Artificial neural network approach on the seasonal variation of soil resistance

Asimakopoulou, FE; Tsekouras, GJ; Gonos, IF; Stathopulos, IA

dc.contributor.author	Asimakopoulou, FE	en
dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Gonos, IF	en
dc.contributor.author	Stathopulos, IA	en
dc.date.accessioned	2014-03-01T02:52:53Z
dc.date.available	2014-03-01T02:52:53Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36132
dc.subject.other	Artificial intelligence techniques	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Artificial neural network approach	en
dc.subject.other	Experimental data	en
dc.subject.other	Ground resistance	en
dc.subject.other	Grounding systems	en
dc.subject.other	Methodological approach	en
dc.subject.other	Non-linear relationships	en
dc.subject.other	Rainfall data	en
dc.subject.other	Seasonal variation	en
dc.subject.other	Single-value	en
dc.subject.other	Soil resistivity	en
dc.subject.other	Lightning	en
dc.subject.other	Soils	en
dc.subject.other	Neural networks	en
dc.title	Artificial neural network approach on the seasonal variation of soil resistance	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/APL.2011.6110235	en
heal.identifier.secondary	http://dx.doi.org/10.1109/APL.2011.6110235	en
heal.identifier.secondary	6110235	en
heal.publicationDate	2011	en
heal.abstract	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. © 2011 IEEE.	en
heal.journalName	2011 7th Asia-Pacific International Conference on Lightning, APL2011	en
dc.identifier.doi	10.1109/APL.2011.6110235	en
dc.identifier.spage	794	en
dc.identifier.epage	799	en