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Site exploration and estimation of soil attributes are key parts of a geotechnical outline process. Geotechnical engineers must focus the normal qualities and variability of soil properties. In-situ testing is turning out to be progressively essential in geotechnical engineering, as basic laboratory tests may not be solid while more advanced lab te...
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Increased access to the polar regions requires increased infrastructure. Design and construction of this infrastructure can only be achieved by accurately estimating surface bearing capacity. We reviewed contemporary methods for determining bearing capacity in soils and applied these to data obtained from almost 100 cone penetration tests conducte...
This paper comprises the study and analysis of Baghdad soil for eight geotechnical properties, which extract from field experiments of 630 boreholes with depth taken to 30m and representing 200 sites. Soil investigation reports are composed from altered laboratory tests. The soil layers. Divided into each 2m, which means 15 studied ' layers and soi...
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... The corrected SPT is presented corresponding to the depth of the boreholes in figure 7a which show the variations of SPT with depth. Usually, this kind of variations occur due to the variations of grain size, compactness, cementing material, relative density, effective overburden, lateral pressure, and fabric of the soil [19]. ...
... Fahim et al. [13] developed liquefaction hazard maps for Dhaka city using LPI and Cumulative Frequency (CF) distribution. Debojit et al. [26] emphasized the significance of standardized and carefully evaluated in-situ tests, like Standard Penetration Test (SPT), for accurate geotechnical engineering results. ...
Bangladesh is an earthquake-prone region. In the near future, it can witness an earthquake of magnitude 7 to 7.5 due to the origin of Dauki fault. Structures built on soft or loose soil can experience catastrophic damage due to liquefaction phenomenon during seismic excitation. Therefore, it is imperative to know the proper site characteristics and liquefaction susceptibility of earthquake-prone areas. Jolshiri Abashon is newly developed on the reclaimed land which has been filled up with loose sand and has a higher chance of liquefaction. In this study, the subsoil condition and geotechnical parameters of soil have been evaluated for Jolshiri using the American Society for Testing and Materials (ASTM). The evaluated soil properties indicate that there are mainly three types of soil layers i.e., loose sand, low plastic silt, and dense silty sand. The upper layer consists of loose sand up to 7.5 m depth that is expected to have high liquefaction potential. The middle layer comprises very soft to stiff soil which can experience large settlements. The last layer is dense to very dense silty sand with high shear strength properties and low compressibility. Moreover, this paper aims to generate a liquefaction hazard map for the studied area in ArcGIS software based on the Liquefaction potential index (LPI) value. The LPI value obtained for different boreholes shows a range around 20 to 30 which delineates that the Jolshiri Abashon area has very high liquefaction potential.
... Important soil parameters for assessing geotechnical properties are SPT-N value, Dry Density, Moisture content, Particle size distribution (Cu, Cc), Liquid limit, Plastic Limit etc. [2]. The resistance property of soil can be measured by its SPT-N value during the soil penetration test [3]. Dry Density refers to the mass of soil per unit volume when it is completely in a dry state. ...
Cohesion is an important soil strength parameter for the overall structure and
quality of building foundations as well as slope stability. For a civil engineer�ing project, cohesion (c) can be determined directly from mainly unconfined
compression tests, direct shear tests, and triaxial tests of soil. However, it’s
quite challenging to collect soil samples as there are time and cost constraints,
as well as a diversity of soil deposits. Hence, this research aims to demon�strate a simplified method in order to determine the strength parameter of
cohesive soil. Here, we propose an alternative solution adopting statistical
correlations and machine learning techniques to establish correlations be�tween the liquid limit, plastic limit, moisture content and %fine of soil with
the strength parameter. In laboratory testing, these parameters can be ob�tained easily and these tests are relatively simple, quick to perform and also
comparatively inexpensive. Hence, several test results were used from 100 bo�reholes which were soft soil or silty clay-type soil. Using the collected in-situ
and lab test results of soil samples, a Multiple Linear Regression (MLR), Ran�dom Forest Regression (RFR) and Machine Learning (ML) model will be de�veloped to establish a relationship between cohesion and the available test re�sults. In order to assess the performances of both models, several perfor�mance indicators like: correlation coefficient (R2
), mean squared error (MSE),
root mean square error (RMSE), and mean average error (MAE) will be used.
