• Earthquakes and Structures
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• v.8 no.5
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• Geomechanics and Engineering
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• v.1 no.3
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• pp.259-262
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• 2009

This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.

• Computers and Concrete
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• v.30 no.3
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• pp.225-236
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• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• Applied Biological Chemistry
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• v.38 no.5
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• pp.440-446
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• 1995

In order to select the proper formulation of newly developed herbicide Cyhalofop-butyl{n-butyl-(R)-2-[4-(2-fluro-4-cyanophenoxy)phenoxy]propionte} to Echino-chloacrus-galli(L)P. Beaw. several formulations were made and tested by biological assay. Weed control of wettable powder formulated with two adjuvants on E. crus-galli showed higher effect as compared with the formulation made without adjuvants. Higher concentration of adjuvants resulted in higher absorption and higher weed control on E. crus-galli. However, adhesional force of wettable powder applied to leaf surface was not positively correlated to the amount of herbicide absorption. The weeding effect and amount of herbicide absorbed on E. crus-galli were higher by emulsifiable concentrateformulations with different HLB and non ionic surfactants as compared with wettable powder formulations. The higher adhesional force and higher absorption of herbicide on E. crus-galli were obtained from microemulsion than the others. Granulization of the herbicide with appropriate adjuvants in a form of resurfacing on the submerged water gave rise to a good weeding effect and believed to be a possible promising formulation.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.304-309
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• 1997

A method to mitigate the thermal stratification flow of a horizontal pipe line is proposed by heating external bottom of the pipe with electrical heat tracing. Unsteady two dimensional model has been used to numerically investigate an effect of the external Denting to the thermally stratified flow. The dimensionless governing equations are solved by using the control volume formulation and SIMPLE algorithm. Temperature distribution, streamline profile and Nusselt numbers of fluids and pipe walls with time are analyzed in case of externally heating condition. no numerical result of this study shows that the maximum dimensionless temperature difference between the hot and the cold sections of pipe inner wall is 0.424 at dimensionless time 1,500 ann the thermal stratification phenomena is disappeared at about dimensionless time 9,000. This result means that external heat tracing can mitigate the thermal stratification phenomena by lessening $\Delta$ $T_{ma}$ about 0.1 and shortening the dimensionless time about 132 in comparison with no external heat tracing.rnal heat tracing.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.121-122
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• 2006

Paralleled with the development of new materials we need to develop methods and techniques to reveal the environmental interaction and impact of the new materials. Small changes in the chemical structure or product formulation may render the product less environmentally adaptable. Degradation products formed from PLLA were identified after aging in different environments and their assimilation in the biotic environment was shown. Green and degradable hydrogels could be designed from renewable hemicelluloses and lactic acid. Hemicelluloses are a renewable and highly interesting raw material source for new green polymers.

• Geomechanics and Engineering
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• v.20 no.5
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• pp.385-397
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• 2020

This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio D_f/B, foundation net applied pressure q_net, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.64-70
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. But those models suffer from poor discrimination between acoustically similar words. In this paper we propose an discriminative training algorithm for predictive neural network models. This algorithm is derived from GPD (Generalized Probabilistic Descent) algorithm coupled with MCEF(Minimum Classification Error Formulation). It allows direct minimization of a recognition error rate. Evaluation of our training algoritym on ten Korean digits shows its effectiveness by 30% reduction of recognition error.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.997-1002
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• 1994

Predictive neural network models are powerful speech recognition models based on a nonlinear pattern prediction. Those models can effectively normalize the temporal and spatial variability of speech signals. But those models suffer from poor discrimination between acoustically similar words. In this paper, we propose a discriminative training algorithm for predictive neural network models based on a generalized probabilistic descent (GPD) algorithm and minimum classification error formulation (MCEF). The Evaluation of our training algorithm on ten Korean digits shows its effectiveness by 40% reduction of recognition error.