Differential evolution algorithm for predicting blast induced ground vibrations

Saadat, Mahdi; Hasanzade, Ali; Khandelwal, Manoj

doi:10.1016/j.ijrmms.2015.03.020

Cited by 12 publications

(7 citation statements)

References 25 publications

(27 reference statements)

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“…Saadat et al attempt to reveal the differential evolution algorithm for predicting blast induced ground vibrations by contrast and analysis. Square and cubic root scaled distance predictors are the best among other empirical models [11]. This law is commonly described by the former Soviet M. A. Sodev's empirical formula (cubic root scaled distance predictors):…”

Section: Shock and Vibrationmentioning

confidence: 98%

Water-Depth-Based Prediction Formula for the Blasting Vibration Velocity of Lighthouse Caused by Underwater Drilling Blasting

Wang²,

Liu

et al. 2017

Shock and Vibration

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Lighthouses are the most important hydraulic structures that should be protected during underwater drilling blasting. Thus, the effect of blasting vibration on lighthouse should be studied. On the basis of the dimensional analysis, we deduced a revised formula for water depth based on Sodev's empirical formula and established the linear fitting model. During the underwater reef project in the main channel of Shipu Harbor in the Ningbo-Zhoushan Port, the blasting vibration data of the lighthouse near the underwater blasting area were monitored. The undetermined coefficient, resolvable coefficient, and value of the two formulas were then obtained. The comparison of the data obtained from the two formulas showed that they can effectively predict the blasting vibration on the lighthouse. The correction formula that considers water depth can obviously reduce prediction errors and accurately predict blasting vibration.

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Section: Shock and Vibrationmentioning

confidence: 98%

Water-Depth-Based Prediction Formula for the Blasting Vibration Velocity of Lighthouse Caused by Underwater Drilling Blasting

Wang²,

Liu

et al. 2017

Shock and Vibration

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“…Excavation of rock mass by blasting is comprised of two processes: releasing of explosive energy and movement of surrounding rock and soil [18,36]. The effect on rock and soil can be regarded as wave mechanics process that can be treated as stress wave spreading in the medium and disturbing to the medium [17,37,38]. After a comprehensive consideration, maximum amount of charge at one time (kg), total amount of charge (kg), horizontal distance (m), elevation difference (m), front row resistance line (m), presplit penetration ratio (%), integrity coefficient, angel of minimum resistance line to measured point ( ∘ ), and detonation velocity (m/s) are chosen as differentiating parameters prepared for inputs, presented as X1 (kg), X2 (kg), X3 (m), X4 (m), X5 (m), X6 (%), X7, X8 ( ∘ ), and X9 (m/s), respectively, as arranged in Table 1.…”

Section: Parameters From Fieldmentioning

confidence: 99%

“…Lapčević et al [16] predicted PPV by ANNs with different number of hidden nodes, and just 5 input parameters were considered, i.e., total charge (kg), maximum charge per delay (kg), distance from blasting shot (m), charge per hole (kg), and delay time (ms). Saadat et al [17,18] discussed differential evolution algorithm for predicting blast-induced ground vibrations, and nine input parameters were taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Jin

2018

Mathematical Problems in Engineering

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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.

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“…DE has demonstrated its robustness and power in a variety of applications, such as neural network learning [26]. DE has been now widely applied to solve various optimization problems from various fields, such as predicting blast-induced ground vibrations [28], optimization of air quantity regulation in mine ventilation networks [29], time series prediction [30], and power systems optimization [31].…”

Section: Differential Evaluationmentioning

confidence: 99%

Prediction of blast-induced flyrock using differential evolution algorithm

Dehghani

Shafaghi

2016

Engineering with Computers

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loading and hauling. Fragments can be thrown beyond the desired pile limit, which is normal but is considered adverse effect of blasting and is called flyrock. Fragments propelled far beyond predefined safety limit are considered security breach and are called wild flyrock [1].Due to high potential to cause damage to machinery and nearby structures and to cause injuries, even fatal, to personnel, flyrock is the most dangerous adverse effect of blasting and this phenomenon is responsible for 28.3 % of the damages and injuries in surface mining activities [2].To prevent flyrock problem, various parameters such as the physico-mechanical properties of rock mass, explosives specifications and geometrical aspects of the blasting pattern should be considered while designing a blasting pattern. For example, flyrock may occur in situations where there is insufficient stemming, too small a burden because of overbreak from the preceding or proceeding blast, planes of weaknesses in rock which reduce resistance to blasting, and finally existence of loose rock fragment on top of the bench [3]. Explosives with high energy can increase the probability of the flyrock. Most of the conventional formula used the TNT strength instead of the explosive strength.To convert this factor, usually, the ballistic mortar test was used (Fig. 1).Since this phenomenon is very dangerous, many researchers trying to establish the flyrock prediction models. Lundborg presented an equation for estimating the flyrock based on the hole diameter and specific charge as follows [4]:where L is the maximum rock projection (m), D is the hole diameter (in) and q is the specific charge (kg/m 3 ). Also, Lundborg et al. established another empirical model based on hole diameter as follows [5]:Abstract Flyrock is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to machinery and nearby structures and to cause injuries, even fatal, to personnel, flyrock is the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for flyrock estimation. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of differential evaluation algorithm (DE) and dimensional analysis algorithm (DA). For this purpose, the parameters of 300 blasting operations were accurately recorded and flyrock distances were measured for each operation. In the next stage, two new empirical predictors were developed to predict flyrock distance. The results clearly showed the superiority of the proposed DE-DA model in comparison with the empirical approaches.

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Differential evolution algorithm for predicting blast induced ground vibrations

Cited by 12 publications

References 25 publications

Water-Depth-Based Prediction Formula for the Blasting Vibration Velocity of Lighthouse Caused by Underwater Drilling Blasting

Water-Depth-Based Prediction Formula for the Blasting Vibration Velocity of Lighthouse Caused by Underwater Drilling Blasting

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

Prediction of blast-induced flyrock using differential evolution algorithm

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