contributor author | Doo Kie Kim | |
contributor author | Jong Jae Lee | |
contributor author | Jong Han Lee | |
contributor author | Seong Kyu Chang | |
date accessioned | 2017-05-08T21:17:50Z | |
date available | 2017-05-08T21:17:50Z | |
date copyright | June 2005 | |
date issued | 2005 | |
identifier other | %28asce%290899-1561%282005%2917%3A3%28353%29.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/46038 | |
description abstract | The compressive strength of concrete is a commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time consuming. More importantly, it is too late to make improvements even if the test result does not satisfy the required strength, since the test is usually performed on the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is very important. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is performed using the probabilistic neural network which is an effective tool for the pattern classification problem and provides a probabilistic viewpoint as well as a deterministic classification result. Application of probabilistic neural networks in the compressive strength estimation of concrete is performed using the mix proportion data and test results of two concrete companies. It has been found that the present methods are very efficient and reasonable in predicting the compressive strength of concrete probabilistically. | |
publisher | American Society of Civil Engineers | |
title | Application of Probabilistic Neural Networks for Prediction of Concrete Strength | |
type | Journal Paper | |
journal volume | 17 | |
journal issue | 3 | |
journal title | Journal of Materials in Civil Engineering | |
identifier doi | 10.1061/(ASCE)0899-1561(2005)17:3(353) | |
tree | Journal of Materials in Civil Engineering:;2005:;Volume ( 017 ):;issue: 003 | |
contenttype | Fulltext | |