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Analysis of Compressive Strength Characteristics of Mineral Admixture in Concrete Containing Various Gelled Materials using Artificial Neural Networks
| Content Provider | Scilit |
|---|---|
| Author | Shyamala, G. Gobinath, R. Sarla, Pushpalatha Shewale, Manisha |
| Copyright Year | 2020 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering The growing population of the world demands the massive concrete production, which resonances the environmental impact by consuming a large number of natural resources. To reduce the let-downs occurring in the concrete, estimation of Strength of concrete is needed. The ratio and combination of mineral admixtures will find out the strength parameters of concrete such as tensile strength and compressive strength of the concrete revealed the Bayesian Regularized and Levenberg-Marquardt approach in Artificial Neural Networks. The comparison of these two ANN models for estimating the compressive and tensile strength of massive concrete is performed to show a novel approach. The Levenberg-Marquardt algorithm furnished the more accurate results among the two algorithms also the estimated values are very nearer to the predicted data. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/981/3/032093/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/981/3/032093 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 3 |
| Volume Number | 981 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2020-12-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Characterization and Testing of Materials Artificial Neural Networks Compressive Strength |
| Content Type | Text |
| Resource Type | Article |
