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Automated intelligent hybrid computing schemes to predict blasting induced ground vibration

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Abstract

Blasting has been widely recognized as an economical and viable method in geo-engineering projects. However, the induced ground vibration in terms of peak particle velocity (PPV) potentially can damage the nearby environment and inhabitants. Therefore, more accurate prediction of the PPV can lead to reduce undesirable and hazardous effects of blasting. With the increase in the computational power, wide variety of predictive PPV models using numerical tools and data mining approaches have been presented. In this paper, the optimum predictive PPV model was specified using generalized feedforward neural network (GFFN) structure integrated with a novel automated intelligent setting parameter approach. Subsequently, two new optimized hybrid models using GFFN incorporated with firefly and imperialist competitive metaheuristic algorithms (FMA and ICA) were developed and applied on 78 monitored events in Alvand–Qoly mine, Iran. According to analyzed metrics, the predictability level of hybrid GFFN-FMA dedicated 6.67% and 20% progress than GFFN-ICA and optimum GFFN. The pursued performance using precision–recall curves and ranked accuracy criteria also exhibited superior improvement in GFFN-FMA. Sensitivity analyses implied on the importance of the distance and burden as the most and least effective factors on predicted induced PPV in the study area.

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Abbreviations

GFFN:

Generalized feed forward neural network

FMA:

Firefly metaheuristic algorithm

ICA:

Imperialistic competitive metaheuristic algorithm

PPV:

Peak particle velocity

MAs:

Metaheuristic algorithms

FT:

Fitness function

ANN:

Artificial neural network

MLP:

Multilayer perceptron

GSN:

Generalized shunting neuron

TA:

Training algorithm

AF:

Activation function

QP:

Quick propagation

CGD:

Conjugate gradient descent

QN:

Quasi-Newton

L–M:

Levenberg–Marquardt

MO:

Momentum

Log:

Logistic

Hyt:

Hyperbolic tangent

Lin:

Linear

AUC:

The area under the curve

ROC:

Receiver-operating characteristics

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Authors and Affiliations

  1. Department of Civil Engineering, Islamic Azad University, Roudehen Branch, Tehran, Iran

    Abbas Abbaszadeh Shahri & Reza Asheghi

  2. Johan Lundberg AB, Uppsala, Sweden

    Abbas Abbaszadeh Shahri

  3. School of Computer Sciences, Chamran Technical Institute, Roudehen, Tehran, Iran

    Fardin Pashamohammadi

  4. Department of Civil, Environment and Construction Engineering, Texas Tech University, Lubbock, TX, USA

    Hossein Abbaszadeh Shahri

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  1. Abbas Abbaszadeh Shahri
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  2. Fardin Pashamohammadi
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  3. Reza Asheghi
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  4. Hossein Abbaszadeh Shahri
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Correspondence to Abbas Abbaszadeh Shahri.

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Abbaszadeh Shahri, A., Pashamohammadi, F., Asheghi, R. et al. Automated intelligent hybrid computing schemes to predict blasting induced ground vibration. Engineering with Computers 38 (Suppl 4), 3335–3349 (2022). https://doi.org/10.1007/s00366-021-01444-1

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