PATLATMA KAYNAKLI PARÇALANMA MODELLERİNİN BİR KUMTAŞI OCAĞINDA İNCELENMESİ

Parçalanma modelleri bir kumtaşı ocağında basamak patlatması operasyonları için incelenmiştir. Patlatma kaynaklı parçalanmanın değerlendirilmesi amacıyla farklı bakış açıları ortaya konmuştur. Her atım için boyut dağılım eğrileri çizilmiştir. KuzRam modelinin başarılı sonuçlar verdiği görülmüştür. KCO modeli özellikle ince boyutlu malzemenin tahmininde başarılıdır. Modellerin yetkinliği ince, orta ve iri boyutlu malzeme miktarının tahmini açısından detaylı bir şekilde incelenmiştir. Chung ve Katsabanis modeli pratik olarak uygulanmış ve ocak için bir modifikasyon önerilmiştir. Modellerin validasyonu için on farklı hata ölçütü kullanılmıştır. Simetrik hata ve ölçekli hata ölçütlerinin parçalanmanın değerlendirilmesi amacıyla kullanılabilirliği incelenmiştir. KuzRam modeli ortalama parça boyutunu üç santimetrenin altında bir hata ile tahmin edebilmektedir. Modifiye edilmiş Chung ve Katsabanis modeli de başarılı sonuçlar vermiştir.

INVESTIGATION OF BLAST FRAGMENTATION MODELS IN A SANDSTONE QUARRY

The existing blast fragmentation models were investigated in a sandstone quarry for bench blasting operations. It was tried to present different perspectives for evaluation of blast fragmentation. Size distribution curves were created for each blast. KuzRam model provided successful results. Kuznetsov-Cunningham-Ouchterlony (KCO) model was useful especially for the fine size predictions. Capability of the models for fine, mean and coarse size predictions were evaluated in detail. Uniformity coefficients proposed by the models were examined. Chung & Katsabanis model was practically tested and a modification implemented for the studied quarry. Model validation was performed using ten different error metrics. Applicability of symmetric errors and scaled errors was investigated for evaluation of blast fragmentation. KuzRam model predicted mean fragment size with an error lower than three centimeters. The results of the modified Chung & Katsabanis model is quite promise.

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