These correlation coefficients will be used to demonstrate the prediction ca�pacity and accuracy of both models. It should be noted that this approach will
substitute the conventional testing required for strength parameters, which is
both expensive and time-consuming.
... The process is suitable to model complex problems where the relationships between the model variables and parameters are unknown for the model process of the complex problem. The use of the different numerical and graphical procedures can be served the geotechnical engineer as the exceptionally compelling instruments [1]. ...
... The test is applicable to a widely ranged soil conditions. Although the use of this test is prevalent in subsurface investigations, it has some majör drawbacks [1]. With the advance of modern geotechnical engineering the actual driving energy of the SPT entering the rods was measured easily as described in ASTM D4633 by energy measurement devices. ...
Site exploration, characterization and prediction of soil properties by in-situ test are key parts of a geotechnical preliminary process. In-situ testing is progressively essential in geotechnical engineering to recognize soil type and stratigraphy alongside. In this study, a general regression neural network (GRNN) model was developed for estimating standard penetration resistance (SPT-N) value. In order to develop the GRNN model, 121 SPT-N values have been collected from 13 boreholes spread over an area of 17 km 2 of Izmir, Turkey including fine-grained deposits of mostly silt and clay with weathered gravel and sand. While developing the model, borehole location coordinates and soil type percentages were used as input parameters. The results obtained from the models were compared with those obtained from the field tests. Moreover, several performance indices, such as determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were calculated to check the prediction capacity of the model developed. The obtained indices make it clear that the GRNN model has shown high prediction performance.
The cone penetration test (CPT) has gained significant popularity in site investigation and geotechnical design, with increasing demand for accurate correlations to estimate various geotechnical properties. In contrast, the standard penetration test (SPT) is commonly used but has limitations, such as variations in results introduced by different operators conducting the test and discrete recording. This study focuses on establishing correlations between CPT parameters, SPT and total unit weight to address the gap in CPT-based correlations for soil parameters, particularly in the Dhaka metropolitan development plan (DMDP) area of Bangladesh. The research methodology involves field data collection, including SPT and CPT tests, across various soil layers in the DMDP area. The data collection process involved drilling 54 boreholes, conducting SPT and CPT tests, and obtaining disturbed samples for laboratory testing to determine soil type classifications and total unit weight. Existing correlations from previous studies were reviewed, and new correlations were developed specifically for the DMDP area. Through thorough data analysis and statistical techniques, multiple linear regression models are formulated to establish empirical correlations for the SPT N value and total unit weight of soil from the CPT parameters. The validity and accuracy of the correlations are assessed using statistical measures such as the coefficient of determination (R-squared value). The dataset used in this study exhibits noise stemming from diverse sources, including measurement errors and environmental factors. The variability caused by noise may influence the accuracy of our regression model, potentially resulting in a lower R-squared value. The proposed correlations are developed for all soil types and specific to each soil type found within the DMDP area. Similarly, correlations are established for estimating soil total unit weight from CPT parameters, considering different soil parameters and specific soil types. The graphical analysis demonstrates a significant correlation between the observed and estimated values of SPT N and total unit weight, with minor deviations, indicating the accuracy of the developed correlations. These correlations can enhance subsurface data reliability, improve geotechnical design accuracy and provide a cost-effective approach to site investigations, which enables engineers to leverage the strengths of both testing methods, leading to more robust and informed engineering decisions.
In this article, we define the concept of intuitionistic fuzzy parameterized complex multi-fuzzy soft set, which is a generalized mathematical model of fuzzy parameterized complex multi-fuzzy soft sets, in order to better express multi-dimensional data. In addition, some basic operations such as subset, complement, union and intersection for intuitionistic fuzzy parameterized complex multi-fuzzy soft sets are examined and some related properties are given.
