Field Evaluation of Reflected Noise from a Single Noise Barrier (2018)

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12 • The reference microphones were named BarRef01 for the microphone placed at the Barrier and NoBarRef02 for the microphone placed at the equivalent No-Barrier site. Measurements were made in terms of the equivalent sound level, Leq, for the broadband (overall) A-weighted sound level, unweighted sound pressure level, and individual one-third octave band sound pressure levels. One-second broadband A-weighted sound levels and unweighted sound pressure levels and one-third octave band unweighted sound pressure levels between 12.5 Hz and 20,000 Hz were saved and later processed into 1-minute intervals; the audio signal was recorded. Analysis of the initial data led to a decision to only present data in the 20 Hz to 10,000 Hz bands to eliminate the distraction of data irrel- evant to the study and undue influence on the broadband unweighted sound pressure levels and A-weighted sound levels. These broadband levels were recomputed after the very low and high bands were deleted. The main reason for including both the A-weighted and unweighted data was to see if there was a difference in the two, which might provide an initial indication of frequency- specific effects. For example, higher unweighted levels might point toward substantial lower frequency components of the received sound. On the other hand, if the A-weighted level was close to or higher than the unweighted levels, there could be important contributions in the 1,000 Hz to 4,000 Hz bands. Studying only the A-weighted levels could disguise lower fre- quency contributions. The Barrier and No-Barrier spectra were studied in terms of unweighted sound pressure levels to give a true picture of the spectra, and the unweighted levels were then used as the basis for Barrier/No-Barrier one-third octave band level difference comparisons (described under “FHWA Method Data Analysis Protocol” in this chapter). Figure 2. Typical microphone positions.

13 Statistical exceedance descriptors (Ln)—specifically L1, L5, L10, L33, L50, L90 and L99—were computed for the 5-minute periods based on the 1-second data. These descriptors were used to determine the sound level range in a sample period and in diagnosing data on individual pass-bys and the pos- sible sustaining of background noise levels due to sound reflections off the barrier. Meteorological Data At each location, the meteorological station was set up in an open area near the No-Barrier site. The wind data were used to determine a vector wind speed in the direction from the roadway to the microphones to determine the appro- priate wind class for the measurements. Temperature data at 5 ft. and 15 ft. above ground level were used to determine the appropriate gradient class for the measurements. Measurement Procedures Before field work, the measurements were planned in detail using a field review report as a guide. On the measurement day, the team set up and calibrated the sound level analyz- ers and deployed the microphones via extension cables on tripods or guyed towers. The electronic noise floor of the entire acoustic instrumentation system also was established. Approximately 4 hours of simultaneous data were then collected at all the microphones. The meteorological data—measurements of wind speed, wind direction, and temperature—were recorded at 1-second intervals for later processing into 1-minute averages that could be time- synched to the sound level data (all times are reported on a 24-hour clock). Each person performing noise measurement also kept a field log of events, recording the time of occurrence of pass-bys of vehicles of interest (typically heavy trucks) and noting any unrepresentative sounds or events that might affect the 1-minute sound level measurements. The unrep- resentative sounds or events were noted for possible elimi- nation of these contaminated 1-minute data intervals from the analysis. Samples of vehicle speeds were stored in a file in the laser speed gun and were also recorded manually on data sheets to identify the vehicle type. Speeds varied by lane, as was expected. For consistency, for roads with more than two lanes in each direction, most speeds were measured in the second lane from the outside. Additional samples of speeds were made in the other lanes to the extent possible. At the end of the measurements, the calibration was checked for sound level analyzers, with the audio of the cali- bration tone recorded. All the data were then downloaded onto personal computers, with a common file-naming con- vention used for all files. Vehicle classification counts (automobiles, medium-size trucks, heavy trucks, buses, and motorcycles) were made from the video in 1-minute intervals that matched the sound level measurement intervals. The speed data were entered into the speed spreadsheet by identification number and vehicle type. The speeds were adjusted to account for the angle of speed shooting off from head-on. As needed, the speed samples were