• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.3
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• pp.109-113
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• 1999

The objective of this research was to develop growth media for artificial ground by blending calcined clay and coconut peat. To achieve this, aggregates of clay particles were mixed with disel oil and heated to high temperature(1150~120$0^{\circ}C$) to expand clays. The particle sizes of expanded clay were controlled to 2~5mm in diameter. Then expanded clayes were mixed with coconut peat and changes of soil physicochemical properties and their effect on plant growth of Hedera L. were determined. The infiltration rate of calcined clay was very high, but the water holding capacity, the cation exchange capacity(CEC), and the nutrient contents were low. The characteritics of coconut peat was vice verse to calcined clay. This indicates that the mixture of calcined clay and coconut peat have the better characteristics than each material. As compared to mineral soil, the infiltration rate, the water holding capacity, the CEC and the nutrient contents increased, but bulk density decreased to about 1/4. And, Hedera L. grown in the mixture of calcined clay and coconut peat(6:4, v/v) had higher plant height, longer leaf length, more total number of leaves per plant and fresh weight than that grown in mineral soil, but statistical differences were not observed between two treatments.

• Geomechanics and Engineering
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• v.3 no.4
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• pp.285-290
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• 2011

This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for determination of $f_s$ of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of $f_s$ of driven piles in clay.

• Coupled systems mechanics
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• v.8 no.6
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• pp.523-535
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• 2019

Artificial Intelligence (AI) is anticipated to be the future of technology. Hence, AI has been applied in various fields over the years and its applications are expected to grow in number with the passage of time. There has been a growing need for accurate, direct, and quick prediction of geotechnical and foundation engineering models especially since the success of each project relies on numerous amounts of data. In this study, two applications of AI in the field of geotechnical and foundation engineering are presented - spatial interpolation of standard penetration test (SPT) data and prediction of consolidation of clay. SPT and soil profile data may be predicted and estimated at any location and depth at a site that has no available borehole test data using artificial intelligence techniques such as artificial neural networks (ANN) based on available geospatial information from nearby boreholes. ANN can also be used to accelerate the calculation of various theoretical methods such as the one-dimensional consolidation theory of clay with high efficiency by using lesser computation resources. The results of the study showed that ANN can be a valuable, powerful, and practical tool in providing various information that is needed in geotechnical and foundation design.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.673-680
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• 2000

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a consolidation behavior of clay from soil parameter, site investigation data and the first settlement curve is proposed. The training and testing of the network were based on a database of 63 settlement curve from two different sites. Five different network models were used to study the ability of the neural network to predict the desired output to increasing degree of accuracy. The study showed that the neural network model predicted a consolidation behavior of clay reasonably well.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6C
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• pp.421-429
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• 2006

This study was undertaken to develop an analysis model representing a reliable estimation of rigidity of Korean soft clay using an artificial neural network (ANN). Data for the model development were obtained through a laboratory study, and were used for training and verification. The coefficient of correlation between the measured and predicted data using the developed model was relatively high. It demonstrates the potential application of ANN for the reliable estimation of Korean soft clay rigidity while past attempts at building such a mathematical model have proved difficult.

• Geophysics and Geophysical Exploration
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• v.8 no.3
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• pp.218-223
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• 2005

This study considers to electrical resistivity characteristics for clay minerals types and contents in fractured and fault zone. The electrical resistivity is measured for an artificial agar specimen with clay minerals instead of a natural rock. The artificial agar specimen with clay minerals was special worked in study. The clay minerals used are Kaolinite and Montmorillonite in test, the clay mineral contents increases until $0\~40\%$ to the same specimen. As results, the electrical resistivity of the specimen decreased gradually as the clay mineral contents increases for all types of clay minerals. Montmorillonite shows remarkably lower resistivity than Kaolinite, although its clay content is fewer than that of Kaolinite. Also, a proposed experimental expression shows a good correlation coefficient as high as 0.89 or more in all clay minerals.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.75-81
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• 2000

The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.

• Journal of Korea Foundry Society
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• v.4 no.2
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• pp.89-95
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• 1984

A Study on the Mechanical Properties of Molding Sand with Various Water/Clay Ratio A standard sample of molding sand was prepared by adding a various amount of bentonite, which has water/clay ratio from 0.4 to 0.6, into artificial sand, Hanyoung #6. The results obtained by measuring the room temperature properties of green mold are as follows. 1. This compressive strength of green molds which have 4% and 10% of bentonite decreased with increasing water/clay ratio, but the maximum strengths of 4.3 (psi) and 7.2 (psi) were observed in the samples with 6%, 8% bentonite respectively when the water/clay is 0.45. 2. The optimum water/clay ratio for strength and permeability increased from 0.4 to 0.5 with increasing clay. 3. The green compressive strength was proportional to the hardness. 4. Deformation increased with increasing water/clay ratio. 5. Flowability decreased with increasing water/clay ratio and clay content in molding sand.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.65-70
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• 2005

A low resistivity zone is found in many places such as a fractured fault zone, weathered zone and aquifer. The electrical resistivity is influenced mainly by pore fluid as well as the clay mineral types and contents, Hence, it is very important to understand the relationship between the electrical resistivity and clay contents associated with the low resistivity zone for geotechnical applications such as civil engineering. This study examines the characteristics of clay mineral types and contents to electrical resistivity through sample measurements, and proposes an expression relating the resistivity and clay content. The electrical resistivity is measured for an artificial agar specimen with clay minerals instead of a natural rock. The clay minerals used are Kaolinite and Montmorillonite. Montmorillonite shows remarkably lower resistivity than Kaolinite, although its clay content is fewer than that of Kaolinite. Also, the proposed expression shows a good correlation coefficient as high as 0.89 or more in all clay minerals.

• Ocean Systems Engineering
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• v.5 no.1
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• pp.41-54
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• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.