Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance

Year 2021, , 205 - 211, 31.12.2021

Serdar Ercins

https://doi.org/10.46460/ijiea.978343

Abstract

In this study, ground vibrations caused by blasting applications in a quarry were recorded and these values were evaluated and estimated by using an artificial neural network (ANN) model. Of the 28 vibration data measured, 20 were used for ANN training, 4 for validation and the remaining 4 for testing. In the model, peak particle velocity (PPV) was used as the output parameter, and the maximum explosive amount per delay and scaled distance were used as input parameters. In addition, MAPE, RMSE and R2 performance criteria were calculated from the realized, predicted by ANN and PPV values obtained from the field equation. The maximum amount of explosives used per delay and the sensitivity analysis of the scaled distance on the highest particle velocity were also determined. As a result, when the vibration data calculated from the field equation and estimated from the ANN model were compared with the realized vibration data, it was seen that the values obtained by the ANN model had a higher correlation.

Keywords

Artificial neural network, vibration, blasting, quarry, sensitivity analysis, MAPE, RMSE, R2

Patlama Kaynaklı Yer Titreşiminin YSA ile Tahmini ve Tahmin Performansı

Abstract

Bu çalışmada bir taş ocağında gerçekleştirilen patlatma uygulamalarından kaynaklanan yer titreşimleri kaydedilmiş ve bu değerler yapay sinir ağı (YSA) modeli kullanılarak değerlendirilmiş ve tahmin edilmiştir. Ölçümü alınan 28 titreşim verisinin 20 tanesi YSA’nın eğitimi, 4’ü doğrulama ve geriye kalan 4’ü de test için kullanılmıştır. Modelde çıktı parametresi olarak PPV, girdi parametresi olarak ise gecikme başına en fazla patlayıcı miktarı ve ölçekli mesafe kullanılmıştır. Ayrıca MAPE, RMSE ve R2 performans kriterleri, gerçekleşen, YSA ile tahmin edilen ve saha denkleminden elde edilen PPV değerlerinden hesaplanmıştır. Gecikme başına kullanılan en fazla patlayıcı madde miktarı ve ölçekli mesafenin, en yüksek parçacık hızı üzerindeki duyarlılık analizi de belirlenmiştir. Sonuçta, saha denkleminden hesaplanan ve YSA modelinden tahmin edilen titreşim verileri, gerçekleşen titreşim verileri ile karşılaştırıldığında, YSA modeli ile elde edilen değerlerin daha yüksek korelasyona sahip olduğu görülmüştür.

Keywords

Yapay sinir ağı, titreşim, patlatma, taş ocağı, duyarlılık analizi, MAPE, RMSE, R2

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