Mining plays an important role in Brazilian exports. On the other hand, large urban centers like São Paulo, with approximately 21 million inhabitants, also demand an increasing domestic consumption of natural resources, such as construction aggregate. There are many quarries located in the surroundings of urban centers in Brazil, competing with the growth of urbanized areas. Such proximity leads to a series of conflicts involving quarries and surrounding communities, where the increase in noise levels is highlighted. Operations in quarries, in general, are intermittent. Noisier equipment, such as drilling rigs and primary crushers, operates only a few hours during the day, while other operations, such as screening and secondary and tertiary crushing, are more constant. This paper presents a study carried out in a quarry located near São Paulo, where in addition to conventional short term noise measurements at surrounding receptors, one noise monitoring station was installed, allowing to identify the noisiest moments during the quarry operating time. Through data transmitted by wireless technology, it was possible to follow the noise variations emitted from mining activities in real time and observe the noisiest events that were recorded for events that exceeded the established standards. A mobile application associated to this monitoring station facilitated the quarry's manager and employees to access immediately the monitoring information. Therefore, by using this system, it was possible to evaluate the effectiveness of noise reduction measures already taken and indicate what steps still need to be held.
Blasting remains as an economical and reliable excavation technique, but there are some environmental shortcomings such as the control of blast-induced vibration. The impacts of vibration over surrounding communities in a blast area have been investigated for decades and researchers have been using a myriad of empirical predictive attenuation equations. These models, however, may not have satisfactory accuracy, since parameters associated to geomechanical properties and geology affect the propagation of seismic waves, making vibration modeling a complex process. This study aims for application of an Artificial Neural Network (ANN) method and Geomechanical parameter relationships to simulate the blast-induced vibration for a Brazilian mining site and then compare them to the traditional approach. ANN had the best performance for this mine despite having demanded large datasets (as much as for the traditional approach), while geomechanical parameters like RQD and GSI may be used to deliver a fair approach even without seismic data. Also, ANN methods may be useful in dealing with a large amount of information to facilitate the simulation process when combined with other methods. Therefore, alternative prediction methods may be helpful for small budget mining operations in planning and controlling blast-induced vibration and helping mining in urban areas becoming a more sustainable activity.
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