Consideration of the amount of learning in the automatic calculation method of tunnel excavation cycle by AI

Abstract

Mountain tunnel excavation work basically involves repeating the same tasks．For this reason，managing cycle time，which is the working time per cycle，is extremely important in improving productivity．However，measuring cycle time is a time-consuming process．Therefore，the authors developed a method to automatically calculate it using AI．In order to improve the AI’s judgment accuracy， it is necessary to have it learn a certain amount of information from multiple work sites，but the amount of learning required is unknown．In this paper，the authors verified and considered the relationship between the amount of learning and judgment accuracy in blast excavation and mechanical excavation，which are typical excavation patterns for mountain tunnels．The verification results showed that by increasing the amount of learning，the overall judgment accuracy improved in blasting excavation，but did not improve much in mechanical excavation．So there’s room for improvement in the judgment method.