Study & Experimental Analysis of Pervious Concrete In Low Volume Roads

2018 ◽  
pp. 172-176
Author(s):  
Suraj Pinate ◽  
Hitesh Sonawane ◽  
Jayesh Barhate ◽  
Mayur Chaudhari ◽  
Utkarsha Dhok ◽  
...  
Keyword(s):  
Experimental Analysis ◽  
Pervious Concrete ◽  
Low Volume Roads ◽  
Low Volume

Comparison of Superpave and Marshall Mixtures for Low-Volume Roads and Shoulders

1998 ◽  
Vol 1609 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Affan Habib ◽  
Mustaque Hossain ◽  
Rajesh Kaldate ◽  
Glenn Fager
Keyword(s):  
Mix Design ◽  
River Sand ◽  
Low Volume Roads ◽  
Superpave Gyratory Compactor ◽  
Asphalt Content ◽  
Low Volume ◽  
Project Site ◽  
Axle Loads ◽  
Crushed Limestone ◽  
Asphalt Film

Superpave and Marshall mix designs using local aggregates were done to study the suitability of the Superpave mix design as compared with the Marshall mix design for low-volume roads, especially shoulders. The project site was Kansas Route 177 in northeast Kansas. Three locally available aggregates, crushed limestone and coarse and fine river sands, were used in this study. Five blends with varying proportions of coarse and fine river sands were selected. Mix samples were compacted in the Superpave gyratory compactor with the applicable number of gyrations and were compacted with the Marshall hammer by using 50 blows per face. Bulk densities of the compacted samples and maximum specific gravities of loose samples also were measured for each blend. The results show that the Superpave mix design for low-volume roads and shoulders results in lower estimated asphalt content than does the Marshall method. The required asphalt content increases as the proportion of coarse river sand increases in the mix. Superpave requirements for the voids filled with asphalt (VFA) for low-volume traffic, that is, less than 0.3 million equivalent single-axle loads, appeared to be too high. High asphalt film thicknesses were computed for the mixtures that did not meet the Superpave VFA requirements. Lowering the design number of gyrations (Ndes) for compaction of samples would result in increased asphalt requirement for the Superpave mixture with a given gradation.

Otta Seals and Gravseals as Low-Cost Surfacing Alternatives for Low-Volume Roads: Experiences in South Africa

2003 ◽  
Vol 1819 (1) ◽  
pp. 338-342 ◽  
Author(s):  
Simon Oloo ◽  
Rob Lindsay ◽  
Sam Mothilal
Keyword(s):  
South Africa ◽  
Low Cost ◽  
Cost Savings ◽  
Cost Effective ◽  
Field Trials ◽  
Low Volume Roads ◽  
Kwazulu Natal ◽  
Traffic Volumes ◽  
Low Volume ◽  
To Come

The geology of the northeastern part of the province of KwaZulu–Natal, South Africa, is predominantly alluvial with vast deposits of sands. Suitable gravel sources are hard to come by, which results in high graveling and regraveling costs brought about by long haul distances and accelerated gravel loss. Most gravel roads carry fewer than 500 vehicles per day of which less than 10% are heavy vehicles. The high cost of regraveling has led to consideration of upgrading such roads to surfaced standard, even though traffic volumes do not justify upgrading. Traditional chip seals are expensive and cannot be economically justified on roads that carry fewer than 500 vehicles per day. The KwaZulu–Natal Department of Transport is actively involved in efforts to identify cost-effective alternative surfacing products for low-volume roads. Field trials were conducted with Otta seals and Gravseals, which have been used successfully in other countries, as low-cost surfacing products for low-volume roads. The Otta seal is formed by placing graded aggregates on a relatively thick film of soft binder that, because of traffic and rolling, works its way through the aggregates. Gravseal consists of a special semipriming rubberized binder that is covered by a graded aggregate. Both Otta seals and Gravseals provide relatively flexible bituminous surfaces suitable for low-volume roads. Cost savings are derived mainly from the broad aggregate specifications, which allow for the use of marginal materials.

Rational Approach to the Evaluation of Soils for Low-Volume Roads

2011 ◽  
Vol 2205 (1) ◽  
pp. 73-78 ◽  
Author(s):  
Washington Peres Núñez ◽  
Jorge Augusto Pereira Ceratti ◽  
Luiz Antônio Bressani ◽  
Rinaldo José Barbosa Pinheiro ◽  
Vinicius Peraça ◽  
...  
Keyword(s):  
Rational Approach ◽  
Low Volume Roads ◽  
Low Volume

Aeolian Sands as Material to Construct Low-Volume Roads

2011 ◽  
Vol 2205 (1) ◽  
pp. 67-72
Author(s):  
P. Paige-Green ◽  
M. I. Pinard ◽  
M. Mgangira
Keyword(s):  
Low Volume Roads ◽  
Low Volume ◽  
Aeolian Sands

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

Estimation of the annual average daily traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period traffic counts. Fifty-five automatic traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

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