Incorporating Distributed Fibre Optic Sensing and Machine Learning Into Dry Deep Mixing

Dry deep mixing (DDM) has frequently been employed as a method to improve the engineering properties of soft soils such as clay and peat. Today’s methods to quantify the strength and stiffness of DDM improved ground are limited in terms of their spatial and temporal resolution. This lack of reliable monitoring data often results in uncertainty about the field performance. As a consequence, DDM projects often use overly conservative design solutions which could have negative economic and environmental implications. Recent smart sensing techniques including distributed fibre optic sensing (DFOS), which could provide the required high-resolution data, have, however, not yet been adopted to DDM. Another shortcoming of DDM is that the prediction of the improved ground properties relies on resource intensive laboratory and field testing. Advanced prediction methods based on machine learning (ML) techniques have not yet been sufficiently explored for DDM. This contribution addresses these current gaps in DDM practice by presenting results from the application of DFOS and ML to DDM. The validation of DFOS for DDM improved ground, drawing on data from laboratory and full-scale field trials, is discussed. The obtained results highlight that fibre optical cables installed in DDM improved ground can reliably map the stress-strain response of improved soil columns. Gradient boosting and random forest techniques are utilized to predict the properties of laboratory improved soil for DDM. The application of these ML techniques to existing databases shows that these methods outperform currently used prediction models based on, for example, the water-to-binder ratio. Overall, this contribution presents latest applied research on incorporating digitalization into DDM.