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An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations
Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering. Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.
2019 (English)In: International Journal of Environmental Studies, ISSN 0020-7233, E-ISSN 1029-0400Article in journal (Refereed) Epub ahead of print
Abstract [en]

This paper presents an artificial neural network (ANN) based mathematical model for the prediction of blast-induced ground vibrations using the data obtained from the literature. A feed-forward back-propagation multi-layer perceptron (MLP) was adopted, and the Levenberg–Marquardt algorithm was used in training the network. The powder factor, the maximum charge per delay, and distance from blasting face to monitoring point are the input variables. The peak particle velocity (PPV) is the targeted output variable. The model was then formulated using the weights and biases output from the ANN simulation. Multilinear regression (MLR) analysis was also performed using the same number of datasets, as in the case of ANN. The quality of the proposed ANN-based model was also tested with another 14 datasets outside the one used in developing the models and compared with more classical models. The coefficient of the determination (R2) of the proposed ANN-based model was the highest.

Place, publisher, year, edition, pages
Taylor & Francis, 2019.
Keywords [en]
Blasting, ground vibration, PPV, ANN, MLR
National Category
Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-75970DOI: 10.1080/00207233.2019.1662186OAI: oai:DiVA.org:ltu-75970DiVA, id: diva2:1350672
Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-09-12

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Idris, Musa Adebayo

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