Besides traditional chemical stabilizers, polymer emulsion that is considered as a nontraditional
chemical stabilizer had been introduced recently. Among polymer emulsions, Canlite and
Probase have been widely used for soil stabilization. The aim of this paper is to investigate the
effectiveness of Laterite soil stabilization treated by Canlite-liquid and Probase-liquid soil stabilizers.
A testing program, scheduled to achieve the overall objectives of this study was conducted to
determine the basic properties of Laterite soil, establish the relationship between the compaction
characteristics (maximum dry density and optimum moisture content) with the amount of polymer
emulsion and last but not least to compare the strength of the Canlite-treated and Probase-treated
Laterite soil. The effects of both polymer soil stabilizers – Canlite and Probase – were examined. The
optimum moisture content of the mixtures was used as a reference to determine the water content for
the preparation of all the specimens and later used in the unconfined compressive strength (UCS) test.
The laboratory test results showed that the additional amount of Canlite and Probase have improved
the physical properties, liquid limit and unconfined compressive strength (UCS) of the Laterite soil.
The compressive strength of the treated Laterite was found varied and depends on the type of
stabilizers, quantity of additives and curing time. From the UCS tests, it was found that Probase
improves the strength of the Laterite more than Canlite.
The Critical State Soil Mechanic (CSSM) is a globally recognised framework while the critical states for sand and clay are both well established. Nevertheless, the development of the critical state of sand matrix soils is lacking. This paper discusses the development of critical state lines and corresponding critical state parameters for the investigated material, sand matrix soils using sand-kaolin mixtures. The output of this paper can be used as an interpretation framework for the research on liquefaction susceptibility of sand matrix soils in the future. The strain controlled triaxial test apparatus was used to provide the monotonic loading onto the reconstituted soil specimens. All tested soils were subjected to isotropic consolidation and sheared under undrained condition until critical state was ascertain. Based on the results of 32 test specimens, the critical state lines for eight different sand matrix soils were developed together with the corresponding values of critical state parameters, M, λ, and Γ. The range of the value of M, λ, and Γ is 0.803-0.998, 0.144-0.248, and 1.727-2.279, respectively. These values are comparable to the critical state parameters of river sand and kaolin clay. However, the relationship between fines percentages and these critical state parameters is too scattered to be correlated.
Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.
Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.