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Ihemeje, J.
Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria
Onyelowe, K. C.
Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria;Department of Mechanical and Civil Engineering, Kampala International University-Western Campus, Kampala, Uganda.
Ebid, A. M.
Deaprtment of Civil Engineering, Faculty of Engineering, Future University in Egypt, Egypt
ABSTRACT
The
traffic noise big data collected from studying traffic situations in
Port-Harcourt Nigeria selected trunks A and C roads sub-sectioned as flexible
pavements locations 1, 2, and 3 and flexible and rigid pavements locations 4
and 5 respectively has been analyzed by using the multi-linear regression (MLR)
technique. Traffic noise is an acoustic hazard affecting mostly people living
closest to the roadway pavement. The solution of such a high degree of
discomfort on roadside dwellers deserves serious study. This work considered
traffic parameters like distance between dwellers and the roadway, traffic
count, vehicular speed, traffic periods, etc. in modeling the traffic noise
intensity (TNI) of the selected road. The average peak traffic noise for location 1 obtained
at various distances of 5m, 10m and 15m from the centre of the roadway are
85.59dB, 84.93dB and 83.97dB respectively, for location 2 are 86.52dB, 85.34dB
and 84.26dB respectively, for location 3 are 84.38dB, 83.88dB and 83.32dB
respectively, for location 4 are 85.16dB, 84.56dB and 83.55dB respectively, for
location 5 Trunk C Flexible Pavement are 55.46dB, 54.36dB and 53.99dB
respectively and for Trunk C Rigid Pavement are 60.58dB, 59.58dB and 58.96dB
respectively. The traffic noise values for location 1-4 had higher noise
intensity and same range, it was categorized as Trunk A flexible pavement and
classified as heavy-trafficked routes while location 5 (Trunk C) had lower
noise intensity and same range which was classified as light-trafficked routes.
MLR predicted the TNI with R2 (0.2015, 0.2110, 0.1894, 0.2203,
0.2275, 0.1983, 0.4398, 0.4398, 0.3907, 0.3952, 0.3427, 0.3355, 0.3149, 0.1505,
0.1526, 0.1441, 0.002, 0.0012, 0.001) values for the model along the selected
routes. From the result, the distance of noise measurement from the centre of the
roadway of Trunk C flexible pavement with the most significant p-value of
0.804145, the equivalent traffic volume and traffic speed had p-values of
0.014782 and 3.22E-50 respectively whereas that of Trunk C rigid pavement with
the most significant p-value of 0.872625, the equivalent traffic volume and
traffic speed had p-values of 0.265025 and 3.67E-61 respectively. The noise level increased more on rigid pavements than
that of flexible pavements, which is attributed to more voids on rigid
pavements and the higher frictional noise due to increased frictional force
between the vehicle tires and road surfaces with the grip being more in rigid
pavements. At the end of the exercises, it was observed that ARIMA (R2
greater 90%) performed better than MLR even with the technical advantage of
determining noise difference between interfering points using the
auto-correlation factor (ACF) and the partial auto-correlation factor (PACF).
Keywords: MLR; Noise Intensity; Traffic Volume; Model Prediction; Rigid and Flexible Pavement; Pavement Traction
https://doi.org/10.33922/j.ujet_v8i1_6
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Published
Wednesday, June 08, 2022
Issue
Vol. 8 No. 1, June 2022
Article Section
GENERAL
The contents of the articles are the sole opinion of the author(s) and not of UJET.
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