Prediction of microtunnelling jacking forces using a probabilistic observational approach

Abstract

Listen

Translate article

Microtunnelling is an increasingly popular means of locating utilities below ground. The ability to predict the total jacking force requirements during a drive is highly desirable for anomaly detection, to ensure the available thrust is not exceeded, and to prevent damage to the pipe string and/or launch shaft. However, prediction of the total jacking force is complicated by site geology, the use of a lubricated overcut, work stoppages, tunnel boring machine driving style and pipe misalignment. This paper introduces a probabilistic observational approach for forecasting jacking forces during microtunnelling. Gaussian process regression is adopted for this purpose which allows forecasts to be performed within a probabilistic framework. The proposed approach is applied to two recent UK microtunnelling monitoring projects and the forecasts are appraised through comparisons to predictions determined using design methods currently applied in industry. The results show that the proposed framework provides excellent forecasts of the monitored field data and highlights a significant opportunity to complement existing prescriptive design methods with probabilistic forecasting techniques.