• Magazine of the Korean Society of Agricultural Engineers
• /
• v.43 no.4
• /
• pp.106-112
• /
• 2001

Recent advance in material technology has accelerated the development of high strength concrete using lightweight artificial aggregates. The lightweight concrete has many advantages that the reduction of dead loads and the increase in load capacity can offer. In this study the lightweight polymer concrete using unsaturated polyester resin and lightweight aggregate were prepared and tested for testing the physical and the mechanical properties. The compressive strengths of lightweight polymer concretes with apparent specific gravity for 1.32 to 1.78 were 250 to 470 kfg/cm$^2$ and flexural strengths were measured to be in the range of 1/3-1/4 of compressive strength.

• Journal of the Korea Institute of Building Construction
• /
• v.22 no.6
• /
• pp.619-630
• /
• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Proceedings of the Materials Research Society of Korea Conference
• /
• 2009.11a
• /
• pp.19.2-19.2
• /
• 2009

금속을 용해 응고시킬 때 생성되는 소위, 주조 결함이나 소결금속 내의 기공은 재료의 성능이나강도를 현저하게 낮추는 결함으로서 예전부터 기피되어 왔다. 또한, 재료공정에있어서도 여하의 기공이나 기포가 없는 치밀한 고강도 및 고기능성 재료를 개발하는 것에 최대한의 주의와 관심을 기울여 왔다. 그렇지만, 우리가 자연계의 천연물이나 인공물을 둘러보면 그 대부분이다공질임을 쉽게 눈치챌 수 있다. 예를 들어 목재, 지엽등의 생물을 시작해서 콘크리트 등의 인공물, 우리 체내의 뼈도 전형적인 다공질구조로 구성되어 있다. 이러한 구조로부터 재료의 재질제어 이외에 구조제어라는 새로운 어프로치를 고려할 수 있고, 최근 들어, 금속재료에 있어서도 이러한 다공질구조에 관한 연구가활성화되어 충격흡수재, 생체재료, 베어링재료 등의 다양한응용이 전개되고 있다. 특히, 원주상의 방향성 기공을 갖는 로터스금속은 기존의 복잡한구조의 다공질금속보다 뛰어난 기계적 성질을 갖는다. 이러한 다공질금속은 일방향응고할 때 생성하는 과포화가스원자를 석출시켜 기공을 일방향으로 성장시킨다. 즉, 융점에서의 고상과 액상의 가스 용해도 차를 이용하는 것으로서 응고시에 고용할 수 없는 가스원자가 기공을 형성한다. 이와같이 제조한 방향성 다공질금속은 BT (인플란트, 생체적합성, 저탄성, 경량), ST (초음속기엔진부품, 경량), IT (고성능수냉모듈), ET(고온촉매, 필터)의 분야로의 응용이 기대된다. 본 강연에서는 방향성 다공질금속의 제조법, 특성 및 응용을 포함하여그 동안의 연구성과 및 앞으로의 과제 등을 소개하고자 한다.

• Journal of the Korea Concrete Institute
• /
• v.22 no.1
• /
• pp.19-27
• /
• 2010

Aging and severe environments are major causes of damage in reinforced concrete (RC) structures such as buildings and bridges. Deterioration such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for strengthening and rehabilitation of RC structures have been developed in the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to the conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) in predicting behavior of circular type concrete column retrofitted with FRP. To construct training and testing dataset, experiment results for the specimens which have different retrofit profile are used. Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber and size of specimens are selected as input parameters to predict strength, strain, and stiffness of post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5A
• /
• pp.399-406
• /
• 2010

In this paper, the optimization of frictional bearings that applied to improve the seismic performance of conventional bridges were conducted. The nonlinear dynamic analysis of steel bridges and concrete bridges are carried out with the El Centro and artificial earthquake motions, and the reponses of the bridges were optimized by genetic algorithm. The object functions were considered with two parameters, such as shear forces and displacements at bearing, and the optimum object functions were searched by varying the weighting factors of the two parameters. As results, in case of the steel bridges, the optimum results were obtained when larger weight factor was imposed to the shear force. However, in case of concrete bridges, larger weight factor was need to the displacement for optimum results.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.5
• /
• pp.127-134
• /
• 2022

