Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data

Load prediction of tunnel boring machines (TBMs) is crucial for the design and safe operation of these complex engineering systems. However, to date, studies have mostly used only geological data, but the operation of TBMs also has an important effect on the load, especially its dynamic behavior. With the development of measurement techniques, large amounts of operation data are obtained during tunnel excavation. Mining these heterogeneous in-situ data, including geological data and operation data, is expected to improve the prediction accuracy and to realize dynamic predictions of the load. In this paper, a dynamic load prediction approach is proposed based on heterogeneous in-situ data and a data-driven technique. In this approach, the integration of heterogeneous in-situ data is conducted as follows: i) the geological data are extended to match the scale of the operation data using an interpolation method; ii) the categorical data and numerical data are fused through a proposed encoding method; and iii) the geological data are combined with the operation data according to the location of each operation datum. A data-driven technique, Random forest, is used to construct the prediction model based on the integrated heterogeneous in-situ data. The approach is applied to a collection of heterogeneous in-situ TBM data from a tunnel in China, and the results indicate that the approach can not only accurately predict the dynamic behaviour of the load but can also precisely estimate the statistical characteristics of the load. This work also highlights the applicability and potential of data-driven techniques in the design and analysis of other complex engineering systems similar to TBMs.