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Persian gum as a newly introduced hydrocolloid with unique adhesive and gel forming properties has found wide applications in various industries such as medicine, food and textile. Recently, it has been employed in soil environment as a biocompatible stabilizing agent. However, due to its novelty, there is no study about its application in cohesionless sandy soil. This paper provides a comprehensive evaluation of Persian gum treated sand through macro- and microscale tests including compaction, unconfined compressive strength (UCS), direct shear (DS), triaxial (UU), California bearing ratio (CBR), falling head permeability, scanning electron microscopy (SEM), stereo zoom microscopy (SZM), Brunner, Emmet and Teller (BET), particle size analysis (PSA) and thermal gravimetric analysis (TGA). The tests were conducted on untreated and treated soil with different Persian gum contents and curing times. The obtained results represent the powerful performance of this novel gum in mechanical strength and bearing capacity enhancement through binding soil particles and formation of large agglomerated grains. Also, the remarkable ability of this novel gum in pore filling and compacting soil interstructure makes it a favorable additive in soil projects with permeability reduction purposes. [ABSTRACT FROM AUTHOR]

Recycled concrete aggregates (RCAs) have a high potential to be used as replacements for natural aggregates in the construction of pavements with similar or even better characteristics. This paper evaluates the influence of RCA as a partial replacement in a granular sub-base of 37.5 mm nominal maximum size (GSB38). Specifically, two replacement percentages of RCA by weight of the coarse aggregate of the GSB38 were evaluated: 10 and 15. To estimate the influence on the mechanical behavior, abrasion tests were carried out in the Los Angeles (LA) abrasion and California bearing ratio (CBR) methods. For GSB38, the average abrasion results were 30.54%. The CBR for the same material at a penetration of 0.254 cm was 26.90, 61.00 and 76.20% and at a penetration of 0.508 cm was 33.00, 65.70 and 82.00% for 10, 25 and 56 blows per layer, respectively. For GSB38 with 10% RCA, similar and even better behavior than natural GSB38 was found in terms of LA abrasion, with an average result of 38.68%. The CBR for the same alternative at a penetration of 0.254 cm was 13.20, 52.80 and 57.70% and at a penetration of 0.508 cm was 18.00, 61.30 and 68.90% for 10, 25 and 56 blows per layer, respectively. But GSB38 with 15% RCA showed an average abrasion of 45.51%, indicating a decrease compared with natural GSB38 and GSB38 with 10% RCA. The CBR for the same alternative also shows a decreasing trend. [ABSTRACT FROM AUTHOR]

California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson's correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. [ABSTRACT FROM AUTHOR]