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Ebrahim Rahimi

Associate Professor of Applied Geology

Selected Publications

Ghasemy, A., Rahimi, E., Malekzadeh, A. Introduction of a new method for determining the particle-size distribution of fine-grained soils (2019) Measurement: Journal of the International Measurement Confederation, 132, pp. 79-86.

DOI: 10.1016/j.measurement.2018.09.041

Particle size distribution is determined by various methods such as laser diffraction scattering, image counting, sieving and sedimentation methods, none of which are perfect and need to be refined in the fields of soil science and civil engineering. This study tried to achieve a more accurate method for gradation of fine-grained soils. Regarding the concept of sedimentation and spectrophotometry, experimental and theoretical studies have been done on the particle size measurement of seventeen fine-grained soil samples. The diameter of the particles was determined on the basis of the Stokes’ law of settling velocity for particles in suspension placed in the cuvette whereas the percentage finer than these diameters were determined by a formula derived herein using the spectrophotometry method at the operating range of 900–1000 nm. The ratios between the concentration values at each reading time and the initial value were related to the percentage of particles passing. The gradation curves obtained by this method for the soil samples in this study correspond to the curves of microscopy imaging analysis. Compared with other methods, this test procedure and the related calculations are rather convenient. © 2018 Elsevier Ltd

AUTHOR KEYWORDS: Fine-grained soils; Gradation; Sedimentation; Spectrophotometry
INDEX KEYWORDS: Consolidation; Light transmission; Particle size; Particle size analysis; Sedimentation; Size distribution; Soils; Spectrophotometry; Suspensions (fluids); Testing, Concentration values; Fine grained soil; Gradation; Laser diffractions; Microscopy imaging; Particle size measurement; Settling velocity; Spectrophotometry methods, Soil surveys
PUBLISHER: Elsevier B.V.

Rahimi, E., Sharifi Teshnizi, E., Rastegarnia, A., Motamed Al-Shariati, E. Cement take estimation using neural networks and statistical analysis in Bakhtiari and Karun 4 dam sites, in south west of Iran (2018) Bulletin of Engineering Geology and the Environment, pp. 1-18. Article in Press.

DOI: 10.1007/s10064-018-1271-0

Water seepage from dam foundations causes reservoir water loss and raises the risk of dam instability. One method of remediation for controlling instability and leakage of these rock foundations is grouting. Since a considerable portion of the costs for dam construction is allocated to grouting, as a result, study of the influencing factors of cement take in grouting jobs is of paramount importance for each site. The most dominant parameters which play a decisive role in the efficiency of grouting are rock mass strength and permeability, grouting pressure, water-to-cement ratio, and properties of jointed rock mass such as joint aperture, roughness, and spacing. In this paper, the relationship between cement take and Q-system, aperture and spacing of joints, Lugeon number, depth of grouting, and grouting parameters such as grouting pressure and water-to-cement ratio is investigated via statistical analysis and artificial neural networks for two large concrete dam sites, Bakhtiari and Karun 4, located in southwest Iran. Karun 4 has been constructed while Bakhtiari is still under construction with respective heights of 230 and 325 m. The mentioned parameters, the relationships of which are found in relation to cement take, are determined based on engineering geology reports for all the 5-m segments of the trial grouting boreholes. Bivariate statistical analyses showed that the highest correlation (R = 0.64) exists between cement take and Q-system by a logarithmic relationship. In addition, statistical investigations based on multivariate analyses between cement take and all the mentioned variables show a poor correlation (R = 0.48) which encouraged the authors to use neural networks for finding a relationship between cement take and the influencing variables. This resulted in a higher correlation (R = 0.77, RMSE = 9.2) with respect to the statistical method. © 2018 Springer-Verlag GmbH Germany, part of Springer Nature

AUTHOR KEYWORDS: Artificial neural network; Joint characteristics; Lugeon; Properties of cement grout; Q-system; Regression
INDEX KEYWORDS: Cements; Concrete construction; Embankment dams; Grouting; Mortar; Neural networks; Reservoirs (water); Rock mechanics; Rocks; Seepage; Statistics, Cement grouts; Grouting parameters; Logarithmic relationship; Lugeon; Multi variate analysis; Q-system; Regression; Water to cement (binder) ratios, Multivariant analysis
PUBLISHER: Springer Verlag

Khodashahi, M., Rahimi, E., Bagheri, V. Earthquake-Induced Landslides Hazard Zonation of Rudbar-Manjil Using CAMEL Model (2018) Geotechnical and Geological Engineering, 36 (2), pp. 1319-1340.

DOI: 10.1007/s10706-017-0395-5

Earthquake-induced landslides are of the phenomena that have caused huge losses to human in recent years. Although many studies on the identification and characterization of landslides are generally done, the review of landslides caused by earthquakes is very young especially in Iran. This paper describes zoning of landslides triggered by 1990 Rudbar–Manjil earthquake with moment magnitude (Mw) 7.3 using a comprehensive areal model of earthquake-induced landslides (CAMEL). At first, the necessary information including disturbance distance (distance from river and road), ground strength class, ground moisture, shaking intensity, slope angle, slope height, soil depth, terrain roughness, material type, and vegetation were collected using aerial photos, Landsat satellite images, geological and topographic maps, and site investigation of the studied region. These data were digitized and weighed using the digital geographic information system in a comparative manner, and then concentration and hazard of the seismic landslides were predicted using the CAMEL program. It can be concluded that CAMEL displays a better performance in predicting and zoning earthquake-induced soil landslides than earthquake-induced rock landslides. © 2017, Springer International Publishing AG.

AUTHOR KEYWORDS: Fuzzy logic; Regional landslide hazard zonation; Rudbar–Manjil earthquake; Seismic landslides
INDEX KEYWORDS: Fuzzy logic; Geophysics; Hazards; Landslides; Maps; Satellite imagery; Seismology; Zoning, Digital geographic informations; Earthquake-induced landslides; LANDSAT satellite images; Landslide hazard; Moment magnitudes; Seismic landslides; Site investigations; Terrain roughness, Earthquakes
PUBLISHER: Springer International Publishing

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