time-adjusted forward or backward to represent the time of passage from the shooting point to a point midway between the Barrier and No-Barrier sites. FHWA Method Data Processing Data processing for the FHWA Method involved three major steps: (1) creation of data spreadsheets, (2) elimination of time periods with unrepresentative events that affected the measured sound levels, and (3) identification of equiva- lent time periods in terms of meteorological class and traffic parameters. First, the sound level and meteorological data were pro- cessed into a single standardized spreadsheet format for both the raw 1-second data and the 1-minute interval (period) data averaged from the 1-second data. The sound level data included the A-weighted sound level and unweighted sound pressure level plus the one-third octave band sound pressure levels. The meteorological data included the average wind speed, wind direction, temperature, and relative humidity at each sensor. (These sensors had high and low heights of 15 ft. and 5 ft., respectively.) For each period, the vector component of the average wind velocity in a perpendicular line from the highway to the reference microphone was computed, along with the temperature gradient. Each period was classified by wind class based on the information shown in Table 1, which reproduces Table 3 from Measurement of Highway-Related Noise (Lee and Fleming 1996). Winds outside these condi- tions (having vector components over ± 11 mph) were put into a class called “Invalid-wind.” Each period also was classified by temperature gradient class (Inversion, Neutral, or Lapse) according to the ranges shown in Table 2. These classes are based on data collected by ATS several years ago from the Arizona Transportation Wind Class Vector Component of Wind Velocity Upwind -2.2 to -11 mph Calm -2.2 to +2.2 mph Downwind +2.2 to +11 mph Source: Lee and Fleming (1996), Table 3 Table 1. Classes of wind conditions.

14 Research Project (ATS Consulting 2005). The Neutral condi- tions are based on the graphs presented in that report. Based on the wind class and temperature gradient class, each 1-minute period of sound level data was then placed in one of 10 meteorological classes: Upwind Lapse, Calm Lapse, Downwind Lapse, Upwind Neutral, Calm Neutral, Down- wind Neutral, Upwind Inversion, Calm Inversion, Down- wind Inversion, or Invalid-wind. Based on the field notes, the data were screened for any potentially bad or unrepresentative events (e.g., loud non-traffic noises, periods of stopped traffic flow) at each microphone position. As needed, the 1-second data and 1-minute averaged data were reviewed to see if the events affected the levels. The next step was to determine 5-minute periods that were equivalent to each other for inclusion in a measure- ment repetition group. First, 5-minute running averages of the vector wind component were computed for each minute of the 4-hour block (excluding those 5-minute periods that had one or more bad 1-minute periods). The phrase “5-minute run- ning averages” means that each consecutive minute begins a new 5-minute period that includes its own data and the data from the next 4 minutes. For example, 12:01 to 12:06, 12:02 to 12:07, and 12:03 to 12:08 are three consecutive run- ning 5-minute periods. Using 5-minute running averages gives researchers more flexibility when trying to determine periods that have equivalent sources and meteorological conditions. Each 5-minute period was assigned to a meteorological class based on a requirement that at least 3 of the 5 minutes be in the same class. All 5-minute periods in the same meteorological class that did not overlap each other in time were then tested for traffic equivalence. (An example of overlapping periods would be 13:45 to 13:50 and 13:47 to 13:52. An example of non-overlapping periods would be 13:45 to 13:50 and 13:50 to 13:55.) Finally, traffic equivalence was determined. FHWA Method Data Analysis Protocol After the equivalent 5-minute periods were determined for the different meteorological classes and traffic condi- tions, each grouping of non-overlapping equivalent periods was used to compute the sound level increases between the Barrier and No-Barrier microphones. The data analysis procedure in Measurement of Highway- Related Noise (Lee and Fleming 1996) was used, with some adjustment. The first step was to determine any needed cali- bration adjustments before conducting the data analysis. The procedure in Section 6.6.3 of Measurement of Highway-Related Noise was adapted for sound levels measured opposite the noise barrier rather than behind the barrier, and for analysis in one-third octave bands. Details are provided in Appendix E (available online). The sound level changes between No-Barrier and Barrier sites were determined for the different groups of equiva- lent 5-minute periods. The mean broadband A-weighted and unweighted level changes and individual one-third octave band sound pressure level changes were computed for each group by arithmetically averaging the differences from