Performance evaluation of LoRa LPWAN technology for IoT-based blast-induced ground vibration system

The recent proliferation of wireless sensor networks (WSNs) evolution into the Internet of Things (IoT) vision enables a variety of low-cost monitoring applications which allows a seamless transfer of information via embedded computing and network devices. Ambiguous ground vibration can be induced by blasting demolition is a severe concern which grievously damages the nearby dwellings and plants. It is an indispensable prerequisite for measuring the blast-induced ground vibration (BIGV), accomplishing a topical and most active research area. Thus, proposed and developed an architecture which emphasizes the IoT realm and implements a low-power wide-area networks (LPWANs) based system. Especially, using the available Long-Range (LoRa) Correct as Radio Frequency (RF) module, construct a WSN configuration for acquisition and streaming of required data from and to an IoT gateway. The system can wirelessly deliver the information to mine management and surrounding rural peoples to aware of the intensity of BIGV level. In this article, an endeavor has been made to introduce a LoRa WAN connectivity and proved the potentiality of the integrated WSN paradigm by testing of data transmission-reception in a non-line of sight (NLOS) condition. The path loss metrics and other required parameters have been measured using the LoRa WAN technology at 2.4 GHz frequency

Monitoring of blast-induced ground vibration using WSN and prediction with an ANN approach of ACC dungri limestone mine, India

Blast-induced ground vibration (BIGV) is an undesirable environmental issue in and around mines. Usage of a high amount of explosive causes ground vibrations that are harmful to the nearby habitats and dwellings. In this paper, an attempt has been made to monitor the BIGV with low-cost wireless sensor network (WSN) and prediction of peak particle velocity (PPV) using an artificial neural network (ANN) technique at ACC Dungri limestone mine, Bargarh, Odisha, India. Eleven blasts PPV were recorded at different locations using wireless sensor network prototype system. The data has been transmitted by ZigBee (IEEE 802.15.4) protocol. The results are very promising and the recorded PPV varies from 0.191 mm/s to 8.60 mm/s. A three-layer, feed-forward back propagation neural network consists of 6 input parameters, 5 hidden neurons, and one output parameters were trained. Obtained results were compared based on correlation of determination (R2) and standard error between recorded and predicted values of PPV

Development of IoT-Based Real-Time Monitoring System and Prediction of Blast-Induced Ground Vibrations in Opencast Mines using Soft Computing Techniques

Blasting is an economical and viable operation for reliable excavation of hard rock in mining and civil construction. An ambiguous ground vibration generated by blasting is unenviable and causes grievous damage to nearby inhabitants, residential premises, and other sensitive sites. Consequently, proper monitoring and prediction of ambiguous ground vibration is an indispensable prerequisite to pinpoint the safe limits in and around mines to reduce their hazardous effects. Currently, conventional monitoring systems (seismographs) are widely used to measure the ground vibrations purposes. The existing systems have few limitations such as expensive, need an expert to operate, tedious, and time-consuming process. To mitigate the flaws of existing system, in this work, designed and developed a real-time, economical, reliable, continuous monitoring wireless system with Internet of Things (IoT) technology for blast-induced ground vibration (BIGV) measurement. The recent proliferation of wireless sensor networks (WSNs) evolution into the IoT vision enables a variety of low-cost monitoring applications which allows a seamless transfer of information via embedded computing and network devices. As Micro-Electro-Mechanical-System (MEMS) based accelerometer sensors are becoming widely prevalent in vibration and condition monitoring applications. Additionally, these sensors are integrated within a wireless sensor network (WSN) to allow monitored data to be transmitted wirelessly. The developed system was integrated with threeaxis, low-g, cost-effective ICM-20600 MEMS accelerometer and 32-bit ATSAMV71N21B microcontroller. In addition, a General Pocket Radio Service (GPRS) for SIM800C device was used as a radio frequency module and integrated to design an effective prototype. The experiment has been carried out by installing IoT prototype at Dungri limestone of ACC Limited, Bargarh, India, and twenty-two blast-events PPV was recorded at variable distances from blast source. The experiment results ensure that the Peak Particle Velocity (PPV) ranges from 0.081 to 2.94 mm/s at different monitoring locations. Similarly, in this study, to evaluate and predict the ambiguous PPV, seven conventional predictor models proposed by the United States Bureau of Mines (USBM), Ambraseys–Hendron, Langefors–Kihlstrom, General predictor, Ghosh–Daemen predictor, Central Mining Research Institute (CMRI) predictor, Bureau of Indian Standards, as well as Multiple Linear Regression (MLR), were applied and established a relation between PPV and its influencing parameters. The results were compared based on evaluation performance models such as Coefficient of Determination (R2), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), and Normalized Root Mean Vii Square Error (NRMSE) between monitored and predicted values of PPV. The outcomes of empirical predictors exhibit that the MLR model yields significant R2 (0.86420), MAD (0.24122), NRMSE (0.23539), and less RMSE (0.28940) as compared to other conventional predictor equations. Although, empirical predictor models have two major flaws such as lack of generalizability and a limited number of input variables. Therefore, a study on the development of an alternative method of accurate PPV prediction using soft computing techniques was undertaken. An endeavor has been made in this research to apply three soft computing prediction models, namely, Feed-Forward Back Propagation Multilayer Perception (MLP) Neural Network, Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). In this context, eleven input parameters such as number of holes, average top stemming, average spacing, average burden, average hole depth, hole diameter, maximum charge per delay, powder factor, total explosive, total depth, as well as absolute distance, and one output: PPV, was used and trained. The obtained results reveal that the RBFNN approach provides high R2 (0.99891), Accuracy (99.62956), MAD (0.00370), NRMSE (0.02287), and low RMSE (0.04897) among all other soft computing techniques and empirical predictor approaches for accurate prediction of blast-induced ground vibration. Hence, the RBFNN model yielded better performance as compared to the other prediction models to estimate the PPV

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure