Prediction of Atmospheric Corrosion of Ancient Door Knockers via Neural Networks

被引：2

作者：

Houshmandynia, Shahrzad ^{[1
]}

Raked, Roya ^{[2
]}

Golbabaei, Fardad ^{[3
]}

机构：

[1] Islamic Azad Univ, Arak Branch, Dept MBA Mkt, POB 1997683953, Arak, Iran

[2] Dept Masters Handicrafts Art & Architecture Ardak, Yazd, Iran

[3] AREEO, Res Inst Forests & Rangelands, Dept Wood & Paper Sci, Tehran, Iran

来源：

CHEMICAL METHODOLOGIES | 2018年 / 2卷 / 04期

关键词：

Anticipation; Neural network; Atmospheric corrosion; Bronze corrosion;

D O I：

10.22034/CHEMM.2018.65388

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The importance of door knockers persuades us to anticipate the atmospheric corrosion through Neural Network (NN) which is validated by data originated from literature. NNs are used in order to anticipate the effective parameter on bronze atmospheric corrosion including the ambient temperature, exposition time, relative humidity, PH, SO2 concentration as an air pollutant and also metal's precipitations. As these factors are extremely complicated, exact mathematical language of the diverse metals corrosion are not comprehended. The results of this study showed that SO2 concentration as an air pollutant and time of exposition are the fundamental effects on corrosion weight loss of bronze.

引用

页码：324 / 332

页数：9

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