Abstract

The floating-slab track is one of the best methods to reduce the vibration from the underground railway. In this paper, the rubber floating-slab track was studied. The train-track model coupled with the track-tunnel-surrounding soil model was built, which was verified by the results of the in-site test. Through this model, the quality of the rubber floating-slab track was optimized by the multi-island genetic algorithm. The optimized Z vibration level was reduced by 4.92 dB with the density of the floating slab 2306 kg/m3, the Young’s modulus of the floating slab 3.21×1010 N/m2, the density of the rubber bearing 730 kg/m3 and the Young’s modulus of the rubber bearing 9.55×108 N/m2.

Highlights

  • IntroductionVibration generated by underground railways is one of the most serious engineering problems

  • Vibration generated by underground railways is one of the most serious engineering problems.The vibration of the subway railway will result in structural vibrations and re-radiated noise [1, 2].There are many measures to control this vibration

  • The numerical model was divided into two sub-models

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Summary

Introduction

Vibration generated by underground railways is one of the most serious engineering problems. The floating slab can be continuous or discontinuous [3], which mainly can be supported by rubber bearings or steel-springs. Bay Area Rapid Transit (BART) system, the discontinuous floating-slab track was used, which was supported by rubber pads [5]. Lei and Jiang [15] build the finite element model of the steel-spring floating-slab track to analyze the effectiveness of vibration reduction. OPTIMIZATION OF VIBRATION REDUCTION OF THE RUBBER FLOATING-SLAB TRACKS. Saurenman and Phillips [5] tested the rubber floating-slab track used on the current San. Francisco Bay Area Rapid Transit (BART) system.

Train-track sub-model
Track-tunnel-surrounding soil sub-model
In-site test
Result comparison between the numerical model and the in-site test
Optimization objective and parameters
Optimization procedure
Optimization results
Conclusions
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