Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling
Given their technical and economic advantages, the application of explosive substances to rock mass excavation is widely used. However, because of serious environmental restraints, there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vib...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2021-06-01
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| Series: | Underground Space |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967419300984 |
| _version_ | 1797716944793632768 |
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| author | Gustavo Paneiro Manuel Rafael |
| author_facet | Gustavo Paneiro Manuel Rafael |
| author_sort | Gustavo Paneiro |
| collection | DOAJ |
| description | Given their technical and economic advantages, the application of explosive substances to rock mass excavation is widely used. However, because of serious environmental restraints, there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations. In the present study, an artificial neural network (ANN) with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results; furthermore, quantitative and qualitative parameters were considered for ground vibration amplitude prediction. The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters, 12 neurons in a one-layer hidden layer, and a sigmoid transfer function. Compared with the traditional models, the model obtained using the proposed methodology demonstrated better generalization ability. Furthermore, the proposed methodology offers an ANN model with higher prediction ability. |
| first_indexed | 2024-03-12T08:29:01Z |
| format | Article |
| id | doaj.art-9feb51815dba4179a309b971dfe31356 |
| institution | Directory Open Access Journal |
| issn | 2467-9674 |
| language | English |
| last_indexed | 2024-03-12T08:29:01Z |
| publishDate | 2021-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj.art-9feb51815dba4179a309b971dfe313562023-09-02T17:55:36ZengKeAi Communications Co., Ltd.Underground Space2467-96742021-06-0163281289Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modelingGustavo Paneiro0Manuel Rafael1DECivil/CERENA, Tecnico Lisboa, ULisboa, Portugal; Corresponding author.Orica Mining Services Portugal, PortugalGiven their technical and economic advantages, the application of explosive substances to rock mass excavation is widely used. However, because of serious environmental restraints, there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations. In the present study, an artificial neural network (ANN) with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results; furthermore, quantitative and qualitative parameters were considered for ground vibration amplitude prediction. The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters, 12 neurons in a one-layer hidden layer, and a sigmoid transfer function. Compared with the traditional models, the model obtained using the proposed methodology demonstrated better generalization ability. Furthermore, the proposed methodology offers an ANN model with higher prediction ability.http://www.sciencedirect.com/science/article/pii/S2467967419300984Rock blastingExcavationGround vibrationsArtificial neural networkK-fold cross-validationModeling |
| spellingShingle | Gustavo Paneiro Manuel Rafael Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling Underground Space Rock blasting Excavation Ground vibrations Artificial neural network K-fold cross-validation Modeling |
| title | Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling |
| title_full | Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling |
| title_fullStr | Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling |
| title_full_unstemmed | Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling |
| title_short | Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling |
| title_sort | artificial neural network with a cross validation approach to blast induced ground vibration propagation modeling |
| topic | Rock blasting Excavation Ground vibrations Artificial neural network K-fold cross-validation Modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2467967419300984 |
| work_keys_str_mv | AT gustavopaneiro artificialneuralnetworkwithacrossvalidationapproachtoblastinducedgroundvibrationpropagationmodeling AT manuelrafael artificialneuralnetworkwithacrossvalidationapproachtoblastinducedgroundvibrationpropagationmodeling |