Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas
Blasting has been proven to be the most cost-effective method for rock excavation known to man. The cost-effectiveness advantage of blasting is overshadowed by its unpleasant environmental problems, particularly at construction sites close to human settlements and public utilities. Therefore, effort...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12654 |
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| author | Nafiu Olanrewaju Ogunsola Chanhwi Shin Young-Keun Kim Abiodun Ismail Lawal Sangho Cho |
| author_facet | Nafiu Olanrewaju Ogunsola Chanhwi Shin Young-Keun Kim Abiodun Ismail Lawal Sangho Cho |
| author_sort | Nafiu Olanrewaju Ogunsola |
| collection | DOAJ |
| description | Blasting has been proven to be the most cost-effective method for rock excavation known to man. The cost-effectiveness advantage of blasting is overshadowed by its unpleasant environmental problems, particularly at construction sites close to human settlements and public utilities. Therefore, efforts are required to develop closed-form equations that can accurately predict environmental problems associated with blasting. This study proposes an ANN-based closed-form explicit equation for forecasting airblast overpressure (AOp) at multiple construction sites in South Korea. Nine important factors that affect AOp generation were used to develop the model. First, a stand-alone ANN was initiated, and the hyperparameters of the optimum ANN structure were tuned using two novel and robust metaheuristic algorithms: the slime mould algorithm (SMA) and multi-verse optimization (MVO). To appraise the predictive accuracy of the developed soft computing models, multilinear regression (MLR) and a generalized empirical predictor were developed for comparison. The analysis showed that the SMA-ANN and MVO-ANN models predicted AOp with the highest accuracy compared with the other models. The two hybrid ANN-based models were transformed into closed-form and explicit equations to aid in the easy forecasting of AOp when planning a blasting round at construction sites. The developed model equations were validated for practical engineering applications and a comprehensive relative importance analysis of the AOp input parameters was performed. The relevance importance analysis shows that the rock mass rating (RMR), charge per delay (Q), and monitoring distance (DIS) have the highest impacts on AOp. |
| first_indexed | 2024-03-09T17:23:12Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:23:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-996160fb26654951a4faa610dfd1a0a72023-11-24T13:02:25ZengMDPI AGApplied Sciences2076-34172022-12-0112241265410.3390/app122412654Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban AreasNafiu Olanrewaju Ogunsola0Chanhwi Shin1Young-Keun Kim2Abiodun Ismail Lawal3Sangho Cho4Department of Mineral Resources & Energy Engineering, Faculty of Engineering, Jeonbuk National University, Jeonju-si 54896, Republic of KoreaDepartment of Mineral Resources & Energy Engineering, Faculty of Engineering, Jeonbuk National University, Jeonju-si 54896, Republic of KoreaTerra Engineering Limited, Daejeon 34018, Republic of KoreaDepartment of Mining Engineering, Federal University of Technology, Akure 340110, NigeriaDepartment of Mineral Resources & Energy Engineering, Faculty of Engineering, Jeonbuk National University, Jeonju-si 54896, Republic of KoreaBlasting has been proven to be the most cost-effective method for rock excavation known to man. The cost-effectiveness advantage of blasting is overshadowed by its unpleasant environmental problems, particularly at construction sites close to human settlements and public utilities. Therefore, efforts are required to develop closed-form equations that can accurately predict environmental problems associated with blasting. This study proposes an ANN-based closed-form explicit equation for forecasting airblast overpressure (AOp) at multiple construction sites in South Korea. Nine important factors that affect AOp generation were used to develop the model. First, a stand-alone ANN was initiated, and the hyperparameters of the optimum ANN structure were tuned using two novel and robust metaheuristic algorithms: the slime mould algorithm (SMA) and multi-verse optimization (MVO). To appraise the predictive accuracy of the developed soft computing models, multilinear regression (MLR) and a generalized empirical predictor were developed for comparison. The analysis showed that the SMA-ANN and MVO-ANN models predicted AOp with the highest accuracy compared with the other models. The two hybrid ANN-based models were transformed into closed-form and explicit equations to aid in the easy forecasting of AOp when planning a blasting round at construction sites. The developed model equations were validated for practical engineering applications and a comprehensive relative importance analysis of the AOp input parameters was performed. The relevance importance analysis shows that the rock mass rating (RMR), charge per delay (Q), and monitoring distance (DIS) have the highest impacts on AOp.https://www.mdpi.com/2076-3417/12/24/12654airblast overpressureunpleasant environmental problemssoft computingclosed-form equationslime mould algorithmmulti-verse optimization |
| spellingShingle | Nafiu Olanrewaju Ogunsola Chanhwi Shin Young-Keun Kim Abiodun Ismail Lawal Sangho Cho Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas Applied Sciences airblast overpressure unpleasant environmental problems soft computing closed-form equation slime mould algorithm multi-verse optimization |
| title | Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas |
| title_full | Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas |
| title_fullStr | Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas |
| title_full_unstemmed | Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas |
| title_short | Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas |
| title_sort | development of an ann based closed form equation for the prediction of airblast overpressure induced by construction rock excavation blasting in urban areas |
| topic | airblast overpressure unpleasant environmental problems soft computing closed-form equation slime mould algorithm multi-verse optimization |
| url | https://www.mdpi.com/2076-3417/12/24/12654 |
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