceleration decreased. The predictive perform ance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and Rsquared (R2) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performa nce analysis was below 5%."
Xiangtan UniversityXiangtanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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