Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Abstract

Blasting vibration is harmful to the nearby habitants and dwellings in diverse geotechnical engineering. In this paper, a novel scheme based on Artificial Neural Network (ANN) method optimized by dimensionality reduction of Factor Analysis and Mean Impact Value (FA-MIV) is proposed to predict peak particle velocity (PPV) of blasting vibration. To construct the model, nine parameters of field measurement are taken as undetermined input parameters for research, while peak particle velocity (PPV) is considered as output parameter. With the application of FA, common factors are extracted from undetermined input parameters. Then, principal components are defined as a linear combination of common factors. The weight of each principal components effected on output parameter is ranked according to the calculation of MIV, and two principal components with minimum weight are eliminated. Ultimately, output parameter (PPV) is explained in a low-dimensional space with four input characteristic parameters. In the prepared database consisting of 108 datasets, 98 datasets are used for the training of the model, while the rest are used for testing performance. The performances of the ANN models are compared with regression analysis, in terms of coefficient of determination (R2) and mean absolute error (MAE). It is found that the performances of ANN models with using FA-MIV are superior to those of models without using FA-MIV in the prediction of PPV. In addition, the abilities of ANN models are all superior to regression analysis in the prediction of PPV. The result obtained from ELM is more accurate than BPNN and MVRA models.

Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

“…Neural network output variation is directly affected by the input value variation. It was previously defined as MIV which can be calculated as follows [32]:…”

Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation

“…In this section, NN sensitivity is introduced and the link between sensitivity and connections is addressed. A three-layer back propagation (BP) NN structure as shown in Figure 1 was illustrated in previous research [23,24]. The output of the output layer can be expressed as follows [24]:…”

“…The NN square error value with respect to actual output F k , k = 1, 2, , m, can be expressed as in (4) without consideration of a threshold value [23,24].…”

“…In previous research [23,24], the mean impact value (MIV) was used to predict building electricity consumption and improve the prediction accuracy of building energy consumption, where we proposed a simplified neural network and verified its effectiveness. During the investigation, we applied several neural network algorithms, and compared their sensitivities.…”

“…As previously mentioned, NN SA has been carried out by analyzing output variance with respect to the variation of inputs and connected weights [21,22]. Besides analytical SA, MIV is also applied to analyze the effect on the output by the variation of input variables [23,24]. Due to the NN structure, which can be considered as a black box, we measure the output variation under perturbation in the input data set.…”