摘要
There are many difficulties in concrete endurance prediction, especially in accurate predicting service life of concrete engineering. It is determined by the concentration of S042-/ Mg2+ / Cl- /Ca2+ , reactionareas , the cycles of freezing and dissolving, alternatives of dry and wet state, the kind of cement, etc. . In general , because of complexity itself and cognitive limitation, endurance prediction under sulphate erosion is still illegible and uncertain, so this paper adopts neural network technology to research this problem. Through analyzing , the paper sets up a 3 - levels neural network and a 4 - levels neural network to predict the endurance undersulphate erosion. The 3 - levels neural network includes 13 inputting nodes, 7 outputting nodes and 34 hidden nodes. The 4 - levels neural network also has 13 inputting nodes and 7 outputting nodes with two hidden levels which has 1 nodes and 8 nodes separately. In the end the paper give a example with laboratorial data and discussion the result and deviation. The paper shows that deviation results from some faults of training specimens; such as few training specimens and few distinctions among training specimens. So the more specimens should be collected to reduce data redundancy and improve the reliability of network analysis conclusion.
There are many difficulties in concrete endurance prediction, especially in accurate predicting service life of concrete engineering. It is determined by the concentration of S042-/ Mg2+ / Cl- /Ca2+ , reactionareas , the cycles of freezing and dissolving, alternatives of dry and wet state, the kind of cement, etc. . In general , because of complexity itself and cognitive limitation, endurance prediction under sulphate erosion is still illegible and uncertain, so this paper adopts neural network technology to research this problem. Through analyzing , the paper sets up a 3 - levels neural network and a 4 - levels neural network to predict the endurance undersulphate erosion. The 3 - levels neural network includes 13 inputting nodes, 7 outputting nodes and 34 hidden nodes. The 4 - levels neural network also has 13 inputting nodes and 7 outputting nodes with two hidden levels which has 1 nodes and 8 nodes separately. In the end the paper give a example with laboratorial data and discussion the result and deviation. The paper shows that deviation results from some faults of training specimens; such as few training specimens and few distinctions among training specimens. So the more specimens should be collected to reduce data redundancy and improve the reliability of network analysis conclusion.
基金
Funded by the Nith-five Plan Key Project in Scientific and Technological Research (9653533)
