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.
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
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.