Ubiquitous wireless sensor network (WSN) enables low-cost monitoring applications such as blast-induced ground vibration (BIGV) and structural health monitoring (SHM). In particular, monitoring and analysing the ambiguous BIGV waves are essential requisite to control and protect surrounding grievous damage structures. Similarly, improving health and longevity of structures using WSN is a new facet that owes to diminish the low-cost installation. Recent advances in WSNs are forging new prospects for sensors. A variety of intelligent sensors are integrated into the wireless system to monitor environmental, and health of civil infrastructures. Considering the current trends in the area of development of wireless monitoring prototypes, Micro-Electro-Mechanical-Systems (MEMS) accelerometer sensors are widely prevalent owing to the small size and inexpensive. In general, BIGV waves are less intensity and low-frequency signals. Hence, it is essential to select an appropriate accelerometer to detect micro-vibration waves. The study exemplifies a summarised review of recently made MEMS-based accelerometer wireless systems for intelligent and reliable monitoring of BIGV and SHM since the last decade. This research effort focuses on the numerous adopted accelerometers and their characteristics such as sensitivity, noise density, measurement range, bandwidth, resolution, network topologies, and performance of designed systems to analyse the microvibration levels comprehensively.

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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.

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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 (R 2) and standard error between recorded and predicted values of PPV.