• 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.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.42 no.2
• /
• pp.99-107
• /
• 2000

The objective of this study are to compare the development of compressive strength of epoxy mortars used as repairing materials with respect to maturity , and to propose a model predicting strength development of epoxy mortars. A series of tests are carried out for the hardener contents of 30, 40 and 50 percentage of epoxy resin and compressive strengths are measured at the age of 6, 12, 24, 72, 120 and 168 hours respectively under the cure temperature of 0, 10, 20 and 3$0^{\circ}C$. The datum temperature is estimated by measured strengths, and the maturity is calculated with the estimated datum temperature. The compressive strength of epoxy mortars can be predicted by regression analysis from the maturity-compressive strength relationship.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.3 no.2
• /
• pp.177-183
• /
• 1999

The major object of this study is to investigate experimentally the experimental equation by the non-destructive testing methods of ultrasonic pulse velocity, rebound number, combined method of ultrasonic pulse velocity and rebound number, maturity which are applicable to the evaluation of compressive strength of concrete at early ages. Also test result of mix are statistically analyzed to infer the correlation coefficient between the maturity and the compressive strength of concrete. The results show good application of Logistic curve for estimating strength development under various curing temperature. The relation between ultrasonic pulse velocity, rebound number, combined method of ultrasonic pulse velocity and rebound number and compressive strength of concrete have low correlation coefficient, but maturity method show good correlation coefficient.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2005.05a
• /
• pp.119-124
• /
• 2005

It is necessary for prediction of recycled aggregate concrete(RAC) strength at the early stage that facilitate concrete form removal and scheduling for construction. However, to predict RAC strength is difficult because of being influenced by complicated many factors. Therefore, this research suggest optimized estimation method that can reflect many factors. One way is Case-Based Reasoning(CBR) that solved new problems by adapting solutions to similar problems solved in the past, which are solved in the case library. Other way is Artificial Neural Networks(ANN) that solved new problems by training using a set of data, which is representative of problem domain. This study is to propose comparing accuracy of the estimating the compressive strength of recycled aggregate concrete using Case-Based Reasoning(CBR) and Artificial Neural Networks(ANN).

• 한국기계연구소 소보
• /
• s.15
• /
• pp.75-87
• /
• 1985

A review was performed on fatigue life prediction and strength evaluation of shot peened parts. Fatigue strength of machine parts can be improved by shot peening due to compressive residual stresses on such parts. Compressive residual stress cannot be uniquely define by peening intensity. Several measuring methods of residual stress and the principle of hole drilling method are presented. Exploratory measurement of residual stress was performed on the shot peened SM35C plate with the hole drilling method. Fatigue life and failure location of shot peened parts under bending load can be predicted by a damage parameter which is incorporated with material properties, residual stress, and applied stress conditions. Some method are presented to predict the fatigue strength of shot peened parts at any given life. Shot peening gives its full benefit to the notched machine parts of high strength steels.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.43 no.4
• /
• pp.97-105
• /
• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• Computers and Concrete
• /
• v.20 no.6
• /
• pp.627-634
• /
• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.

• Smart Structures and Systems
• /
• v.26 no.6
• /
• pp.753-763
• /
• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Computers and Concrete
• /
• v.24 no.2
• /
• pp.137-150
• /
• 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.

• International Journal of Concrete Structures and Materials
• /
• v.19 no.1E
• /
• pp.11-16
• /
• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.