• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• The Journal of Engineering Geology
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• v.20 no.2
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• pp.183-189
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• 2010

Fault gouge samples were collected from the fault cores of the boundary faults between the Cretaceous Basement and the Tertiary Waeup Basin. Fractal dimensions (D) were obtained by using survivor grains which were analysed from six thin sections of the gouges under the optical microscope. The elliptical survivor grains show a shape preferred orientation almost parallel to clay foliation in matrix, suggesting that it was formed by the rotation of the survivor grains in abundant fine-grained matrix during repeated fault slips. The size distributions of the survivor grains follow power-laws with fractal dimensions in the 2.40-3.02 range. D values of all samples but one are higher than a specific D value equal to 2.58 which predicts the self similarity of fragmentation process in constrained comminution model (Sammis et al., 1987), which indicates large fault slip and multiple faulting. Probably the higher D values than 2.58 mean the non-self-similar evolution of cataclastic rocks where fragmentation mechanism changed from constrained comminution to the grain abrasion accompanying selective fracture of larger grains.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.96-108
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• 1996

In this paper, we propose an object-oriented coding method in low bit-rate channels using block-based motion vectors and residual image compensation. First, we use a 2-stage algorithm for estimating motion parameters. In the first stage, coarse motion parameters are estimated by fitting block-based motion vectors and in the second stage, the estimated motion parametes are refined by the gradient method using an image reconstructed by motion vectors detected in the first stage. Local error of a 6-parameter model is compensted by blockwise motion parameter correction using residual image. Finally, model failure (MF) region is reconstructed by a fractal mapping method. Computer simulation resutls show that the proposed method gives better performance than the conventional ones in terms of th epeak signal to noise ratio (PSNR) and compression ratio (CR).

• Journal of KSNVE
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• v.7 no.3
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• pp.439-447
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• 1997

In this study, the dynamic instabilities of a nonlinear elastic system subjected to follower forces are investigated. The two-degree-of-freedom double pendulum model with nonlinear geometry, cubic spring, and linear viscous damping is used for the study. The constant, the initial impact forces acting at the end of the model are considered. The chaotic nature of the system is identified using the standard methods, such as time histories, power density spectrum, and Poincare maps. The responses are chaotic and unpredictable due to the sensitivity to initial conditions. The sensitivities to parameters, such as geometric initial imperfections, magnitude of follower force, direction control constant, and viscous damping, etc., are analysed. Dynamic buckling loads are computed for various parameters, where the loads are changed drastically for the small change of parameters.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.341-347
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• 2000

To estimate crack growth rate and cycle ratio uniquely, many investigators have developed various kinds of mechanical parameters and theories. But, these have produced local solution space through single parameter. Neural Networks can perform pattern classification using several input and output parameters. Fatigue damage model by neural networks was used to recognize the relation between da/dN N/Nf, and half-value breadth ratio B/BO0, fractal dimension Df and fracture mechanical parameters in 2024-T3 ability to predict both crack growth rate da/dN and cycle ratio N/Nf within engineering estimated mean error (5%).

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.70-73
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• 1996

Many researchers had considered biological systems as linear systems. In many cases of biological systems, the phenomena that show the regular and periodic dynamics are considered the normal state. However, some clinical experiments reported, in some cases, the periodic signals represented the abnormal state. We assume that signals from human body system are generated from deterministic, intrinsic mechanisms and can be represented a simple equation that show nonlinear dynamics dependent on control parameters. The objective of our study is to model a nonlinear dynamics correctly from the nonlinear time series using the genetic programming method; to find a simple equation of nonlinear dynamics using collected time series and its nonlinear characteristics. We applied genetic programming to model RR interval of ECG that shows chaotic phenomena. We used 4 statistic measures and 2 fractal measures to estimate fitness of each chromosome, and could obtain good solutions of which chaotic features are similar.

• Journal of Korea Water Resources Association
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• v.32 no.4
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• pp.427-435
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• 1999

The Clark unit hydrograph is a three parameter synthetic unit hydrograph procedure that can be used in flood hydrology. The present work is an attempt to estimate parameters of the Clark model in ungaged basin by means of relationships that provides for the hydrologic similarity. The time area concentration curve was determined by analytic method and the Clark model was generalized by being made dimensionless form. Calculation of the concentration time was made with the formula fractal concept used, and the storage coefficient was estimated by the empirical and regional equation. Evaluation on Dongok basin was performed to prove the validity of the proposed model. The derived hydrograph predicted the observed hydrograph fairly well.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.121-130
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• 2024

Many foundation projects are built on red-bed soft rocks, and the damage evolution of this kind of rocks affects the safety of these projects. At present, there is insufficient research on the damage evolution of red-bed soft rocks, especially the progressive process from mesoscopic texture change to macroscopic elastoplastic deformation. Therefore, based on the dual-porosity characteristics of pores and fissures in soft rock, we adopted a cellular automata model to simulate the propagation of these voids in soft rocks under an external load. Further, we established a macro-mesoscopic damage model of red-bed soft rocks, and its reliability was verified by tests. The results indicate that the relationship between the number and voids size conformed to a quartic polynomial, whereas the relationship between the damage variable and damage porosity conformed to a logistic curve. The damage porosity was affected by dual-porosity parameters such as the fractal dimension of pores and fissures. We verified the reliability of the model by comparing the test results with an established damage model. Our research results described the progressive process from mesoscopic texture change to macroscopic elastoplastic deformation and provided a theoretical basis for the damage evolution of these rocks.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.53-61
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• 2016

In this paper, we propose an algorithm for generating lightning paths, which are more realistic than those of random tree based algorithm and faster than a physically based simulation algorithm. Our approach utilizes physically based Dielectric Breakdown Method (DBM) and approximates the electric potential field dramatically to generate the lightning path. We also show a guide path method for the lightning to avoid obstacles in a complex scene. Finally, our method renders fast and realistic lightning by considering physical characteristics for the thickness and brightness of the lightning stream. Our result of the lightning path shares similarity to natural phenomenon by having about 1.56 fractal dimensions, and we can generate the lightning path faster than a previous physically based algorithm. On the other hand, our method is difficult to apply on the real-time games yet, but our approach can be improved by performing the path generation algorithm with GPU in future.