• Structural Engineering and Mechanics
• /
• v.74 no.4
• /
• pp.503-514
• /
• 2020

This study proposes prediction models for the shear strength of steel fiber reinforced concrete (SFRC) and ultra-high-performance fiber reinforced concrete (UHPC) beams using a Bayesian parameter estimation approach and a collected experimental database. Previous researchers had already proposed shear strength prediction models for SFRC and UHPC beams, but their performances were limited in terms of their prediction accuracies and the applicability to UHPC beams. Therefore, this study adopted a statistical approach based on a collected database to develop prediction models. In the database, 89 and 37 experimental data for SFRC and UHPC beams without stirrups were collected, respectively, and the proposed equations were developed using the Bayesian parameter estimation approach. The proposed models have a simplified form with important parameters, and in comparison to the existing prediction models, provide unbiased high prediction accuracy.

• Asian-Australasian Journal of Animal Sciences
• /
• v.28 no.7
• /
• pp.993-998
• /
• 2015

Possibilities of predicting eggshell ultrastructure from direct non-destructive and destructive measurements were examined using 120 Fayoumi eggs collected from the flock at 45 weeks of age. The non-destructive measurements included weight, length and width of the egg. The destructive measurements were breaking strength and shell thickness. The eggshell ultrastructure traits involved the total thickness of eggshell layer, thickness of palisade layer, cone layer and total score. Prediction of total thickness of eggshell layer based on non-destructive measurements individually or simultaneously was not possible ($R^2=0.01$ to 0.16). The destructive measurements were far more accurate than the non-destructive in predicting total thickness of eggshell layer. Prediction based on breaking strength alone was more accurate ($R^2=0.85$) than that based on shell thickness alone ($R^2=0.72$). Adding shell thickness to breaking strength (the best predictor) increased the accuracy of prediction by 5%. The results obtained indicated that both non-destructive and destructive measurements were not useful in predicting the cone layer ($R^2$ not exceeded 18%). The maximum accuracy of prediction of total score ($R^2=0.48$) was obtained from prediction based on breaking strength alone. Combining shell thicknesses and breaking strength into one equation was no help in improving the accuracy of prediction.

• Computers and Concrete
• /
• v.32 no.3
• /
• pp.233-246
• /
• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• Smart Structures and Systems
• /
• v.14 no.5
• /
• pp.785-809
• /
• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.11a
• /
• pp.93-94
• /
• 2012

In this study, prediction of later-age compressive strength of ultra-high strength concrete, based on the accelerated strength of concrete cured in 80℃ warm water was investigated. As a result, the nature of ultra-high strength concrete showed a rapid early strength enhancement, compressive strength using warm water method of 80℃ at 2days is same compressive at 28days using standard curing.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2006.05b
• /
• pp.145-148
• /
• 2006

Schmidt hammer and ultra-sonic method are commonly used for crushed sand concrete compressive strength test in a construction field. At present, various of equations for prediction of strength are present, which have been used in a construction field. The purpose of this study is to evaluate the correlation between prediction strength by presentation equations and destructive strength to test specimen, and find out which is a suitable equation for the construction site, In this study, a strength test was carried out destructive test by means of core sampling and traditional test. Non-destructive test was conducted Schmidt hammer and ultra-sonic method, the experimental parameter were concrete age, curing condition, test method and strength level. It is demonstrated that the correlation behavior of crushed sand concrete strength in this study good due to the perform analysis of correlation between core, destructive strength and non-destructive strength.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.8
• /
• pp.1503-1512
• /
• 1992

The strength evaluation and life prediction on the corrosion part of structure is one of the most important subjects, as a viewpoint of reducing economic loss by regular inspection, maintenance, repair and replace. For this purpose, it has been difficult to obtain the available data on growth of pit depth or growth rate of each pit which depends on time. In this paper, the life prediction and strength evaluation method was suggested for the structure with irregular stress concentration part by surface corrosion. The statistical distribution pattern of corrosion depth and the degree of fatigue strength decline were confirmed according to corrosion period by artificial corrosion of SS41 steel. The life prediction and the fatigue strength evaluation of materials with consideration of the corrosion period on the extreme value statistic analysis by the data of maximum depth of corrosion and on random variable was studied.

• KEPCO Journal on Electric Power and Energy
• /
• v.5 no.3
• /
• pp.201-207
• /
• 2019

An anchorage system is essential for most reinforced concrete structures to connect building components. Therefore, the prediction of strength of the anchor is very important issue for safety of the structures themselves as well as structural components. The prediction models in existing design codes are, however, not applicable for large anchors because they are based on the small size anchors with diameters under 50 mm. In this paper, new prediction models for strength of a single anchor, especially the tensile strength of a single anchor, is developed from the experimental results with consideration of size effect. Size effect in the existing models such as ACI or CCD method is based on the linear fracture mechanics which is very conservative way to consider the size effect. Therefore, new models are developed based on the nonlinear fracture mechanics rather than the linear fracture mechanics for more reasonable prediction. New models are proposed by the regression analysis of the experimental results and it can predict the tensile strength of both small and large anchors.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.11a
• /
• pp.107-108
• /
• 2012

The study on the strength prediction using Maturity is mainly focused on, but the study on the concrete mixing blast furnace slag powder is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by Maturity function and is to compare and examine the strength prediction of concrete mixing Blast Furnace Slag Power using ACI and Logistic Curve prediction equation. So it is intended that fundamental data are presented for quality management and process management of concrete mixing Blast Furnace Slag Power in the construction field.

• Computers and Concrete
• /
• v.6 no.2
• /
• pp.155-165
• /
• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.