Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach

Springer Science and Business Media LLC - Tập 74 - Trang 2799-2817 - 2015
Mohsen Hajihassani1, Danial Jahed Armaghani2, Masoud Monjezi3, Edy Tonnizam Mohamad2, Aminaton Marto2
1Construction Research Alliance, Universiti Teknologi Malaysia, UTM Skudai, Johor, Malaysia
2Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, Johor, Malaysia
3Department of Mining, Tarbiat Modares University, Tehran, Iran

Tóm tắt

Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting.

Tài liệu tham khảo

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