The Sharbyan river is located in the Sharbyan village, Sarab, East Azarbaijan province. This river alluvials are supplied from rock units belonging to Oligo-miocene and Miocene, including conglomerate, sandy lime, limestone, marl and shale. These deposits are used as raw materials of producing hot asphalt in two asphalt plants that have been built in the vicinity of this river, and the produced asphalt is used mainly in the neighbor provinces that have rather cold climate. Combined analysis of the sediments indicate high level of silica, around 60 percent, for which the prepared asphalt is prone to stripping phenomenon in the cold seasons. During this process, the moisture penetration in aggregates and asphalt mixtures, causes weakening bitumen-asphalt materials bounding and finally asphalt demolition. The role of sediments and its impact on the quality of asphalt has not been studied in this area, therefore, the solutions for dealing with this phenomenon is also examined and presented. This study is based on the conventional sedimentology methods, different standards of ASTM, AASHTO and Ministry of Roads and Urban Development guidelines. In this study, the combined effects of hydrated lime (lime filler) and natural filter materials with different proportions was used to deal with the stripping phenomenon, and the parameters of strength, softness, indirect tensile strength, asphalt quality and durability criteria, have been appraised. The results show that these parameters are improved using additives in various proportions and the produced asphalt quality and durability is better. The results illustrate, when the lime is used in its maximum ratio of 3%, stripping score is 1 and is disappeared by other parameters improvement
Specimen | Tensile Strength (MPa) | Fracture Toughness (MPa√m) |
Limestone | 3.74 | 1.23 |
Sandstone | 7.14 | 1.63 |
Tuff | 16.36 | 2.17 |
Lithic Tuff | 4.34 | 1.01 |
Andesite | 13.25 | 1.86 |
Travertine | 8.27 | 1.14 |
This paper presents a feed-forward back-propagation neural network model to predict the retained tensile strength and design chart to estimate the strength reduction factors of nonwoven geotextiles due to the installation process. A database of 34 full-scale field tests was utilized to train, validate and test the developed neural network and regression model. The results show that the predicted retained tensile strength using the trained neural network is in good agreement with the results of the test. The predictions obtained from the neural network are much better than the regression model as the maximum percentage of error for training data is less than 0.87% and 18.92%, for neural network and regression model, respectively. Based on the developed neural network, a design chart has been established. As a whole, installation damage reduction factors of the geotextile increases in the aftermath of the compaction process under lower as-received grab tensile strength, higher imposed stress over the geotextiles, larger particle size of the backfill, higher relative density of the backfill and weaker subgrades.
Page 1 from 1 |
© 2024 CC BY-NC 4.0 | Journal of Engineering Geology
Designed & Developed by : Yektaweb