• Computers and Concrete
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• 제19권6호
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• pp.711-716
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• 2017

This paper presents a method using artificial neural networks (ANNs) to predict the residual moment capacity of thermally insulated reinforced concrete (RC) beams exposed to fire. The use of heat resistant insulation material protects concrete beams against the harmful effects of fire. If it is desired to calculate the residual moment capacity of the beams in this state, the determination of the moment capacity of thermally insulated beams exposed to fire involves several consecutive calculations, which is significantly easier when ANNs are used. Beam width, beam effective depth, fire duration, concrete compressive and steel tensile strength, steel area, thermal conductivity of insulation material can influence behavior of RC beams exposed to high temperatures. In this study, a finite difference method was used to calculate the temperature distribution in a cross section of the beam, and temperature distribution, reduction mechanical properties of concrete and reinforcing steel and moment capacity were calculated using existing relations in literature. Data was generated for 336 beams with different beam width ($b_w$), beam account height (h), fire duration (t), mechanical properties of concrete ($f_{cd}$) and reinforcing steel ($f_{yd}$), steel area ($A_s$), insulation material thermal conductivity (kinsulation). Five input parameters ($b_w$, h, $f_{cd}$, $f_{yd}$, $A_s$ and $k_{insulation}$) were used in the ANN to estimate the moment capacity ($M_r$). The trained model allowed the investigation of the effects on the moment capacity of the insulation material and the results indicated that the use of insulation materials with the smallest value of the thermal conductivities used in calculations is effective in protecting the RC beam against fire.

• 산업경영시스템학회지
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• 제33권4호
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• 한국농공학회지
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• 제39권5호
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• pp.64-70
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• 1997

Sludge is generated in the process of water and wastewater treatment, and it has been causing various problems environmentally and economically. The firing technology in pottery industry was applied to the sludge treatment, and the final product was called artificial soil. For the production of artificial soil, lime and chabazite was used as additive, and the mixed material was thermally treated in the firing kiln at $300^{\circ}$ temperature for about 15 minutes. The physico-chemical characteristics of the artificial soil was analyzed and it showed that the artificial soil could be used as a soil conditioner for farmland. The concentrations of the toxic heavy metals in the artificial soil were lower than those in the soil quality standard for farmland. It was high in permeability, total nitrogen and total phosphorous concentrations and surface area of the artificial soil compared to the common field soil. Preliminary cost analysis showed that the sludge treatment cost for artificial soil was less than the disposal cost in the current landfill disposal method. This study illustrated that the artificial soil production process can be a feasible alternative for sludge treatment, and produced artificial soil may he applied to farmland without causing significant adverse effect. Further study is recommended for practical application of the system and verification of the longterm effect of the artificial soil on farmland.

• 한국전기전자재료학회논문지
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• 제12권1호
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• pp.1-10
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• 1999

In this paper, we have investigated the effects of the environmental factors on Low Molecular Weight (LMW) silicone fluid contents existing in high temperature vulcanized (HTV) silicone rubber sample, using dipping method and contact angle, current measurement. Artificial treatments such as immersion in water, elevated temperature, UV irradiation and dry band arcing under salt-fog condition are selected as the environmental factors. This results will be helpful to investigate the degradation with time and to expect a life time, because the LMW silicone content, which is important to recovery the hydrophobicity of silicone rubber surface, show different results by each environmental factors.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.256-272
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the newly developed techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs, field survey data, and a spatial database of the topography, soil type, timber cover, geology and land use. The landslide-related factors (slope, aspect, curvature, topographic type, soil texture, soil material, soil drainage, soil effective thickness, timber type, timber age, and timber diameter, timber density, geology and land use) were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods. For this, the weights of each factor were determinated in 3 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated and the susceptibility maps were made with a GIS program. The results of the landslide susceptibility maps were verified and compared using landslide location data. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to maintain precision and accuracy.

• Structural Engineering and Mechanics
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• 제75권5호
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• pp.633-642
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• 2020

Artificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gâteaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E_t/E_b), a shear correction factor (k_s), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2000년도 춘계 학술대회논문집
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• pp.27-32
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• 2000

In this study, the three-dimensional blood flow within the sac of KTAH(Korean artificial heart) is simulated using fluid-structure interaction model. The numerical method employed in this study is the finite element commercial package ADINA. The thrombus formation is one of the most critical problems in KTAH. High fluid shear stress or stagnated flow are believed to be the main causes of these disastrous phenomenon. We solved the fluid-structure interaction between the 3D blood flow in the sac and the surrounding sac material. The sac material is assumed as linear elastic material and the blood as incompressible viscous fluid. Numerical solutions show that high shear stress region and stagnated flow are found near the upper part of the sac and near the comer of the outlet during diastole stage.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.221-222
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• 2021

Conventionally, in material management at a construction site, the type, size, and quantity of materials are identified by the eyes of the worker. Labor-intensive material management by manpower is slow, requires a lot of manpower, is prone to errors, and has limitations in that computerization of information on the identified types and quantities is additionally required. Therefore, a method that can quickly and accurately determine the type, size, and quantity of materials with a minimum number of workers is required to reduce labor costs at the construction site and improve work efficiency. In this study, we developed an automated convolution neural network(CNN) and computer vision technology-based rebar size and quantity estimation system that can quickly and accurately determine the type, size, and quantity of materials through images.

• Steel and Composite Structures
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• 제49권6호
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• pp.633-643
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• 2023

In this study, the natural frequencies of Functional Graded Materials (FGM) plates are predicted using Artificial Neural Network (ANN). A model based on Third-order Shear Deformation Theory (TSDT) and FEM is used to train the ANN model. Different training methods are tested to simulate input and output dependency. As this is a parametric model, several architectures and optimization algorithms were tested. The proposed model allows us to minimize the CPU time to evaluate candidate material properties for FGM plate material selection and demonstrate their influence on dynamic behavior. Consequently, the time required for the FGM design process (candidate materials for material selection) and the geometric optimization of the FGM structure would remain reasonable. The ANN model can help industries to produce FGM plates with good mechanical properties of the selected materials. I addition, this model can be used to directly predict vibration behavior by testing a large number of FGM plates, representing all possible combinations of metals and ceramics in today's industry, without having to solve any eigenvalue problems.

• Transactions on Electrical and Electronic Materials
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• 제16권1호
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• pp.25-28
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• 2015

In this study, a basic research on artificial liver was performed for its application to people on the waiting list of liver transplant or patients with hepatic insufficiency. Artificial livers are generally classified into mechanic type, bioartificial type, and hybrid type. An extracorporeal circulation device was examined herein, which is indispensable in the application of an artificial liver, for its effectiveness in supporting the recovery of liver functions. Extracorporeal circulation system is a treatment and life-support system which sends out the patient's blood, removes toxicity by various methods, and then sends the blood back to the interior of the body. This study used an extracorporeal circulation system which enables the Plasma Perfusion by CVVH method, and applied the program of Bioateco corp. Animals with acute hepatic insufficiency were produced to apply the extracorporeal circulation device. As a result, their ammonia, bilirubin, SGOT, SGPT, and bile acid levels rose, confirming the liver function restoration in the experimental animals.