## 2 Methods
### Bb-Ann  
The evolutionary algorithm BBO was proposed by [PERSON] [36]. The basic idea of BBO is based on biogeography concepts: (i) migration of habitants (species) from a habitat (island) to another habitat, (ii) arising of habitants, and (iii) extinction of habitants. BBO is the most popular optimization algorithm used to solve complex, non-linear real-world problems [37]. In this paper, BBO is used to optimize the network weights and biases of ANN. Each habitat contains network weights and biases as the number of features or habitants. The main operation of BBO is a migration process which involves an immigration (I) and emigration (E) rate. The exploration and exploitation tasks of BBO depend on migration operators. It is a successful technique for bringing local search and good convergence capability to a global optimum. The BBO algorithm modifies features of a selected habitat based on the immigration rate. Then the algorithm choses other habitats based on their emigration rate for migration of an inhabitant of a habitat to another selected habitat. Initially, BBO generates number of problem solutions (ANN generations) based on the size of the habitats. After each of the run of the main algorithm best fitness (minimum) the valued habitat is stored and this process is continued for a maximum number of iterations run or until the accepted level of the fitness score is reached. Mean-square error (MSE) is used as the fitness function for BBO algorithm. A more detailed review of BBO algorithm can be found in reference [37; 38]. The Algorithm 1 shows a rudimentary structure of BBO-ANN. ```
1. Select calibration and validation dataset
2. Begin ANN calibration period
3. Get ANN learning operators in BBO (decision variables (N));
4. Set objective/fitness function (MSE);
5. Initialize habitats (say S);
6. Set mutation probability (\(m_{k}\));
7. Calculate \(E\) and \(I\). 8. Evaluate the fitness measure for every habitat;
9. Sort habitats (ascending order) according to the fitness value;
10. B = best so far habitat (least fitness valued habitat);
11. for it = 1 to maximum iteration do
12. for i = 1 to S do
13. for j = 1 to N do
14. if rand() < \(I\) of \(i\)th habitat then
15. choose an emigrating habitat with a probability proportional to \(E\);
16. Replace jth habitat of the immigrating habitat with a corresponding value of the emigrating habitat;
17. end if
18. for j = 1 to N do
19. if rand() < \(m_{k}\) then
20. Update jth habitat with rand \(\star\) (\(u_{j}-l_{j}\));
21. end if
22. end for
23. Evaluate fitness value of the \(i\)th habitat;
24. end for
25. Elitism
26. Sort habitats. 27. B = Keep the good solution;
28. end for
29. End
30. Acquire the optimal parameter set for ANN using B;
31. ANN test

## 3 Field Investigation

Two quarry mines in the Shur river dam region, located in the southwest of Iran, were investigated in this study. Andesite and tuff were the types of bed rock in these mines. The blasting method was performed to fragment rock mass in the monitored mines. Controlling blasting environmental issues like flyrock and ground vibration was considered to be an important task there. Lots of equipment was used for construction purposes and to assist workers in mine sites. In addition, a residential area is very close to the mines and some local people live there. As a result, there is a need for the blasting engineers to predict, monitor and control the effects of ground vibrations on nearby residents and building structures. There was always a high risk of causing blast-induced ground vibration damage affecting nearby residents and buildings. Based on the recommendations of mining engineers in the site, the measurement of ground vibration was conducted for every single blasting operation. For the mentioned descriptions, the amount of environmental risk due to ground vibration at blasting mines in the study area was high and because of this, the development of any models or techniques that can minimize the risk is useful.