Artificial Neural Network Approach on the Seasonal Variation of Soil Resistance
Objective of this paper is the development of a methodological approach for estimating the ground resistance by using artificial intelligence techniques (specifically, Artificial Neural Network).The value of the ground resistance greatly depends on the grounding system and the properties of the soil, where the system is embedded.Given that the value of soil resistivity fluctuates during the year, the ground resistance does not have one single value.The approach proposed in this paper, takes advantage of the capability of artificial neural networks (ANNs) to recognize linear and non-linear relationships between various parameters.By taking into account measurements of resistivity and rainfall data accrued for previous days, the ground resistance is estimated.On that purpose ANNs have been trained and validated by using experimental data in order to examine their ability to predict the ground resistance.The results prove the effectiveness of the proposed methodology.
Fani E. Asimakopoulou Georgios J. Tsekouras Ioannis F. Gonos Ioannis A. Stathopulos
School of Electrical and Computer Engineering,National Technical University of Athens,9 Iroon Polyte Department of Electrical Engineering and Computer Science,Hellenic Naval Academy,Terma Hatzikiriaku,
国际会议
成都
英文
1-6
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）