• Journal of the Korean GEO-environmental Society
• /
• v.5 no.3
• /
• pp.17-30
• /
• 2004

In this study, a new formula of settlement at the head of IGM was suggested and the applicability of suggested formula was verified with field test results. This suggested formula was the function of the settlement at the shaft head and the elastic compression of shaft. The applicability of suggested formula was verified with the result of in-situ load test. Also, the bearing capacity of drilled shaft with the IGM's theory was compared with those of classical theories. The results showed that classical method showed smaller values of bearing capacity than those of field load test data. The results of analysis also showed that the suggested formula and IGM's theory were applicable for the estimation of bearing capacity with the increase of shaft settlement. Especially, settlement correction factor($k_m$), which reflects ground condition and load transfer characteristics, increases as the applying load and shaft deformation increase. This suggested formula was applicable for medium density or higher density of soil condition and $k_m=1$ means yielding load for firm soil condition.

• Journal of the Korean Geotechnical Society
• /
• v.15 no.1
• /
• pp.79-98
• /
• 1999

In the present study, a simple finite element method of analysis to predict non-uniform settlements at the interface between the mat foundation and foundation soils is proposed. Based on the proposed finite element method of analysis, the method to evaluate load sharing ratios of the foundation soils adjacent to the granular group piles is also presented. Further proposed is a procedure to estimate ultimate bearing capacity of the skirted granular group piles in a square pattern. To verify validity of the proposed methods and the estimated ultimate bearing capacity of the skirted group piles, comparisons are made with the results analyzed by using the PENTAGON3D FEM program. Finally, behavior characteristics with different reinforcement patterns of the skirts and the effect of an increase of ultimate bearing capacity due to the skirts are analyzed in connection with the design parameters.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.2
• /
• pp.339-347
• /
• 2007

Penetration tests were performed for two types of steel sheet piles which were driven in clay deposit and sand deposit. Penetration velocity data acquired from penetration tests were used in order to estimate bearing capacity and vibro-driveability of steel sheet piles. Bearing capacity values predicted from Davisson method and Bombard method were greater than that calculated from static bearing capacity formula by 11.9 times and 1.6 times respectively. Vibro-driveability predictions from $T\ddot{u}nkers$ method and ${\beta}$ method show correspondence to field test result fur sand deposit but not for clay deposit. From motor powers estimated by Savinov and Luskin method it can be seen that larger capacities of motor powers are required for clay deposit and adequate hammer was used for sand deposit.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2002.03a
• /
• pp.197-203
• /
• 2002

The estimation of pile bearing capacity is important since the design details are determined from the result. There are numerous ways of determining the pile design load, but only few of them are chosen in the actual design. According to the recent investigation in Korea, the formulas proposed by Meyerhof based on the SPT N values are most frequently chosen in the design stage. In the study, various static and dynamic formulas have been used in predicting the allowable bearing capacity of a pile. Further, the reliability of these formulas has been verified by comparing the perdicted values with the static and dynamic load test measurements. Also in cases, these methods of pile bearing capacity determination do not take the time effect consideration, the actual allowable load as determined from pile load test indicates severe deviation from the design value. The principle results of this study are summarized as follows : A a result of estimate the reliability in criterion of the Davisson method, in was showed that Terzaghi & Peck > Chin > Meyerhof > Modified Meyerhof method was the most reliable method for the prediction of bearing capacity. Comparisons of the various pile-driving formulas showed that Modified Engineering News was the most reliable method. However, a significant error happened between dynamic bearing capacity equation was judged that uncertainty of hammer efficiency, characteristics of variable , time effect etc... was not considered. As a result of considering time effect increased skin friction capacity higher than end bearing capacity. It was found out that it would be possible to increase the skin friction capacity 1.99 times higher than a driving. As a result of considering 7 day's time effect, it was obtained that Engineering News. Modified Engineering News. Hiley, Danish, Gates, CAPWAP(CAse Pile Wave Analysis Program ) analysis for relation, respectively, $Q_{u(Restrike)}$ $Q_{u(EOID)}$ = 0.971 $t_{0.1}$, 0.968 $t_{0.1}$, 1.192 $t_{0.1}$, 0.88 $t_{0.1}$, 0.889 $t_{0.1}$, 0.966 $t_{0.1}$, 0.889 $t_{0.1}$, 0.966 $t_{0.1}$

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.3
• /
• pp.315-327
• /
• 2017

In this paper, we present a method for estimating of position and velocity of a moving target by using the range and the bearing measurements from multiple sensors of aiming unit. In many cases, conventional low cost gyro sensor and a portable laser range finder(LRF) degrade the accuracy of estimation. To enhance these problems, we propose two methods. The first is background image tracking and the other is principal component analysis (PCA). The background tracking is used to assist the low cost gyro censor. And the PCA is used to cope with the problems of a portable LRF. In this paper, we prove that our method is robust with respect to low-frequency, biased and noisy inputs. We also present a comparison between our method and the extended Kalman filter(EKF).

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1846-1856
• /
• 2016

This study proposes a novel feature point initialization method in order to improve the accuracy of feature point positions by fusing a vision sensor and a lidar. The initialization is a process that determines three dimensional positions of feature points through two dimensional image data, which has a direct influence on performance of a 6-DoF bearing-only SLAM. Prior to the initialization, an extrinsic calibration method which estimates rotational and translational relationships between a vision sensor and lidar using multiple calibration tools was employed, then the feature point initialization method based on the estimated extrinsic calibration parameters was presented. In this process, in order to improve performance of the accuracy of the initialized feature points, an iterative automatic scaling parameter tuning technique was presented. The validity of the proposed feature point initialization method was verified in a 6-DoF bearing-only SLAM framework through an indoor and outdoor tests that compare estimation performance with the previous initialization method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.7
• /
• pp.536-542
• /
• 2016

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.3
• /
• pp.251-268
• /
• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.10 no.6
• /
• pp.213-223
• /
• 2006

In this study, artificial neural network is applied to the evaluation of bearing capacity of the PHC auger-drilled piles at sites of domestic decomposed granite soils. For the verification of applicability of error back propagation neural network, a total of 168 data of in-situ test results for PHC auger-drilled plies are used. The results show that the estimation of error back propagation neural network provide a good matching with pile test results by training and these results show the confidence of utilizing the neural networks for evaluation of the bearing capacity of piles.

• Geomechanics and Engineering
• /
• v.37 no.6
• /
• pp.629-641
• /
• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.