the individual 5-minute periods. A standard deviation was computed for each sound level increase, and the results were plotted. The average differences by frequency band were then com- puted for all equivalent 5-minute periods that were analyzed within a meteorological class occurring at each location. Dif- ferences of these average differences were also computed to allow study of the possible effect of meteorological class on the results. After study of the average differences for each equivalent group for each meteorological class, the average differences by meteorological class were found to represent the individual groupings’ differences adequately. Accord- ingly, these latter average differences by meteorological class are presented in this report. All the graphs and tables of the average differences by the groups of equivalent 5-minute periods are in the spreadsheets in the project record. Finally, the differences of the Ln values for each 5-minute period were computed and analyzed. Spectrogram Data Processing and Analysis Protocol A spectrogram analysis allows examination of spectral (fre- quency) content over specified periods, such as a 5-minute data block or a vehicle pass-by event. The master raw data spreadsheet files from the measured sound level data files were screened to identify bad or invalid data periods. Clean data blocks were identified for the spectro- gram analysis; the length of these data blocks varies among the sites, based on how often the intrusive noise events occurred and whether or not it was desirable to examine the same 5-minute data blocks as were examined with the standard analysis. Example data blocks for each site are shown in this report. Vehicle pass-by events also were identified for investiga- tion. The vehicle pass-by events were first identified using the site logs, where potential isolated events were noted. Multiple Temperature Gradient Class Gradient: (Temp_upper – Temp_lower) Divided by Vertical Distance Between Sensors Inversion Positive > 0.1 Neutral -0.1 to 0.1 Lapse Negative < -0.1 Table 2. Classes of temperature gradients.

15 events were examined for each site, and only those events that could be clearly identified at both the Barrier and No-Barrier sites were retained. Example vehicle pass-by events for each site are shown in this report. Calibrated WAV files were processed and examined in one- third octave bands in one-eighth-second intervals. The data were then displayed using spectrogram-type graphs. In-house MATLAB code allowed for the spectrogram processing and the flexibility to compare data for selected periods of time (i.e., 5-minute data blocks) among differing microphones at each site. For the data blocks and vehicle pass-by events, pairs of microphones were compared. Each pair consisted of one microphone at the Barrier site and an equivalent microphone at the No-Barrier site. For all sites, spectrogram data were examined for the community microphone pair BarCom03/ NoBarCom05 and the community microphone pair BarCom04/ NoBarCom06. The spectrograms for these two pairs show the effect of the barrier noise reflected across the highway to communities opposite a noise barrier. At some of the sites, the reference microphones were strategically placed between the road and the barrier to capture barrier reflections on the Barrier side of the highway. In these cases, microphones BarRef01 and NoBarRef02 were compared to show the effect of the barrier-reflected noise close to the reflecting surface. Upon examination of the spectrograms, spectral shapes and values were compared for the equivalent microphone pairs, and trends were noted. Where results were similar between microphone pairs, only one pair is presented in this report. To facilitate comparison of spectrogram results for reflec- tive and absorptive barriers and to visualize subtle differences in spectrograms, the research team developed a method to generate difference spectrograms. First, the team aligned the maximum sound level at each site after applying 3-second averaging to the data. Once the maxima were aligned for the event from each site, 1-second averaging could be applied, and differences for each one-third octave band could be used to create a difference spectrogram (Barrier minus No-Barrier). The averaging times were optimized to expose key spectral features (to expose distinctive horizontal lines of high ampli- tude). A positive difference (Barrier louder than No-Barrier) is indicated with increasing intensity of red, and a negative difference (Barrier quieter than No-Barrier) is indicated with increasing intensity of blue. White indicates no difference in sound level between the Barrier and No-Barrier sites. A slice-in-time plot was extracted from each difference spectrogram to further examine the location of the high- amplitude horizontal lines, revealing multiple peak frequen- cies with a harmonic relationship, which indicates comb filtering. (The time chosen for each slice was based on visual distinction of high-amplitude horizontal lines in the related