Environmentally sensitive blasting design based on risk analysis by using artificial neural networks

Özer, Ümit; Karadoğan, Abdulkadir; Özyurt, Meriç Can; Şahinoğlu, Ülkü Kalaycı; Sertabipoğlu, Zeynep

doi:10.1007/s12517-018-4218-7

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Many different techniques have been to predict blast-induced ground vibrations such as neuro-fuzzy inference system (Iphar et al, 2010;Koçaslan et al, 2017), random forest method (Longjun et al, 2011;Ohadi et al, 2019;Zhou et al, 2020), support vector machine (Hasanipanah et al, 2015), artificial neural networks (Dehghani & Ataee-Pour, 2011;Monjezi et al, 2011;Armaghani et al, 2015;Hajihassani et al, 2015;Azimi et al, 2019;Ozer et al 2019), Hilbert-Huang transform (Zhao et al, 2021) and dynamic finite-element method (Peng et al, 2021).…”

Section: = ( ) −mentioning

confidence: 99%

Blast Induced Vibration Estimation by Using Different Machine Learning Methods in a Tunnel Excavation

Özyurt

Şahinoğlu

Karadoğan

et al. 2021

Preprint

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This study aimed to develop a new model in which rock characteristics, blasting design parameters and excavation planning were also considered by using various machine learning methods in a funicular line excavation where blast-induced vibrations could not estimate with a high correlation by using the commonly and successfully used PPV-SD (Peak Particle Velocity-Scaled Distance) estimation formula. In addition to developing a new model, another aim was to reveal the effect of rock characteristics, blasting and excavation planning parameters on PPV estimation numerically in the form of weights. For this purpose, 225 events in 57 shots were recorded in the funicular line excavation. Each blasted cross-section's rock characteristics were obtained from on-site inspection and geological reports. At first, recorded blasting vibration data were evaluated using the well-known PPV–SD equation, and it was seen that the relationship between PPV and SD was not able to represent the site-specific vibration attenuation. Therefore, the obtained data were evaluated with Random Forest and other Machine Learning Methods. In these evaluations, RQD, UCS, unit of advance, the maximum charge per delay, the cross-sectional area of tunnel face, total charge, and distance between shot point and vibration measurement station were used as inputs, and peak particle velocity was used as output. The results showed that the random forest model's prediction accuracy was more acceptable than the well-known PPV–SD equation and other machine learning methods. Another significant finding of the study is that parameters not considered in PPV estimation, such as UCS, RQD, and cross-sectional area of tunnel face, may be more effective than the commonly used scaled distance.

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confidence: 99%

Blast Induced Vibration Estimation by Using Different Machine Learning Methods in a Tunnel Excavation

Özyurt

Şahinoğlu

Karadoğan

et al. 2021

Preprint

Self Cite

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“…ANN are widely used in the study of soil vibrations and earthquakes [45][46][47][48][49][50][51][52][53].…”

Section: Soil Mechanicsmentioning

confidence: 99%

Applying Artificial Neural Networks In Construction

Doroshenko

2020

E3S Web Conf.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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“…In the last decade, researchers have applied several soft computing techniques and statistical analysis methods to predict ground vibration and other environmental effects of rock blasting. Some researchers applied neural networks or ANFIS technique (e.g., Bakhshandeh Amnieh et al 2010; Armaghani et al 2014;Kocaslan et al 2017, Ozer et al 2019, Ozer et al 2020. Hasanipanah et al (2015), Dindarloo (2015), and Masmoudi et al (2020) practiced support vector machines and machine learning methodology to investigate environmental issues.…”

Section: Introductionmentioning

confidence: 99%

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

Hudaverdi

Akyildiz

2021

Arab J Geosci

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This research focuses on blast-induced structural damage and human response to vibrations. Frequency is one of the important parameters that affect damage probability and human perception. The first and primary target of the study is to predict ground vibration and frequency simultaneously. A feed-forward back propagation neural network was created for modeling. Three operational parameters and vibration measurement distance were used as input parameters. The developed model was compared to the regression equations. Different error measures were considered to perform a vigorous model validation. Ten different blasting level charts were used to estimate structural damage probability. The regulations from North America, Europe, and Southern Hemisphere were examined and compared. Variation in the vibration regulations was also discussed. Some suggestions were put forward for the development of an international regulation. Two practical charts were created to predict human response to vibrations. A specific graph, which was proposed for pile driving induced vibrations, was considered for evaluation of blast vibrations. The developed soft computing framework can forecast probability of structural damage and human perception of vibrations effectively.

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Environmentally sensitive blasting design based on risk analysis by using artificial neural networks

Cited by 9 publications

References 23 publications

Blast Induced Vibration Estimation by Using Different Machine Learning Methods in a Tunnel Excavation

Blast Induced Vibration Estimation by Using Different Machine Learning Methods in a Tunnel Excavation

Applying Artificial Neural Networks In Construction

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

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