• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.27-38
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• 2003

The characteristics and prediction model of the shear strength of unsaturated residual granite soils were studied in this paper. In order to investigate the influence of the net normal stress on the shear strength, unsaturated triaxial tests and SWCC tests were carried out by varying the net normal stress. Experimental data for unsaturated shear strength tests were compared with predicted shear strength envelopes obtained from existing prediction models. It was shown that the soil-water characteristic curve and the shear strength of the unsaturated soil varied with the change of the net normal stress. Therefore, to achieve a truly descriptive shear strength envelope for unsaturated soils, the effect of the normal stress on the contribution of matric suction to the shear strength has to be taken into consideration. In this paper, a modified prediction model f3r the unsaturated shear strength was proposed.

• Computers and Concrete
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• v.22 no.2
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• pp.197-207
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• 2018

In the present study, group method of data handling networks (GMDH) are adopted and evaluated for shear strength prediction of both FRP-reinforced concrete members with and without stirrups. Input parameters considered for the GMDH are altogether 12 influential geometrical and mechanical parameters. Two available and very recently collected comprehensive datasets containing 112 and 175 data samples are used to develop new models for two cases with and without shear reinforcement, respectively. The proposed GMDH models are compared with several codes of practice. An artificial neural network (ANN) model and an ANFIS based model are also developed using the same databases to further assessment of GMDH. The accuracy of the developed models is evaluated by statistical error parameters. The results show that the GMDH outperforms other models and successfully can be used as a practical and effective tool for shear strength prediction of members without stirrups ($R^2=0.94$) and with stirrups ($R^2=0.95$). Furthermore, the relative importance and influence of input parameters in the prediction of shear capacity of reinforced concrete members are evaluated through parametric and sensitivity analyses.

• Structural Engineering and Mechanics
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• v.56 no.2
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• pp.201-221
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• 2015

The fatigue damage problems are frequently encountered in the design of civil engineering structures. A realistic and accurate fatigue life prediction is quite essential to ensure the safety of engineering design. However, constructing a reliable fatigue life prediction model can be quite challenging. The use of traditional deterministic approach in predicting the fatigue life is sometimes too dangerous in the real practical designs as the method itself contains a wide range of uncertain factors. In this paper, a new fatigue life prediction method is going to be proposed where the residual strength is been utilized. Several cumulative damage models, capable of predicting the fatigue life of a structural element, are considered. Based on Miner's rule, a randomized approach is developed from a deterministic equation. The residual strength is used in a one to one transformation methodology which is used for the derivation of the fatigue life. To arrive at more robust results, fuzzy sets are introduced to model the parameter uncertainties. This leads to a convoluted fuzzy based fatigue life prediction model. The developed model is illustrated in an example analysis. The calculated results are compared with real experimental data. The applicability of this approach for a required reliability level is also discussed.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Journal of the Korean GEO-environmental Society
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• v.9 no.3
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• pp.17-28
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• 2008

In this study, the residual strength prediction models were proposed by measuring various residual strength according to pit characteristics for metallic water pipes such as cast iron pipe (CIP), ductile iron pipe (DIP), and steel pipe (SP). The exponential prediction model was better fitted to measured residual tensile strength for CIP. In case of DIP and SP, the prediction model using loss of strength was more exactly predicted compared with other model types. The fracture toughness were averagely $40.46kgf/mm^2{\sqrt{mm}}$ for CIP, $85.27kgf/mm^2{\sqrt{mm}}$ for DIP, and $92.27kgf/mm^2{\sqrt{mm}}$ for SP, the determination coefficient ($R^2$) of between measured residual tensile strength and predicted values for residual strength prediction models using fracture toughness was estimated from 0.44 to 0.86. Especially, the proposed residual tensile strength prediction models were applied for the verification and reliability to CIPs and DIPs at 14 sites. The determination coefficient ($R^2$) between measured residual tensile strength and predicted values was estimated from 0.76 to 0.78. Therefore it was thought that the proposed residual tensile strength models could help to support resonable and economical decision of rehabilitation/replacement.

• Journal of Industrial Technology
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• v.41 no.1
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• pp.1-6
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• 2021

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.647-652
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• 2002

In the previous study, authors presented the I-ProConS (Intelligent PREdiction system of CONcrete Strength) using artificial neural networks (ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction. The serious problem of the system has occured, which it cannot appropriately predict the concrete strength when the curing temperature of a curing day is changed. This is because it uses the single neural networks, which all nodes are fully connected, and thus it cannot smoothly respond for external impact. However this paper presents that the problem can be solved by multiple neural networks, which is composed of five ANNs.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.591-598
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• 2000

In the present study, the variation of settlement, pore water pressure and undrained shear strength through model tests were measured. Also, the variation of water content, unit weight and shear strength by the vane shear tests were observed. In this study, appropriate deposit time of construction equipments used in treatment of hydraulic fills is determined from the prediction curve of increased shear strength in dredged fills.

• Computers and Concrete
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• v.19 no.6
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• pp.651-658
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• 2017

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.