Kashitij Guleria; Ravi Kumar Sharma
Abstract
This paper discusses the applications of industrial waste like waste foundry sand (10%, 20%, 30%, and 40%) and calcium carbide residue (3%, 6%, 9%, and 12%) blended with polypropylene fibre (0.25%, 0.50%, 0.75%, and 1%) for soil stabilization. The purpose of this study is to develop a composite of clayey ...
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This paper discusses the applications of industrial waste like waste foundry sand (10%, 20%, 30%, and 40%) and calcium carbide residue (3%, 6%, 9%, and 12%) blended with polypropylene fibre (0.25%, 0.50%, 0.75%, and 1%) for soil stabilization. The purpose of this study is to develop a composite of clayey soil mixed with different additives, so it can be used for improving the geotechnical properties of the clayey soil. Multiple tests are conducted including differential free swell, Atterberg's limits test, compaction tests, unconfined compression test (UCS), and California-bearing ratio test (CBR) on clay soil individually and in different combinations and proportions with additive mixed with each other. The optimum percentage for the additives is found by performing differential free swell index and Atterberg limits test. The results demonstrate that the inclusion of additives in the clayey soil decreases the differential free swell and plasticity index of the composite but raises the composite UCS and CBR values. The maximum increase in the UCS and CBR values is obtained for optimum combination of C:PP:WFS:CC::76.25:0.75:20:3. Based on the CBR values, the thickness of flexible pavement is designed using the IITPAVE software. The results of the software analysis show a reduction in the pavement thickness for various values of commercial vehicles per day (1000, 2000, and 5000) for all combinations. The maximum reduction in layer thickness and construction costs is noticed for C:PP:WFS:CC:76.25:0.75:20:3. To further examine the improvement in the geotechnical properties of soil, calcium carbide residue, and waste foundry sand can be blended with nano-additives for potential uses.
Rock Mechanics
M. Rezaei; M. Asadizadeh
Abstract
Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which ...
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Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especially during the drilling operation in cracked, fractured, and weak rocks. Therefore, some attempts have recently been made to develop the indirect methods, i.e. intelligent predictive models for rock UCS estimation, which require no core preparation and laboratory equipment. This work focuses on the application of new combinations of intelligent techniques including adoptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA), and particle swarm optimization (PSO) in order to predict rock UCS. These models were constructed based on the collected laboratory datasets upon 93 core specimens ranging from weak to very strong rock types. The proposed hybrid model results were compared with each other, and the real data and multiple regression (MR) results. These comparisons were made using coefficient of correlation, mean of square error, mean of absolute error, and variance account for indices. The comparison results proved that the ANFIS-GA combination had a relatively higher accuracy than the ANFIS-PSO combination, and both had a higher capability than the MR model. Furthermore, the ANFIS-GA and ANFIS-PSO model results were completely in accordance with the UCS laboratory test, and they were more accurate than the previous single/hybrid intelligent models. Lastly, a parametric study of the suggested models showed that the density and Schmidt hammer rebound had the highest influence, and porosity had the lowest influence on the output (UCS).
