Alireza Majidi; Golamreza Lashkaripour; Ziaedin shoaei
Abstract
Erodibility, resistance and soil engineering behavior are affected by their physical and chemical properties. Lithology and characteristics of parent rock can be such factors that influence on soil properties and behavior. The aim of this study was to investigate and compare some of the physical properties ...
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Erodibility, resistance and soil engineering behavior are affected by their physical and chemical properties. Lithology and characteristics of parent rock can be such factors that influence on soil properties and behavior. The aim of this study was to investigate and compare some of the physical properties of Marly fine grained soils with two different maternal formations in a unit basin (Qom salt lake). The study was carried out on 61 soil samples. The physical parameters studied are the specific weight of the unit volume of soil, the grain size, the Atterberg limits and the soil activity number, which was measured according to ASTM standards. The investigation of the above mentioned soil properties showed that the soils of these two marl formations are silty and all are classified in the category of fine-grained soils with low to moderate plasticity. The values of the soil activity number and the Atterberg limits, especially the plasticity limit and plasticity index, indicated that the clay minerals in these soils are more Kaolinite and Illite and less montmorillonite. The comparison of variance and mean of physical properties of two groups of soil by using t-student (two independent groups) test, showed that in most of the physical properties of two Marl Soil groups consist of liquid limit, plasticity index, soil activity number and percentage of clay, silt and sand, there is a differences significant level of confidence of over 95%. This difference of physical properties was validated by clustering the samples by hierarchical method. Considering to the unit basin and similar conditions in the formation and evolution of these two groups of soil, the results of this study indicate that conditions and characteristics of the sedimentary environment of the matter rocks mainly affected on the properties and behavior of soils, especially in their early stages of evolution.
Ali Bagherian Kalat; Gholamreza Lashkaripour; Mohammad Ghafoori; Aliakbar Abbasi
Abstract
On areas with highly eroded soils, where vegetation is absent or negligible, runoff generation and erosion processes can greatly be affected by the nature of parent material. This research was carried out to investigate the effects of lithology and soil parent material on erosion and soil loss, using ...
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On areas with highly eroded soils, where vegetation is absent or negligible, runoff generation and erosion processes can greatly be affected by the nature of parent material. This research was carried out to investigate the effects of lithology and soil parent material on erosion and soil loss, using rainfall simulator in Sangerd Drainage Basin which is located in Khorasan Razavi Province. To do this research, 11 homogeneous soil units (map units) with the same slope (20%), land use (rangeland), different lithology (marls and conglomerate) and erosion facies (sheet-rill, rill and rill-gully) were selected. A rainfall simulator producing 36 mm h-1 rainfall intensity and 30 minute duration was used at 33 locations over soil erosion plots. The amount of sediments was measured. In order to investigate factors affecting on soil erosion and sediment yield, soil was sampled from 0-15 cm depth of plot adjacent areas. For statistical analysis, SPSS 22 was used. Results showed that soil erosion and sediment yield have meaningful differences in different lithologies. Eem-RG and Plc-SR soil units with 260.9 and 45.1 gr m-2 have the most and the least sediment yield, respectively. It was found that the sediment yield had positive correlation with some soil properties such as silt, Ec, pH, SAR and antecedent soil moisture and negative correlation with sand, OC, NPV, vegetation and rock fragment cover.
Alireza Majidi; Gholamreza Lashkaripour; Ziaoddin Shoaei
Abstract
The swelling potential of fine-grained soils is one of effective parameters on soil mechanical behavior and erosion and fundamental data required for the design, construction and choosing construction materials. This paper presents a multi-layer perceptron (MLP) artificial neural network (ANN) model ...
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The swelling potential of fine-grained soils is one of effective parameters on soil mechanical behavior and erosion and fundamental data required for the design, construction and choosing construction materials. This paper presents a multi-layer perceptron (MLP) artificial neural network (ANN) model to prediction of the swelling potential of marl soils. Marl soil is a fine-grained soil. The Levenberg-Marquadt learning algorithm was used to train the networks. Existing models prediction of soil swelling potential based on physical and soil index parameters. The present study considers the effects of chemical factors on the behavior and characteristics of fine-grained soils along with the common soil index parameters. The model used physicochemical and mechanical test results from 60 marl soil samples taken from marl formations in the Neogene basin in central Iran (Tehran, Qom and Saveh regions). The models were designed to use different input data sets and structures to determine which soil properties and ANN structures correlate well with the swelling potential parameter. Electrical conductivity (EC) of saturated soil was a new input parameter used in addition to the physical and soil index parameters that include the atterberg limit, activity, content of the clay and silt, initial of porosity ratio and dry density. Values of RMSE, R2 and MCE (evaluation criteria) related to the best model with the physical parameters LL, PI, A, M, C and Yd0 are respectively 0.89, 2.3, 0.84, and for the best model with the physical parameters LL, PI, M, C, Yd0 and EC are respectively 0.92, 1.7, and 0.91.The results of the evaluation criteria models show that inclusion of EC improved the accuracy of the model. It was found that the accuracy of the generalizations and estimations of the ANN models was further increased by clustering data before the data division stage by k-means method to Compared with hierarchical method.
