Overbreak prediction of tunnels carved in rock mass through exponential smoothing: case study of tunnel in Brazil

Abstract

Among several works, constructions in rock masses are the most complex in engineering, due to many uncertainties of the environment. Tunnel construction is a case in point. A common problem found in tunnel excavations with the use of explosives is the occurrence of overbreak beyond the boundary line of the tunnel. Many researchers worldwide have proposed forecasting techniques based on the use of regression models or machine learning, however, these require many samples and variables, such as the explosive rate and rock mass. Predicting overbreak before blasting is essential in project management, as it can modify parameters of the blasting plan, design, schedule and employee safety. Therefore, this study sought to employ an exponential smoothing model, which could predict the percentage (%) of overbreak in a tunnel section, based solely on the previous overbreak values. This time series methodology has never been used before in tunnel excavations, although it is used in the financial market. The technique was tested in one Brazilian tunnel. The model proved to be very efficient in predicting overbreak in tunnel construction, since it can be adjusted at each advance. The model can be adjusted as a function of α: where α is close to zero, the model prioritizes past events, while for α close to 1, it prioritizes recent events. The best fit occurred with α = 0.9; 0<α<1.