Field Validation for the Constructability of Exposed Aggregate Concrete Overlay

Functional performance of existing concrete pavement is necessarily improved to maintain the low tire-pavement noise and the long-term skid resistance. Thus, exposed aggregate concrete overlay is introduced since these high functional performances are expected. Its surface texture requires the removal of cement mortar to expose the aggregates. This technique results a durable surface texture and an effective surface drainage through the random shape of aggregates. In Korea, concrete overlay is allowed to construct between 200 and 400 meters per day. This concrete amount is insufficient to use batch plant. Therefore, mobile mixer is employed to supply a consistent quality and on-time production. In addition, surface texture of this overlay method is significantly related to the reduction of tire-pavement noise. Accordingly, outcome of surface texture should be validated in the real field construction. This study intended to verify the constructability of exposed aggregate concrete overlay using mobile mixer for concrete production and deck finisher for concrete placement to acquire the surface texture and structural strengths. Surface texture and concrete strengths from mock-up and laboratory tests were measured and compared with design criteria. This research indicated that the suggested equipment and construction procedure can efficiently maintain the required surface texture of EACO for low tire-pavement noise and the adequate strengths for early traffic opening.

Artificial Neural Network Model for Road Pavement Classification using Features of Tire-Pavement Noise and Road Surface Images

Artificial neural network models were developed to classify road pavement types into the transverse-tined, the longitudinal-tined, NGCS(Next Generation Concrete Surface), Diamond Grinding, and Stone Mastic Asphalt by utilizing tire-pavement noise and road surface images. Tire-pavement noise data were collected by OBSI(On-Board Sound Intensity) method, and analyzed to obtain sound intensity level, sound pressure level, and sound quality indices. Road surface image data was analyzed through image feature extraction algorithms of Hough transformation and HOG(Histogram of gradient). The important features among the acoustic and image characteristics were selected by a random forest model. The acoustic features selected by the random forest algorithm are the overall sound intensity level of 400~5kHz 1/3-octave bands, the sound intensities (W/m2) of 800~2kHz 1/3-octave bands, loudness, fluctuation strength and tonality. The image features selected are the number of longitudinal lines extracted from Hough transform algorithm and HOG of the central cell. The two groups of the selected features were applied separately or together to an artificial neural network model to find classification performance. The classification accuracy rates of the models using acoustic features only, image features only and both acoustic and image features combined were 90.8%, 88.8%, and 97.3%, respectively.

Influence of pavement type and aggregate size on tire-pavement noise generation

Among the sources of vehicle noise, the interaction of tires with the pavement is the most important. Tire-pavement noise is the result of a number of generation and amplification mechanisms as the tire rolls along the pavement. These mechanisms tend to fall into independent low-frequency and high-frequency ranges. In this current study, 24 measured pavements were grouped by type and evaluated via multiple linear regression analyses with respect to vehicle speed and specified aggregate dimensions. The evaluation found that tire-pavement noise variation for a specific pavement type is explained largely by aggregate size. Tire-pavement noise tended to increase with aggregate size—a behavior consistently exhibited, for example, by SMA pavements. Porous asphalt pavements ranged from relatively quiet to relatively noisy depending upon aggregate size. The ultimate goal of this work is the development of methods enabling the design of quieter pavements using analytical means.

Investigation of Ambient Noise, Surface Quality Improvements, and Friction Characteristics of Different Asphalt Surfaces in Alberta, Canada

To determine how pavement noise and grip may be affected by pavement treatment type, a test section was constructed on a major arterial roadway in St. Albert, Alberta. This test section was used to compare the noise and friction differences of a microsurfacing layer, stone mastic asphalt (SMA) and high traffic asphalt (HT) utilizing an onboard sound intensity (OBSI) device, roadside noise meters, and a grip tester. The results of the grip tests revealed that SMA and HT had little effect on improving friction in the corridor, while microsurfacing improved the corridor grip by 15%. From noise testing, it was found that HT provided the best immediate noise attenuation compared to SMA and microsurfacing.

Evaluation of the Functional Performance of Paving Materials Based on the Driving Wheel Pavement Analyzer

There is still a lack of suitable methods for evaluating pavement functional performance according to the characteristics of real-world environment and traffic. This study developed an acceleration test method based on the Driving Wheel Pavement Analyser (DWPA) to evaluate the anti-sliding properties, anti-stripping properties, and tire–pavement noise of AC-13, SMA-13, and OGFC-13 asphalt mixtures, and MS-1, MS-2, and MS-3 micro-surfacing materials. The results indicate that the OGFC-13 mixture exhibited the largest texture depth, and the SMA-13 mixture exhibited the largest British pendulum number (BPN) at the end of the test. The MS-3 material had the best anti-sliding performance among the micro-surfacing materials. Coarse gradation improved the anti-stripping performance of the micro-surfacing materials. The tire–pavement noise for all materials increased with the increase of wheel repetitions. The OGFC-13 mixture and MS-3 micro-surfacing material exhibited the best and worse noise reduction performance, respectively. The new measurement method for evaluating the pavement surface functional performance was proved to be efficient.