Prediction of subsidence due to underground mining by artificial neural networks

Tomaž Ambrožič (Avtor), Goran Turk (Avtor)

Povzetek

Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network was used. The training and testing of neural network is based on the available data. Input variables represent extraction parameters and coordinates of the points of interest, while the output variable represents surface subsidence data. After the neural network has been successfully trained, its performance is tested on a separate testing set. Finally, the surface subsidence trough above the projected excavation is predicted by the trained neural network. The applicability of ANN for the prediction of surface subsidence was verified in different subsidence models and proved on actual excavated levels and in levelled data on surface profile points in the Velenje Coal Mine. (C) 2003 Elsevier Science Ltd. All rights reserved.

Ključne besede

umetna nevronska mreža;napovedovanje ugreznine;večslojna usmerjena nevronska mreža;aproksimacija funkcij;rudarska škoda;artificial neural network;subsidence prediction;multi-layer feed=forward neural network;approximation of functions;mining damage;

Podatki

Jezik:	Angleški jezik
Leto izida:	2003
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FGG - Fakulteta za gradbeništvo in geodezijo
Založnik:	Elsevier
UDK:	624.131.5
COBISS:	1972833
ISSN:	0098-3004
Št. ogledov:	2870
Št. prenosov:	1074
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Angleški jezik
Sekundarne ključne besede:	umetna nevronska mreža;napovedovanje ugreznine;večslojna usmerjena nevronska mreža;aproksimacija funkcij;rudarska škoda;
Vrsta datoteke:	application/pdf
Vrsta dela (COBISS):	Delo ni kategorizirano
Strani:	str. 627-637
Letnik:	ǂVol. ǂ29
Čas izdaje:	2003
DOI:	10.1016/S0098-3004(03)00044-X
ID:	8312291