USING LATIN HYPERCUBE SAMPLING BASED ON THE ANN-HPSOGA MODEL FOR ESTIMATION OF THE CREATION PROBABILITY OF DAMAGED ZONE AROUND UNDERGROUND SPACES

The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.

USING LATIN HYPERCUBE SAMPLING BASED ON THE ANN-HPSOGA MODEL FOR ESTIMATION OF THE CREATION PROBABILITY OF DAMAGED ZONE AROUND UNDERGROUND SPACES Keywords:

latin hypercube sampling , artificial neural network , particle swarm optimization , genetic algorithm , The creation probability of damaged zone