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The compression index (Cc), which is used to calculate the consolidation settlement of fine-grained soils, can be determined through consolidation testing. Given that exploring the soil in a local region is highly important to determine the correlation between the Cc and other soil indices, the present study investigated these correlations in undisturbed and disturbed samples through 130 consolidation tests and determining the Cc of Tehran clay. The results are suggestive of the validity of the linear correlation between the Cc and the unit weight and initial void ratio of the soil, with several relations presented to estimate the Cc of Tehran clay soil. In contrast, the initial water content, liquid limit and the plasticity index do not produce reliable correlations with the Cc of the local clay soil, and a relation based on these index parameters cannot be recommended in this area. Further, the presented empirical correlations were compared with the existing ones. More over time-displacement and e-log σ’ graphs for undisturbed and disturbed samples are compared and stress history of the site are presented. The results are significant in terms of engineering applications, saving time and money and provides an initial estimation of compression index.
相似文献Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σn) and anchorage length (L) and the least effective parameters are average particle size (D50) and anchor base area (Ab).
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