• Computers and Concrete
• /
• v.20 no.1
• /
• pp.31-38
• /
• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

• Advances in Computational Design
• /
• v.2 no.3
• /
• pp.225-240
• /
• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Korean Journal of Construction Engineering and Management
• /
• v.19 no.4
• /
• pp.43-51
• /
• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Journal of the Korea Concrete Institute
• /
• v.13 no.3
• /
• pp.237-243
• /
• 2001

The prediction model is proposed to estimate the variation of compressive strength of fly ash concrete with aging. After analyzing the experimental result with the model, the regression results are presented according to fly ash replacement content and water-cement ratio. Based on the regression results, the influence of fly ash replacement content and water-cement ratio on apparent activation energy was investigated. According to the analysis, the model provides a good estimate of compressive strength development of fly ash concrete with aging. As the fly ash replacement content increases, the limiting relative compressive strength and initial apparent activation energy become greater. The concrete with water-cement ratio smaller than 0.40 shows that the limiting relative compressive strength and apparent activation energy are nearly constant according to water-cement ratio. But, the concrete with water-cement ratio greater than 0.40 has the increasing limiting relative compressive strength and apparent activation energy with increasing water-cement ratio.

• Structural Engineering and Mechanics
• /
• v.58 no.6
• /
• pp.989-999
• /
• 2016

Moist curing of concrete is a time consuming procedure. It takes minimum 28 days of curing to obtain the characteristic strength of concrete. However, under certain situations such as shortage of time, weather conditions, on the spot changes in project and speedy construction, waiting for entire curing period becomes unaffordable. This situation demands early strength of concrete which can be met using accelerated curing methods. It becomes necessary to obtain early strength of concrete rather than waiting for entire period of curing which proves to be uneconomical. In India, accelerated curing methods are used to arrive upon the actual strength by resorting to the equations suggested by Bureau of Indian Standards' (BIS). However, it has been observed that the results obtained using above equations are exaggerated. In the present experimental investigations, the results of the accelerated compressive strength of the concrete are used to develop the regression models for predicting the short term and long term compressive strength of concrete. The proposed regression models show better agreement with the actual compressive strength than the existing model suggested by BIS specification.

• Computers and Concrete
• /
• v.14 no.5
• /
• pp.577-597
• /
• 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.

• Journal of the Korea Concrete Institute
• /
• v.12 no.2
• /
• pp.31-42
• /
• 2000

This paper discusses the validity of models predicting the compressive strength of concrete subjected to various temperature histories and the shortcomings of existing rate constant model and apparent activation energy concept. Based on the discussion, a modified rate constant model is proposed. The modified rate constant model, in which apparent activation energy is a nonlinear function of curing temperature and age, accurately estimates the development of the experimental compressive strengths by a few researchers. Also, the apparent activation energy of concrete cured with high temperature decreases rapidly with age, but that of concrete cured with low temperature decreases gradually with age. Finally generalized models to predict apparent activation energy and compressive strength are proposed, which are based on the regression results.

• Computers and Concrete
• /
• v.19 no.2
• /
• pp.217-226
• /
• 2017

This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in $20{\pm}2^{\circ}C$ waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% $MgSO_4^{-2}$, 2% $MgSO_4^{-2}$, and 4% $MgSO_4^{-2}$ for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm.

• Geomechanics and Engineering
• /
• v.36 no.4
• /
• pp.381-390
• /
• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.11a
• /
• pp.149-150
• /
• 2023

This study proposes the most suitable strength prediction model equation for UHPC by calculating the apparent activation energy of UHPC according to the curing temperature and deriving the integrated temperature and compressive strength prediction equation. The results are summarized as follows. The apparent activation energy was calculated using the Arrhenius function, which was calculated as 21.09 KJ/mol. A model equation suitable for UHPC was calculated, and when the Flowman model equation was used, it was confirmed that it was suitable for the properties of UHPC using a condensation promoting super plasticizing agent.