This study is conducted to investigate the strength and stiffness of clayey soil stabilized with fly ash-based geopolymer for unpaved roads. Two sodium hydroxide concentrations of 6 and 8M and two alkali solution ratios of NaOH:Na2SiO3= 1 and 1.5 were considered. Other factors such as fly ash replacement ratio (by mass), curing period, and curing temperature were held constant at 15%, 48 hours, and 65 C, respectively. The unconfined compressive strength (UCS) and ultrasonic pulse velocity (UPV) tests were performed to evaluate the mixtures. Outcomes of this study revealed that the strength of the clayey soil could be increased by up to 94%. Additionally, increasing sodium silicate content in the alkali solution increased the solution's activity and yielded higher strength and stiffness. This study confirms the effectiveness of the geopolymer binder for the improvement of soil strength and stiffness.
ABSTRACT: In this paper, artificial neural networks (ANNs) are used in attempt to obtain the strength of polymer-modified concrete (PMC). A database of 36 case records is used to develop and verify the ANN models. Four parameters are considered to have the most significant impact on the magnitude of (PMC) strength and are thus used as the model inputs. These include the Polymer/cement ratio, sand/cement ratio, gravel/cement ratio, and water/ cement ratio. The model output is the strength of (PMC). Multi-layer perceptron trained using the back-propagation algorithm is used. In this work, the feasibility of ANN technique for modeling the concrete strength is investigated. A number of issues in relation to ANN construction such as the effect of ANN geometry and internal parameters on the performance of ANN models are investigated. Design charts for prediction of polymer modified concrete strength are generated based on ANN model. It was found that ANNs have the ability to predict the strength of polymer modified concrete, with a very good degree of accuracy. The ANN models developed to study the impact of the internal network parameters on model performance indicate that ANN performance is reality insensitive to the number of hidden layer nodes, momentum terms or transfer functions. On the other hand, the impact of the learning rate on model predictions is more pronounced.keywords:; Artificial Neural networks; Strength; Polymer Modified Concrete; Modeling.
Processed and natural clays are widely used to construct impermeable liners in solid waste disposal landfills. The engineering properties of clay liners can be significantly affected by the leachate from the waste mass. In this study, the effect of inorganic salt solutions on consistency and compressibility characteristics of compacted clay was investigated at different concentrations. Two type of inorganic salt MnSO4 and FeCl3 are used at different concentration 2%, 5%, and 10%. The Clay used was the CL- clay (kaolinite). The result shows that the consistency limits increased as the concentration of salts increased, while the compression index (Cc) decreases as the concentration increased from 2% to 5%, after that the Cc is nearly constant. The swelling index (Ce) tends to increase slightly as the concentration of MnSO4 increased, while it decreases as the concentration of FeCl3 increased.
The mechanical behaviour of partially saturated soils can be very different from that of fully saturated soils. It has long been established that for such soils, changes in suction do not have the same effect as changes in the applied stresses, and consequently the effective stress principle is not applicable. A procedure was proposed to define the soil water characteristic curve. Then this relation is converted to relation correlating the void ratio and matric suction. The slope of the latter relation can be used to define the H-modulus function. This procedure is utilized in the finite element analysis of a footing on unsaturated coarse grained soil to investigate its bearing capacity. The finite element results demonstrated that there is a significant increase in the bearing capacity of the footing due to the contribution of matric suction in the range 0 to 6 kPa for the tested compacted, coarse-grained soil. The ultimate pressure increases from about 120 kPa when the soil is fully saturated to about 570 kPa when the degree of saturation becomes 90%. This means that an increase in the bearing capacity of about 375% may be obtained when the soil is changed from fully saturated to partially saturated at a degree of saturation of 90%. This development in the bearing capacity may exceed 600% when the degree of saturation decreases to 58%.
Abstract. In this research, Artificial Neural Networks (ANNs) will be used in an attempt to predict collapse potential of gypseous soils. Two models are built one for collapse potential obtained by single oedemeter test and the other is for collapse potential obtained by double oedemeter test. A database of laboratory measurements for collapse potential is used. Six parameters are considered to have the most significant impact on the magnitude of collapse potential and are being used as an input to the models. These include the Gypsum content, Initial void ratio, Total unit weight, Initial water content, Dry unit weight, Soaking pressure. The output model will be the corresponding collapse potential. Multi-layer perceptron trainings using back propagation algorithm are used in this work. A number of issues in relation to ANN construction such as the effect of ANN geometry and internal parameters on the performance of ANN models are investigated. Information on the relative importance of the factors affecting the collapse potential are presented and practical equations for prediction of collapse potential from single oedemeter test and double oedemeter test in gypseous soils are developed. It was found that ANNs have the ability to predict the collapse potential from single oedemeter test and double oedemeter test in gypseous soil samples with a good degree of accuracy. The ANN models developed to study the impact of the internal network parameters on model performance indicate that ANN performance is sensitive to the number of hidden layer nodes, momentum terms, learning rate, and transfer functions. The sensitivity analysis indicated that for the models the results indicate that the initial void ratio and gypsum content have the most significant affect on the predicted the collapse potential.Keywords. Artificial Neural Networks, collapse potential, gypseous soils
Abstract:This study explores the potential of back propagation neural networks (BPNN) computingparadigm to predict the ultimate bearing capacity of shallow foundations on cohesionlesssoils. The data from 97 load tests on footings (with sizes corresponding to those of realfootings and smaller sized model footings) were used to train and validate the model. Fiveparameters are considered to have the most significant impact on the magnitude ofultimate bearing capacity of shallow foundations on cohesionless soil and are thus used asthe model inputs. These include the width of the footing, depth of embedment, length towidth ratio, dry or submerge unit weight and angle of internal friction of the soil. Themodel output is the ultimate bearing capacity. Performance of the model wascomprehensively evaluated. The values of the performance evaluation measures such ascoefficient of correlation, root mean square error, mean absolute error reveal that themodel can be effectively used for the bearing capacity prediction. BPNN model iscompared with the values predicted by most commonly used bearing capacity theories.The results indicate that the model perform better than the theoretical methods.KEYWORDS: Ultimate bearing capacity; Shallow foundations; cohesionless soil; backpropagation neural network (BPNN); prediction