concrete slump

The expansion of the planet's population and the need for the construction industry has caused a shortage of primary materials. On the other hand, the process of making and using these materials causes harmful damage to the environment and causes the pollution of natural resources and the release of carbon dioxide (CO2) in nature. In addition to this, plastic polymers used in various industries are one of the most consumed items that cause environmental pollution and the waste recycling process is costly and time-consuming. One of the most widely used polymers is polyvinyl chloride (PVC), which is used in the industry of making wires, electric cables, and all kinds of mobile phone chargers as a cover for their conductors.Limited studies have been done in the field of reusing this polymer as a member of the concrete structure as fibres and improving its mechanical properties in the past. This study deals with a comprehensive review of these studies in the field of using these types of fibres that have been used as reinforcing and reinforcing properties of concrete and focuses on a detailed investigation of the effect of different percentages and various dimensions of these types of fibres on the mechanical characteristics of concrete.

Concrete performance is of very high importance in civil engineering projects. One of the most common ways to measure the performance of concrete, is the slump test. To save time, money and materials, it is better to use intelligent methods in predicting the slump. Therefore, in this study a method based on soft computing is used, so without the need to perform arduous physical experiments, one can obtain an estimate of the slump.In this study, an adaptive neuro-fuzzy model which has the benefits of both neural network and fuzzy inference system, is used to predict the concrete slump. In order to train the algorithm for future use, comprehensive experimental data is essential .So by collecting data related to 44 concrete slump experimental tests, variables such as water-cement ratio, sand, gravel, silica fume and super plasticizer which are the principal components of concrete, are considered as input variables and the amount of slump is considered as the output variable in the proposed model.In order to evaluate the performance of the proposed model and accuracy of the results, the results of the adaptive neuro-fuzzy model is compared to that of artificial neural network model, which is obtained in a parallel research done by author, by statistical parameters such as correlation coefficient and root mean square error. By averaging the results of ten different classifications of experimental input data, the correlation coefficient is approximately equal between adaptive neuro-fuzzy and neural network slump. While the root mean square error obtained by using adaptive neuro-fuzzy model is 0/4477 which is less than 0/6964 by neural network model. The difference in the output error of the two models are due to different learning algorithms used in two models and unknown number of hidden layers and neurons in the desirable artificial neural network model.

Workability is the important property of a fresh concrete. There are many methods to measure the workability of concrete. One of the most common and frequently used method is the slump test. In order to achieve the concrete with a desired workability, different concrete mixtures must be made and the slump test be done. To save time, money and material, the prediction methods are used to predict the slump on the basis of the results obtain from a certain number of concrete mixtures. In this study, Multivariable Linear Regression (MLR) method and the Artificial Neural Network (ANN) model, as one of soft computation algorithms, are utilized to evaluate slump prediction and the results in terms of applicability, accuracy and efficiency are compared. The proposed ANN model is the multilayer perceptron with back-propagation learning algorithm. The results showed that the predicted values of both methods are desirable and acceptable. The correlation coefficient, mean square error and the mean absolute error in the ANN model are respectively 0/9853, 0/485 and 0/547. These values in the multivariable linear regression method are respectively 0/8717, 1/7731 and 0/9136. In the multivariable linear regression method, a linear relationship between independent and dependent variables is determined. But, in the most cases and reality, this relationship is not extremely linear, so the predicted values in this method may have big errors and can be neglected. The ANN model, predicts the output by learning the true relationship between inputs and output parameters.