• Computers and Concrete
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• 제24권2호
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• Journal of Electrochemical Science and Technology
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• 제15권2호
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• pp.242-252
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• 2024

To date, methods used to assess the interfacial transition zone (ITZ), which represents the boundary between the aggregate and paste inside concretes, have primarily relied on destructive tests, and non-destructive tests has received little attention until recently. This study assessed the interfaces of concretes with lightweight aggregates based on electrochemical impedance spectroscopy (EIS) for high-strength concretes and examined the possibility of estimating the compressive strength of concretes through non-destructive testing using EIS. The experimental results revealed that the impedance of the hardened cement increased with increasing compressive strength and aggregate density. In particular, when the results of impedance measurement were displayed as a Nyquist plot, the intercept of the x-axis depicting the effective conductivity was proportional to the compressive strength. Furthermore, an equivalent circuit was selected to interpret the correlation between cement aggregates and impedance. Consequently, the compressive strength was found to increase with the value of the resistances of the electrolyte filled in continuous pores in the cement aggregate. And, the pores formed in the ITZ affect this value. The resistance at the ITZ for different aggregates was also obtained, and it was found that the resistance was consistent with the results predicted by SEM images of the ITZ and correlated with the strength of the concretes. The proposed method can be used as a way to easily determine the strength of cement according to differences in aggregate.

• 한국도로학회논문집
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• 제20권4호
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• pp.21-26
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• 2018

PURPOSES : The objective of this study is to evaluate the application of soil stabilization method for soft shoulder construction in the iRoad Project of Sri Lanka. METHODS : Firstly, the quantitative analysis of soil strength improvement due to soil stabilization was done for soil samples collected from iRoad construction sites. Two types of soils were selected from iRoad Project sites and prepared for soil stabilization testing by the Road Development Authority. Secondly, the appropriate stabilizer was selected at given soil type based on test results. Two different stabilizers, ST-1 and ST-2, produced in Korea were used for estimating soil strength improvements. Finally, the optimum stabilizer content was determined for improving shoulder performance. The uniaxial compressive strength (UCS) test was conducted to evaluate the strength of stabilized soil samples in accordance with ASTM D 1633. The use of bottom ash as a stabilizer produced from power plant in Sri Lanka was also reviewed in this task. RESULTS : It is found from the UCS testing that a 3% use of soil stabilizer can improve the strength up to 2~5 times in stabilized soft shoulder soils with respect to unstabilized soils. It is also observed from UCS testing that the ST-1 shows high strength improvement in 3% of stabilizer content but the strength improvement rate with increase of stabilizer content is relatively low compared with ST-2. The ST-2 shows a low UCS value at 3% of content but the UCS values increase significantly with increase of stabilizer content. When using the ST-2 as stabilizing agent, the 5% is recommended as minimum content based on UCS testing results. Based on the testing results for bottom ash replacement, the stabilized sample with bottom ash shows the low strength value. CONCLUSIONS : This paper is intended to check the feasibility for use the soil stabilization technique for shoulder construction in Sri Lanka. The use of soil stabilizer enables to improve the durability and strength in soft shoulder materials. When applying the bottom ash as a soil stabilizer, various testings should be conducted to satisfy the specification criteria.

• 한국항공우주학회지
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• 제50권10호
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• pp.701-708
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• 2022

압축력을 받는 발사체의 추진제 탱크 구조는 좌굴에 의한 파손이 발생할 위험이 크다. 탱크 구조와 같이 두께가 얇고 반지름이 큰 대형 경량 구조물은 제작 과정이 어렵고 복잡하므로 시험 후 사용을 위해 비파괴적 시험법을 이용한 좌굴 하중 예측이 요구된다. 압축 하중-고유 진동수와의 관계를 이용하여 좌굴 하중을 예측하는 Vibration Correlation Technique(VCT)에 관한 많은 연구가 수행되었으나 좌굴 하중을 정확히 예측하기 위하여 큰 압축 하중을 필요로 하는 시험이 요구되었고 구조물의 내부 압력이 증가됨에 따라 예측 정확도가 현저히 떨어지는 경향을 보였다. 본 논문에서는 내압 증가에 따라 예측 정확도가 저하되는 경향과 원인을 분석하고 유한요소해석 결과와 압축 시험 결과를 혼합한 VCT를 제안하여 시험 후 추진제 탱크의 사용이 가능할 정도의 낮은 압축 하중 시험 값에서도 좌굴 하중 예측 정확도를 증대시킬 수 있는 방법을 제안하였다. 제안된 방법에 의한 좌굴 예측값은 실제 좌굴 시험 값과 매우 잘 일치하였다.

• Computers and Concrete
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• 제14권5호
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• pp.577-597
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• 2014

This paper aims to develop a prediction model for the hardened properties of waste LCD glass that is used in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. We also summarized the testing results of the hardened properties of a variety of waste LCD glass concretes and discussed the effect of factors such as the water-binder ratio (w/b), waste glass content (G) and age (t) on the concrete compressive strength, flexural strength and ultrasonic pulse velocity. This study also applied a hyperbolic function, an exponential function and a power function in a non-linear regression analysis of multiple variables and established the prediction model that could consider the effect of the water-binder ratio (w/b), waste glass content (G) and age (t) on the concrete compressive strength, flexural strength and ultrasonic pulse velocity. Compared with the testing results, the statistical analysis shows that the coefficient of determination $R^2$ and the mean absolute percentage error (MAPE) were 0.93-0.96 and 5.4-8.4% for the compressive strength, 0.83-0.89 and 8.9-12.2% for the flexural strength and 0.87-0.89 and 1.8-2.2% for the ultrasonic pulse velocity, respectively. The proposed models are highly accurate in predicting the compressive strength, flexural strength and ultrasonic pulse velocity of waste LCD glass concrete. However, with other ranges of mixture parameters, the predicted models must be further studied.

• Computers and Concrete
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• 제21권1호
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Geomechanics and Engineering
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• 제25권4호
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• pp.317-330
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• 2021

This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15 MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models.

• Advances in materials Research
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• 제11권1호
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• pp.75-90
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• 2022

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

• Computers and Concrete
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• 제32권3호
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• pp.233-246
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• 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.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 가을 학술발표회 논문집
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• pp.3-9
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• 2002

Paper sludge ash was assured as material of a sort of pozzolan. For the reason of fluidity decrease caused by the strong absorption of paper stooge ash and the decrease of compressive strength in case of using over30% replacement by the weight of cement, paper sludge ash is not suitable for blending material. Therefore, it is necessary to find proper replacement ratios between paper sludge and blast furnace slag to insure compressive compensation and appropriate slump. Accordingly, as varied the blending ratios of paper sludge and blast furnace slag, testing mortar was made. This study was aimed to investigate the possibility of using blending replacement of paper sludge ash and blast furnace slag throughout compressive test, flow test, SEM(Scanning Eletron Microscope), MIP(Mercury Intrusion Porosity test), and TG-DTA(Thermal analysis).