Autonomous evaluation of human annoyance rate induced by subway trains using high-sensitivity wireless smart sensors
-
Published:2016
Wei Zhang, Ke Sun, Huaping Ding, Robin E. Kim, Billie F. Spencer Jr., 2016. "Autonomous evaluation of human annoyance rate induced by subway trains using high-sensitivity wireless smart sensors", Transforming the Future of Infrastructure through Smarter Information: Proceedings of the International Conference on Smart Infrastructure and ConstructionConstruction, 27–29 June 2016, RJ Mair, K Soga, Y Jin, AK Parlikad, JM Schooling
Download citation file:
ABSTRACT
The operation of subway trains induces secondary structure-borne vibrations in the nearby buildings. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced human annoyance rate. The system was implemented on the MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application AnnoyanceRate, which quantitatively determines the adverse vibration impact on human comfort, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor of the Imote2, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of human annoyance rate to the central server via radio to display the information. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced adverse effect on human comfort and the system holds the potential of greatly reducing the laboring of dynamic field testing.
