Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas

• Published in: Applied sciences Vol. 12; no. 24; p. 12654
• Main Authors: Ogunsola, Nafiu Olanrewaju, Shin, Chanhwi, Kim, Young-Keun, Lawal, Abiodun Ismail, Cho, Sangho
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: airblast overpressure; Algorithms; Artificial intelligence; Blasting; Closed form solutions; closed-form equation; Construction; Construction sites; Datasets; Engineering; Exact solutions; Excavation; Explosives; Forecasting; Geology; Human settlements; Methods; Mines; multi-verse optimization; Neural networks; Optimization; Overpressure; Public utilities; Regression analysis; Rock excavation; Rock mass rating; Rocks; Slime molds; slime mould algorithm; Soft computing; Support vector machines; unpleasant environmental problems; Urban areas; Urban planning; South Korea
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122412654

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.