The objective of this research was to derive an empirical equation that estimates the acoustical shadow zone length (SZL) of roadway noise barriers. The acoustical shadow zone is the area behind a noise barrier of reduced sound levels, generally to some stated level at or near background. The ability to predict the SZL can be used as a method to evaluate the performance, and possibly the design, of roadway noise barriers. The current federally required roadway noise model is the Federal Highway Administration (FHWA) Traffic Noise Model (TNM). TNM uses insertion loss (IL) to evaluate the effectiveness of a barrier. Insertion loss is the difference in sound level between the "no barrier" and the "with barrier" case. One major limitation with TNM is that the reported IL does not take into account how background noise levels influence the mitigated sound levels. Background noise can be defined as the noise present at a barrier location in the absence of roadway noise. The shadow zone represents a region behind the noise barrier where the barrier is effective at reducing noise levels and takes into account how background noise affects the IL and thus the SZL. The inclusion of background noise becomes significant in evaluating barrier effectiveness because as the distance from the barrier increases, background noise begins to overtake roadway noise as the dominate noise source. The derivation of the empirical equation began by collecting in-situ noise measurements at 18 noise barrier locations across Florida. The measured noise data was supplemented by noise data obtained from computer modeling. After a sufficient quantity of measured and modeled IL data was obtained, a contour of equal IL (IL = 5 dB) was plotted for each barrier location. The area defined by the contour is called the shadow zone. All the SZLs were statistically compared to several variables that were expected to influence it. Regression modeling showed that the background noise level, noise barrier height, the distance from the roadway to the noise barrier, and percent of heavy truck traffic volume were statistically significant as useful predictors of SZL. Two empirical equations were derived, one from linear regression and one from polynomial regression, and are referred to as the Shadow Zone Equations.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-2092 |
| Date | 01 January 2006 |
| Creators | Arner, Wayne |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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