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

Ragam, Prashanth; Nimaje, Devidas S.

doi:10.21595/jve.2017.18647

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…In 2018, Ragam and Nimaje have designed and developed a WSN blast-induced vibration monitoring system includes ADXL 345 accelerometer and ZigBee as RF module. The prototype was installed along with minimate plus seismograph at ACC Dungri limestone mine, India [20]. The authors [13][14][15][16][17][18][19][20] implemented a WSN system using RF modules such as ZigBee and ChipCon 2420 chip only.…”

Section: Previous Workmentioning

confidence: 99%

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

Ragam¹,

Nimaje²

2019

J. meas. eng.

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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.

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Section: Previous Workmentioning

confidence: 99%

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

Ragam¹,

Nimaje²

2019

J. meas. eng.

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“…Ragam and Nimaje evaluated the blast-induced ground vibration at different mines using MLPNN technique along with statistical predictor models to predict the PPV. The results were shown that the developed ANN gives more accurate prediction compare to other models [19][20] . Other researchers predicted PPV based on ANN models in different projects and found very superior results compared with conventional methods.…”

Section: Introductionmentioning

confidence: 95%

Estimation of ambiguous blast-induced ground vibration using intelligent models: A case study

Ragam

Nimaje

2018

Noise & Vibration Worldwide

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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. Accordingly, the proper assessment of indistinct blast-induced ground vibration is a requisite to pinpoint the safe limits in and around mines. An endeavor has been made in this article to apply four predictive models, namely, support vector machine, feed forward back propagation neural network, cascaded forward back propagation neural network, and radial basis function neural network to estimate the ground vibration caused by blasting operation conducted at Mine-A, India. In this article, a total number of 121 blasting operations with relevant parameters are recorded. The most influential parameters of ground vibration are the number of holes, burden, spacing, hole diameter, hole depth, top stemming, maximum explosive charge per delay, and the distance from blast source, which were considered as input parameters. Ground vibration is measured in terms of peak particle velocity and is considered as output. The performance indicators of constructed network models were chosen as the coefficient of determination (R 2), root mean square error, and variance account for. Among all constructed intelligent models, the radial basis function neural network with architecture 8-80-1 and R 2 of 0.9918, root mean square error of 4.4076, and variance account for of 99.1800 was found to be optimum. Sensitivity analysis showed that the number of holes, burden, and top stemming are the most effective parameters leads to ground vibration due to blasting.

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“…Few researchers were developed artificial neural network (ANN) models to predict the PPVin opencast mines. [9][10][11][12][13][14] Other researchers were predicted PPV based on various novel soft computing technique approaches such as artificial neural adoptive neuro fuzzy interference system (ANFIS), Gene Expression Programming (G EP), fuzzy interface systems (FIS), genetic algorithm (GA), support vector machine (SVM), group method of data handling (GMDH), particle swarm optimization (PSO), imperialist competitive algorithm (ICA), hybrid models including PSO-ANN, ICA-ANN in multiple projects and found very superior results compared to conventional predictor equations (see Table 2). The motto of the current study is to evaluate and estimate of ambiguous PPV by blasting process in Mine-A, India based on hybrid ensemble machine learning approach such as XGBoost-RF and Decision Tree, respectively.…”

Section: Previous Work(s)mentioning

confidence: 99%

Estimation of blast-induced peak particle velocity using ensemble machine learning algorithms: A case study

Ragam¹,

Komalla

Kanne

2022

Noise & Vibration Worldwide

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Over recent decades, ambiguous ground vibration induced by blasting operation can cause extensive damage to structures, lives and fields in and around mine premises. As a consequence, it is indispensable to measure the ambiguous ground vibration intensity levels for assessing and reduce their perilous impact. In this investigation, estimation and evaluation of blast-induced ground vibration in terms of peak particle velocity (PPV) through the ensemble machine learning intelligent algorithms were carried out. One hundred and 21 experimental and blasting events were monitored to collect the real-time field data in Mine-A, India. The collected data was randomly split into training and testing to generate models. Eight input parameters include number of holes, burden, spacing, hole diameter, hole depth, top stemming, maximum explosive charge per delay and the distance were selected for development of ensemble machine learning algorithms. An eXtreme gradient boosting (XGBoost) and random forest (RF) ensemble model, Decision Tree were developed to assess the PPV levels. In addition to that, four empirical predictor models proposed by the US Bureau of Mines, Langefors–Kihlstrom, Central Mining Research Institute, and Bureau of Indian Standards were applied to derive a relation between PPV and its influencing parameters. The accuracy and efficiency of developed models can be determined by performance evaluation metrics chosen as the coefficient of determination (R2), and root mean square error (RMSE). Among all models, yielded results evidence that the Decision Tree ensemble model with the R2 of 0.9549, and RMSE of 0.0444 was more precise optimum model to assess the PPV. Besides, a sensitivity analysis method was applied in this current study to know the role of the input parameters in estimating PPV. The determined results inferred that burden, number of holes and top stemming are more influenced parameters on the intensity of PPV levels.

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

Cited by 10 publications

References 8 publications

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

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

Estimation of ambiguous blast-induced ground vibration using intelligent models: A case study

Estimation of blast-induced peak particle velocity using ensemble machine learning algorithms: A case study

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