###### Abstract  
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. causing various unwanted environmental events, including air blast or air overpressure (AOp), blast-induced ground vibration (BIGV), flyrock, overbreak, and noxious gases (Figure 1) [6; 7; 8]. These unwanted environmental events from blasting must be addressed seriously, particularly in construction projects where blasting is undertaken near human settlements and public utilities. Hence, the accurate forecasting of the severity level of these events at varying distances from the point of blast initiation is crucial to prevent and minimize their occurrence and, subsequently, to ensure the effective use of rock excavation by blasting in construction projects. Airblast overpressure (AOp), also known as the \"overpressure wave,\" is an unwanted event that usually emanates from blasting operations and has recently received considerable attention owing to its virulence. AOp can cause deleterious damage to nearby buildings and important utilities and greater harm to nearby inhabitants. Some of the adverse effects of virulent AOps include the shattering of windows and roofing, visible structural cracking, deafness, the irritation of nearby residents, and eventual unresolved issues and litigation between inhabitants, construction companies, and mines [9; 10]. These issues could become even more critical in urban areas where blasting is performed near residential buildings and public infrastructure [4]. Owing to its potential to harm inhabitants and damage important structures and utilities, researchers have attempted to develop methods for quantifying AOp generation during blasting. These methods were developed using the influential factors causing AOp generation, including drill and blast design (controllable factors), meteorological factors, and geological factors (uncontrollable factors). The controllable factors include the burden, spacing, charge weight, monitoring distance, and explosive parameters, whereas the uncontrollable parameters include the rock and rock mass strength, such as the uniaxial compressive strength, and meteorological parameters, such as temperature and wind direction. Researchers have proposed empirical models and equations that use two of the blast design parameters, explosive maximum charge weight and the distance between the point of blast initiation and seismograph position, with some level of accuracy [11; 12].