spectrogram difference plot and to be most representative of high-amplitude lines before and after the vehicle passes by, given that the closest point of approach typically does not reveal the lines.) Psychoacoustics Processing and Analysis Protocol The audio recordings made by the measurement team for each location were calibrated, postprocessed, and analyzed for the psychoacoustic parameters of interest. In some cases, audio filters were applied, especially at higher frequencies, to remove electronic artifacts before postprocessing. The Psychoacoustics Processing and Analysis Protocol outlines the background and details behind the psycho- acoustic processing selected for this study (see Appendix E). The protocol can be summarized as follows: • Processing is based on a subset of industry-accepted psychoacoustic metrics; • Metrics are combined parametrically to estimate potential annoyance based on three algorithms from extant sound quality literature; • Metrics are computed as functions of time, in 1-minute intervals, for each recorded microphone signal; and • Descriptive statistics are derived from each metric’s time series to examine trends and differences between locations. The psychoacoustic metrics applied to the audio recordings included the following: • Loudness (measured in sones); • Sharpness (measured in acums); • Roughness (measured in aspers); and • Fluctuation strength (measured in vacils). Not all the psychoacoustic metrics have an internationally standardized algorithm. Therefore, among the commercially available software offerings, there is not a fixed calculation associated with each metric. For this study, all the psycho- acoustic analyses were completed using a software package developed specifically for this project by Nelson Acoustical Engineering, Inc. The algorithms encoded into the software comply with the international standards that are currently available and make use of widely accepted expressions for those metrics that are not standardized. The formulas that were applied in the psychoacoustic analysis software are detailed in Appendix E. It is also important to consider the time-varying loudness created by traffic noise, because a barrier will create addi- tional temporally varying sounds due to reflections. The time- varying loudness created by traffic noise can cause interaural

16 time differences, interaural intensity differences, and spectral changes that, in turn, create issues such as localization errors. In this study, statistical values of loudness were calculated to help account for the temporally varying sounds. Although these individual psychoacoustic metrics are use- ful for comparing sounds of widely different natures, they do not necessarily indicate whether an individual might be annoyed by a given sound, nor to what extent. Several annoy- ance scales have been suggested in the literature; each is based on regressions over listener preference trials, using weighted combinations of the psychoacoustic parameters. The metrics demonstrated in the current work are detailed in the Psycho- acoustics Processing and Analysis Protocol in Appendix E. These metrics include the following: • Unbiased annoyance (UBA), using loudness exceeded 10% of the time (N10), mean sharpness (Sm), and mean fluctuation strength (Fm); • Psychoacoustic annoyance (PA), using loudness exceeded 5% of the time (N5), mean sharpness (Sm), mean fluctuation strength (Fm), and mean roughness (Rm); and • Category scale of annoyance (CSA), using loudness exceeded 5% of the time (N5), sharpness exceeded 50% of the time (S50), and mean roughness (Rm). For each site, for the corresponding community micro- phone locations in the presence and absence of the barrier, respectively, the annoyance metrics computed as a function of time are paired for comparison. Time intervals are the same as those used in the spectral analysis (1 minute). This time interval is sufficiently long that short-term events, such as truck pass-bys, are captured at both monitoring locations. Because the annoyance metrics UBA and PA are sensitive to sharpness, the high-frequency content in the recordings is cru- cial. Some of the digital audio recordings used in this work had some high-frequency contamination due to electronic noise. Audio filtering was applied to the contaminated recordings to reduce this effect. The annoyance metrics exhibited sensitivity to the applied filters, however; as a result, the Briley Parkway data were discarded from this analysis, and the annoyance metrics computed for I-24 may be slightly biased by their filtering. The I-90 and SR-71 recordings also had some con- tamination, but it was at lower magnitude. Filtering was mild in this case, and the annoyance metrics are likely internally consistent for those sites. The recordings from MD-5 were free from contamination by electronic noise. The presence of tree frogs and insects in the night recordings required additional filtering, but in this case, the same filter was applied to all the recordings, so the resulting metrics are still comparable.

