• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.264-274
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• 2001

An integrated fatigue strength assessment system was computerized. The system developed consists of 9 modules: user interface, cycle counting, load history construction, data searching, fatigue properties estimation, fatigue data analysis, true stress and strain analysis, expert system for crack initiation life prediction, fatigue crack initiation and propagation life prediction. Fatigue strength database also was included in this system. The fatigue expert system helps a beginner to predict a fatigue crack initiation life in fatigue strength assessment. The expert system module in this system is developed on the personal computer by using C language and UNiK, an expert system developing tool. To evaluate the system, the results of test under variable loading of SAE and failure data from a field were analyzed. The evaluation show that the system provided fatigue life prediction within 3-scatter band and gave reasonable predictions. To get more accurate predictions of fatigue life without fatigue properties, we recommend utilizing the system along with the fatigue strength database.

• Computers and Concrete
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• v.11 no.4
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• International Journal of Concrete Structures and Materials
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• v.3 no.1
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• pp.57-61
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• 2009

The present work deals with the prediction, through models and experimental evaluation, of the bending strength of roller compacted concrete (RCC) for pavement applications. This concrete was manufactured using low cement proportioning (150 to $250\;kg/m^3$). The characterization of hardened RCC was carried out by experimental measurements of bending strengths. The predictions of these characteristics were achieved using empirical models. Comparison, of the values found in experiments with those empirically obtained, was made in order to choose and to propose the adapted and the most reliable models of prediction. The study showed that the bending strengths of the RCC mixture, experimentally found, can be also identified by models.

• Computers and Concrete
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• v.34 no.4
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• pp.427-446
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• 2024

The I-shaped steel reinforced recycled aggregate concrete (SRRC) composite structure has the advantages of high bearing capacity and environmental protection, and the interfacial bond strength is an important theory. To this end, the I-shaped SRRC bond strength and its calculation based on artificial neural network (ANN) will be studied. Firstly, 39 push out tests of I-shaped SRRC were conducted, the load-slip curve has obvious regularity, which is divided into 4 segments by 3 regular points. Three bond strengths were defined based on these three rule points, and the approximate ranges of their values and the laws of influence of each factor on them were found. Secondly, the Elman ANN model used for the prediction of bond strength was established, and the parameters of Elman ANN predicting I-shaped SRRC bond strength were studied, and the effects of detailed parameters on the prediction results were revealed. Finally, the bond strength of SRRC was predicted using Elman and BP (back propagation) neural network models, both of which showed good prediction results. This study is a theoretical basis for the design and fine simulation of I-shaped SRRC composite structures.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.287-290
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• 2000

Nowadays, strength control is performed by test of compressive strength of concrete which is taken in construction filed. But because it is possible to confirm only compressive strength of concrete by that way, it is difficult to performing strength control pr process plan, So, if we can predict compressive strength of concrete, we can decide when shores and forms can be removed safety, plan process efficiently. This study intends to propose basic data for strength control as determination the time of forwoak removal through investigating propriety of strength prediction using Freiesleben function.

• Smart Structures and Systems
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• v.16 no.3
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• pp.497-519
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• 2015

This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS's results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ($f_c^{\prime}$) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.179-182
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• 2002

Rapid Prototyping (RP) technologies provide the ability to fabricate initial prototypes from various model materials. Stratasys' Fused Deposition Modeling (FDM) is a typical RP process that can fabricate prototypes out of plastic materials, and the parts made from FDM were often used as load-carrying elements. Because FDM deposits materials in about $300\mutextrm{m}$ thin filament with designated orientation, parts made from FDM show anisotropic material properties. This paper proposes an analytic model to predict the tensile strength of FDM parts. Applying the Classical Lamination Theory, which was developed for laminated composite materials, a computer code was implemented. Tsai-Wu failure criterion was added to the code to predict the failure of the FDM parts. The tensile strengths predicted by the analytic model were compared with experimental data. The data and prediction agreed reasonably well to prove the validity of the model. In addition, a web-based advisory service was developed to provide to strength prediction and design rules for FDM parts.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.442-445
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• 2004

The acoustic target strength (TS) of submarine is associated with its active detection, positioning and classification. That is, the survivability of submarine depends on its target strength. So it should be managed with all possible means. An anechoic coating to existing submarine or changing of curvature can be considered as major measures to reduce the TS of submarine. It is mainly based on the prediction of its TS. Under this circumstances, a study on the more accurate numerical methods becomes big topic for submarine design. In this paper, Kirchhoff approximation method was adopted as a numerical tool for the physical optics region. Secondly, the scaled models of submarine were built and tested in order to verify its performance. Through the comparison, it was found out that the Kirchhoff approximation method could be good design tool for the prediction of TS of submarine.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.2
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.

• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.2
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• pp.137-146
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• 2001

The non-destructive tests are widely used to predict the strength of existing structures. The purpose of the present study is to propose the prediction equations for strength evaluation of concrete structures. The present study focuses on the rebound method and ultrasonic pulse velocity method for quartzite aggregate concrete. The major test variables include the water-cement ratio and curing methods. The water-cement ratio are 0.4, 0.5, 0.6, 0.7, respectively and the curing method covers ail-dry condition and standard curing condition. The prediction equations for strength of concrete are proposed from the present test data.