An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations

Document identifier: oai:DiVA.org:ltu-75970
Access full text here:10.1080/00207233.2019.1662186
Keyword: Engineering and Technology, Civil Engineering, Other Civil Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Annan samhällsbyggnadsteknik, Blasting, Ground vibration, PPV, ANN, MLR, Mining and Rock Engineering, Gruv- och berganläggningsteknik
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructureSDG 3 Good health and wellbeing
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Abiodun Ismail Lawal

Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
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Musa Adebayo Idris

Luleå tekniska universitet; Geoteknologi; Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
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