17 Sound-Reflecting Barrier Study Locations Study Location Selection Criteria and Process Location selection criteria were developed in Phase 1 pri- marily to judge if a Barrier site and its potentially equivalent No-Barrier site are indeed equivalent. The criteria were incor- porated in a Preliminary Site Evaluation Form prepared by the researchers. These forms were then used by each team mem- ber in a desktop identification of potential study locations. Twenty-one potential locations were identified. The researchers refined the list of 21 potential locations to 10 preliminary study locations on the basis of the following variables: • Comparable Barrier and No-Barrier locations: Sec- tions of road with a consistent pavement type, either a reflective or absorptive barrier on one side, and a nearby No-Barrier section having similar geometry and topogra- phy so that monitors could be placed at the same distances and heights relative to the barrier. Paired simultaneous measurements could then be conducted under the same traffic and meteorological conditions with no normaliza- tion of data needed for varying traffic conditions. • Noise sources: Sites without other significant noise sources. • Roadway type/classification: Types of roadway and number of lanes. • Road cross-section: At-grade, elevated (on fill), or depressed (in cut). • Pavement type: Asphalt or concrete. • Traffic mix: Traffic volumes and vehicle mix (i.e., percent trucks). • Noise barrier height: Different barrier heights between locations, to the extent feasible. • Noise barrier location: Locations at the shoulder, near the source, or at the right-of-way (ROW), near the receiver. Field reviews were then conducted at the most promising locations. A final set of eight acceptable candidate locations was identified. Selected Locations From the eight candidate locations, five locations were selected for study. 1. BA-1, I-24, Murfreesboro, Tennessee: This eight-lane freeway has a reflective 19-ft. precast concrete post-and- panel noise barrier in the ROW. It has a relatively high volume of traffic at 78,000 vehicles per day (vpd) with a relatively high percentage of trucks at 14%. 2. BA-3, SR-155 (Briley Parkway), Nashville, Tennessee: This six-lane freeway has a reflective 12-ft. to 13-ft. cast- in-place concrete noise barrier that is set close to the road, along the shoulder. It carries one-third as much traffic as I-24 does, at 45,820 vpd, and has moderate truck traffic at 8% of the total volume. 3. SID-1, I-90, north of Spring Creek Rd., Rockford, Illinois: This six-lane freeway has a 15-ft. precast concrete post-and-panel noise barrier situated just beyond the shoulder. It carries 53,500 vpd, with 9.7% trucks. 4. ATS-3, SR-71, Chino Hills, California: This six-lane free- way provides the only sound-reflecting location with con- crete pavement. It also allows for community microphones as far as 400 ft. from the center of the near travel lane. The noise barrier is 13 ft. high, which includes a 7-ft. concrete block barrier atop a 6-ft. high earthen berm. It carries 60,000 vpd, with 7% trucks. 5. EA-5, MD-5, Hughesville, Maryland: This site is the only arterial to be studied, and its barrier is close to the road. The relatively lower volume (34,200 vpd) and monitor- ing extending into the night allowed the research team to observe individual vehicle pass-bys and continuous traffic flow. The truck volume is moderate at 8%. Table 3 summarizes the characteristics of the selected loca- tions. The first two locations were studied as part of Task 4. At the project team’s interim meeting with the technical panel, the decision was made to continue with the spectro- gram and psychoacoustics analyses. A decision was also made C H A P T E R 3

Location Roadway City, State Road Class Lanes Pavement Type Geometry Relative to Adjacent Land Uses AADT (vpd) Percent Trucks Barrier Location Barrier Material Barrier Height at Study Site BA-1 I-24 Murfreesboro, TN Freeway 8 DGAC At-Grade 78,140 14% ROW Precast Concrete 16–19 ft. BA-3 SR-155 (Briley Pkwy.) Nashville, TN Freeway 6 DGAC Fill (Retaining Wall) 45,820 8% Shoulder Cast-in- Place Concrete 12–13 ft. SID-1 I-90 Rockford, IL Freeway 6 Asphalt (Not determined) At-Grade 53,470 9.7% Shoulder Precast Concrete 15 ft. ATS-3 SR-71 Chino Hills, CA Freeway 6 PCC (Longitudinal grooving) At-Grade 60,000 7% ROW Concrete Block atop Berm 13 ft. (7-ft. wall atop 6-ft. berm) EA-5 MD-5 Hughesville, MD Arterial 4 DGAC At-Grade 34,160 8% Shoulder Precast Concrete 16 ft. Table 3. Selected Phase 1 sound-reflecting barrier locations.