Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Safe Vibrations of Spilling Basin Explosions at “Gotvand Olya Dam” Using Artificial Neural Network
Ground vibration is an undesirable outcome of an explosion which can have destructive effects on the surrounding environment and structures. Peak Particle Velocity (PPV) is a determining factor in evaluation of the damage caused by an explosion. To predict the ground vibration caused by blasting at the Gotvand Olya Dam (GOD) spilling basin, thirty 3-component records (totally 90) from 19 blasts were obtained using 3 VIBROLOC seismographs. Minimum and the maximum distance from the center of the exploding block to the recording station were set to be 11 and 244 meters, respectively. To evaluate allowable safe vibration and determining the permissible explosive charge weight, Artificial Neural Networks (ANN) was employed with Back Propagation (BP) and 3 hidden layers. The mean square error and the correlation coefficient of the network in this study were found to be 1.95 and 0.995, respectively, which compared to those obtained from the known empirical correlations, indicating substantially more accurate prediction. Considering the network high accuracy and precision in predicting vibrations caused by such blasting operations, the nearest distance from the center of the exploding block at this study was 11 m, and considering the standard allowable vibration of 120 mm/sec for heavy concrete structures, the maximum permissible explosive weight per delay was estimated to be 47.00 Kg. These results could be employed in subsequent safer blasting operation designs.
Safe Vibrations of Spilling Basin Explosions at “Gotvand Olya Dam” Using Artificial Neural Network
Ground vibration is an undesirable outcome of an explosion which can have destructive effects on the surrounding environment and structures. Peak Particle Velocity (PPV) is a determining factor in evaluation of the damage caused by an explosion. To predict the ground vibration caused by blasting at the Gotvand Olya Dam (GOD) spilling basin, thirty 3-component records (totally 90) from 19 blasts were obtained using 3 VIBROLOC seismographs. Minimum and the maximum distance from the center of the exploding block to the recording station were set to be 11 and 244 meters, respectively. To evaluate allowable safe vibration and determining the permissible explosive charge weight, Artificial Neural Networks (ANN) was employed with Back Propagation (BP) and 3 hidden layers. The mean square error and the correlation coefficient of the network in this study were found to be 1.95 and 0.995, respectively, which compared to those obtained from the known empirical correlations, indicating substantially more accurate prediction. Considering the network high accuracy and precision in predicting vibrations caused by such blasting operations, the nearest distance from the center of the exploding block at this study was 11 m, and considering the standard allowable vibration of 120 mm/sec for heavy concrete structures, the maximum permissible explosive weight per delay was estimated to be 47.00 Kg. These results could be employed in subsequent safer blasting operation designs.
Safe Vibrations of Spilling Basin Explosions at “Gotvand Olya Dam” Using Artificial Neural Network
Bakhshandeh Amnieh, Hassan (Autor:in) / Bahadori, Moein (Autor:in)
Archives of Mining Sciences ; 59 ; 1087-1096
2014
10 Seiten, 20 Quellen
Aufsatz (Zeitschrift)
Englisch
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