首页|New Findings from Beijing Jiaotong University Describe Advances in Machine Learn ing (Real-time Detection of the Lateral Resistance of Ballast Bed During Track R ealigning In Tamping: a Novel Test Method Based On Track Shifting Operation)
New Findings from Beijing Jiaotong University Describe Advances in Machine Learn ing (Real-time Detection of the Lateral Resistance of Ballast Bed During Track R ealigning In Tamping: a Novel Test Method Based On Track Shifting Operation)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Beijing, People’s Repu blic of China, by NewsRx journalists, research stated, “Real-time detection of t he mechanical state of ballast bed during the tamping operation in railway maint enance is of great significance for improving the effectiveness of operations. I n this study, a novel test method named the track shifting test was proposed bas ed on the track realigning operation of the tamping vehicle.” Financial support for this research came from Science and Technology Research an d Development Project of China State Railway Group Co., Ltd.. The news correspondents obtained a quote from the research from Beijing Jiaotong University, “The track panel was pushed by the shifting device. Moreover, the l ateral resistance of ballast bed was reflected through easily measured indexes. An accurate coupling model of the shifting device and the ballasted track was co nstructed. Based on the model, the mechanical response of ballast and the track panel induced by the shifting load was analyzed. Results indicated that at an ef fective loading displacement of 2 mm, the lateral resistance of ballast bed with in a detectable range of up to five sleepers can be inverted by the shifting for ce and the displacement of sleepers. A machine learning model was established to obtain the mapping relationship between the shifting force, the displacement of sleepers, and the lateral resistance of ballast bed. Therefore, real-time detec tion of the lateral resistance was achieved by combining the proposed test metho d and the machine learning algorithm.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningBeijing Jiaotong University
NETL
NSTL
