Anticipation of the compressive strength of steel fiber‐reinforced concrete by different types of artificial intelligence methods

Abstract

Although concrete is one of the most prevalent materials in the world, it has a main flaw that is fragility, and this flaw stands out especially in strain. One way for improving the fragility property of the concrete is to add the amount of steel fiber to the concrete mixture. Adding steel fiber to the concrete mixture will not only improve the brittleness of concrete but also enhance other mechanical properties including compressive strength (which is one of the most important properties of the concrete). Determining the compressive strength of steel fiber-reinforced concrete (SFRC) is a costly and timeconsuming procedure and can have an impact on the environment. So, reliable alternative method(s) for determining the value of this property is needed. However, the development of a technique for predicting the compressive strength of SFRC is at an initial level in comparison with the normal concrete because of its complexity and limited available data. In this study, three artificial intelligence methods such as radial basis function neural network (RBFNN), artificial neural network, and adaptive-neuro fuzzy inference system have been proposed for predicting the compressive strength of SFRC. In this regard, 230 data have been collected from previous studies for introduction to the models. In addition, the performance of the models was investigated by comparing the model's results with experimental data. Moreover, the statistical parameters have been used for assessing the performances of the models and comparing the ability and accuracy of the models with each other. In this study, the values of statistical parameters show that all three models have a good ability for predicting the compressive strength of SFRC, and the accuracies of the models are acceptable. Overall, based on the values of statistical parameters like the Pearson correlation coefficient (which is 0.97 for all data of the RBFNN model), and the amount of time required by the model to get the results, the RBFNN model has been considered as the best model for predicting the compressive strength of SFRC.Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors' closure, if any, approximately nine months after the print publication.

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

“…From formula (8), it can be seen that the scheme is K(Δd ik , Δq ik , Δb ik ) relatively small and large in preliminary analysis, which can expand the judgment range of the leading scheme, avoid selecting the local "subjective scheme," and keep the diversity of selecting the best scheme K(Δd ik , Δq ik , Δb ik ) as shown by Flament [18]. In the production stage of judgment, the scheme is relatively large and relatively small ], which narrows the judgment range, improves the neighborhood judgment ability of the leading scheme, and enhances the dynamic judgment performance of the arti cial intelligence algorithm as shown in Figure 1.…”

“…Some scholars have put forward the arti cial intelligence algorithm. Although the arti cial intelligence algorithm can judge the choice of innovation strategy of lm and television works, there are relatively few postproduction parameters, and the overall production ability is greatly reduced under changeable and complex conditions as stated by Pazouki and Pourghorban [8]. erefore, some scholars have put forward a variety of innovative production strategies and improved the artificial intelligence algorithm, as shown in Table 1.…”