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Wyświetlanie 1-2 z 2
Tytuł:
Impact of voids and backfill on seismic wave velocity-preliminary results
Autorzy:
Mukhamedyarova, Zarina
Suorineni, Fidelis
Aldubay, Temirlan
Sapinov, Gylym
Powiązania:
https://bibliotekanauki.pl/articles/2201421.pdf
Data publikacji:
2022
Wydawca:
Główny Instytut Górnictwa
Tematy:
seismic event source location accuracy
constantly changing mine conditions
real-time seismic wave velocity
void and cemented sand backfill effect with time
dokładność lokalizacji źródła zdarzeń sejsmicznych
stale zmieniające się warunki kopalniane
prędkość fali sejsmicznej w czasie rzeczywistym
Opis:
In this study, laboratory experiments were conducted on discrete physical models that mimic mining effects to better understand the impact of continuous changes in mining environments on seismic wave velocities. The discrete physical models are represented by concrete and granite cubic samples of different sizes with holes of different diameters filled and unfilled with cemented sand backfill of different cement-sand content ratios. The hole diameters range from 0 to 150 mm in block sizes ranging from 150 mm to 450 mm in increments of 75 mm. The increasing hole size mimics increasing extraction in the mine with time. Cemented sand fills at cement contents ranging from 0 to 20% are used to fill the voids after testing them empty and retesting the same at different backfill cured ages. The SAEU3H AE eight-channel system is used in the study. Preliminarily results show that the impact of continuous changes in mining environments significantly affects the seismic wave velocities. The impact of voids and their contents on the seismic wave velocity depends on the sensor location relative to source and void, and it backfills cement content with time.
Źródło:
Journal of Sustainable Mining; 2022, 21, 4; 319--333
2300-1364
2300-3960
Pojawia się w:
Journal of Sustainable Mining
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees
Autorzy:
Tambouratzis, T>
Souliou, D.
Chalikias, M.
Gregoriades, A.
Powiązania:
https://bibliotekanauki.pl/articles/91652.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
traffic accident
location
prediction
probabilistic neural networks
random forest
accuracy
efficiency
decision tree
Opis:
The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 31-42
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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