In this study, RCPTs (Rapid Chloride Penetration Test) were performed for fly ash concrete with curing age of 4 ~ 6 years. The concrete mixtures were prepared with 3 levels of water to binder ratio (0.37, 0.42, and 0.47) and 2 levels of substitution ratio of fly ash (0 and 30%), and the improved passed charges of chloride ion behavior were quantitatively analyzed. Additionally, the results were trained through the univariate time series models consisted of GRU (Gated Recurrent Unit) algorithm and those from the models were evaluated. As the result of the RCPT, fly ash concrete showed the reduced passed charges with period and an more improved resistance to chloride penetration than OPC concrete. At the final evaluation period (6 years), fly ash concrete showed 'Very low' grade in all W/B (water to binder) ratio, however OPC concrete showed 'Moderate' grade in the condition with the highest W/B ratio (0.47). The adopted algorithm of GRU for this study can analyze time series data and has the advantage like operation efficiency. The deep learning model with 4 hidden layers was designed, and it provided a reasonable prediction results of passed charge. The deep learning model from this study has a limitation of single consideration of a univariate time series characteristic, but it is in the developing process of providing various characteristics of concrete like strength and diffusion coefficient through additional studies.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.28 no.5
• /
• pp.277-283
• /
• 2016

Tetrapod is one of the most widely used concrete armor units for rubble mound breakwaters. The calculation of the stability number of Tetrapods is necessary to determine the optimal weight of Tetrapods. Many empirical formulas have been developed to calculate the stability number of Tetrapods, from the Hudson formula in 1950s to the recent one developed by Suh and Kang. They were developed by using the regression analysis to determine the coefficients of an assumed formula using the experimental data. Recently, software engineering (or machine learning) methods are introduced as a large amount of experimental data becomes available, e.g. artificial neural network (ANN) models for rock armors. However, these methods are seldom used probably because they did not significantly improve the accuracy compared with the empirical formula and/or the engineers are not familiar with them. In this study, we propose an explicit method to calculate the stability number of Tetrapods using the weights and biases of an ANN model. This method can be used by an engineer who has basic knowledge of matrix operation without requiring knowledge of ANN, and it is more accurate than previous empirical formulas.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.4 no.3
• /
• pp.322-329
• /
• 2016

This study is about the method to control the quality of material used to repair leakage and crack of concrete structure and suggests the "Temperature Stability Test Method" as a follow-up study. In the result of performance evaluation for 45 samples of 15 types in 5 series, the temperature stability test showed different material changes including rolling down, volume change, and color change as they are frozen and melt repeatedly in the somewhat extreme conditions at low($-20^{\circ}C$) and high($60^{\circ}C$) temperatures, where 13 samples (approx. 29%) and 32 samples (approx. 71%) showed leakage, respectively, in the permeability test to evaluate leakage. This result shows the enough importance of setting the quality control criteria of leakage repair material currently used to maintain concrete structures considering the temperature conditions, and proves the applicability of the Temperature Stability Test Method as a standard test method to ensure long-term durability of concrete structure.

• International Journal of Highway Engineering
• /
• v.1 no.1
• /
• pp.107-119
• /
• 1999

The joints in the jointed concrete pavement provide a control against transverse or longitudinal cracking at slab, which may be caused by temperature or moisture variation during or after hydration. Without control of cracking, random cracks cause more serious distresses and result in structural or functional failure of pavement system. However, joints nay cause distresses due to its inherent weakness in structural integrity. Thus, the evaluation at joint is very important. and the joint-related distresses should be evaluated reasonably for economic rehabilitation. The purpose of this paper was to develop an evaluation system at joints of jointed concrete pavement using finite element analysis program, ILLI-SLAB, and nondestructive testing device. FWD. To develop an evaluation system for JCP, a sensitivity analysis was performed using ILLI-SLAB program with a selected variables which might affect fairly to on the performance of transverse joints. The most significant variables were selected from precise analysis. An evaluation charts were made for jointed concrete pavement by adopting the field FWD data. It was concluded that the variables which most significantly affect to pavement deflections are the modulus of subgrade reaction(K) and the modulus of dowel/concrete interaction(G), and limiting criteria on the performance of joints at JCP are 300pci. 500,000 lb/in. respectively. Using these variables and FWD test, a charts of load transfer ratio versus surface deflection at joints were made in order to evaluate the performance of JCP. Practically, Chungbu highway was evaluated by these evaluation charts and FWD field data for jointed concrete pavement. For Chungbu highway, only one joint showed smaller value than limiting criterion of the modulus of dowel/concrete interaction(G). The rest joints showed larger values than limiting criteria of the modulus of subgrade reaction(K) and the modulus of dowel/concrete interaction(G).

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.17 no.2
• /
• pp.131-140
• /
• 2004

It really needs fuzzy decision making of integrity assessment considering about both durability and load carrying capacity for maintenance and administration, such as repairing and reinforcing. This thesis shows efficient models about reinforced concrete structure using CART-ANFIS. It compares and analyzes decision trees parts of expert system, using the theory of fuzzy, and applying damage & diagnosis at reinforced concrete structure and decision trees of integrity assessment using established artificial neural. Decided the theory of reinforcement design for recovery of durability at damaged concrete ＆ the theory of reinforcement design for increasing load carrying capacity keep stability of damage and detection. It is more efficient maintenance and administration at reinforced concrete for using integrity assessment model of this study and can carry out predicting cost of life cycle.