###### Abstract
These empirical relations are easier to employ, less laborious, and more cost-effective. However, owing to geological heterogeneity and differences in blast area conditions from location to location, these models are generally mine-specific and often less accurate [13; 14]. Compared to empirical equations, numerical models provide more accurate models that are closer to field measurements, as demonstrated by [PERSON] and [PERSON] [15] and [PERSON] et al. [16] using Autodyn2D and LS-Dyna, respectively. However, numerical solutions are often time-consuming and costly. Hence, there is a need to devise a more reliable and accurate, albeit non-time-consuming, method for quantifying the hazardous impacts of AOp using soft computing or artificial intelligence methods. Soft computing techniques such as artificial neural networks (ANNs), random forest (RF), ensemble methods, extreme learning machines (ELMs), fuzzy inference systems, support vector machines (SVMs), and hybrid models have recently been widely used to forecast AOp [7; 14; 17; 18; 19; 20]. The surge in the use of these intelligent methods can be attributed to the accuracy, cost-effectiveness, and non-time consumption of these models. Soft computing methods are versatile approximation methods owing to their non-parametric nature and ability to accept additional factors that influence AOp generation [7; 21]. [PERSON] et al. [19] forecasted AOp due to blasting using 62 blasting events measured at quarry sites in Malaysia using a cascaded forward neural network (CFNN) trained by the Levenberg-Marquardt (LM) algorithm (CFNN-LM) and compared it with the results of a generalized regression neural network (GRNN) and an extreme learning machine (ELM). They showed that the forecasted values of AOp by the CFNN-LM were much closer to the field measurements than those by the GRNN and ELM. In another study, [PERSON] and [PERSON] [14] employed an SVM model to predict AOp using 75 datasets from three mines in India and compared its predictive performance with that of an empirical equation. The analyses showed that the SVM model was better than the empirical equation for predicting AOp. [PERSON] and [PERSON] [17] predicted the AOp using hybridized ANN-RF, ANN, and RF models from 114 blasting events obtained from a quarry in Vietnam. Based on a comparison of these methods with empirical models, they concluded that soft computing models provide more accurate predictions than empirical models. Similar studies on the utilization of soft computing models to predict AOp can be found in [8; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. According to the no-free-launch (NFL) hypothesis, despite the advantages of various machine learning (ML) algorithms, no single ML technique can be considered the most appropriate method for solving all engineering problems [36]. Thus, numerous ML algorithms have been investigated, and new algorithms are being developed to solve various complex engineering optimization problems, including AOp prediction. Furthermore, the existing AOp models are limited to one or a few sites and are all based on blasting for mine production, and none accurately predict the AOp generated by blasting for construction rock excavations. Thus, this study proposes novel optimization algorithms known as the slime mold algorithm (SMA) and multi-verse optimization (MVO) to optimize an ANN and predict AOp generation from multiple construction blasting sites. To the best of our knowledge, the SMA has never been used for the prediction of AOp in the literature. The first novelty of this study is the transformation of AOp ANN-based models into easy-to-implement, explicit, and closed-form equations. The developed explicit, closed-form equations can be easily implemented in an Excel spreadsheet without prior knowledge of ANN principles, thereby simplifying the practical engineering implementation of the models. The second novelty lies in the employment and evaluation of the impacts of both controllable and uncontrollable factors (rock mass rating) on AOp generation.