Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Monitoring roof stress is essential for identifying structural anomalies and preventing disasters during underground construction. However, current sensors mainly focus on monitoring in one dimension, and it is challenging to obtain the mechanical status of the overall roof owing to the limitations of sensor numbers and the working environment. Therefore, we aimed to present an overall sensing method for the three-dimensional stress status of a roadway roof through machine learning (ML) based on limited monitoring points. First, the framework of the overall sensing method was developed, where a three-dimensional stress sensor was created to obtain the mechanical behaviours of some sensitive positions, and an ML model driven by the physical mechanism and limited monitoring data was developed to derive the overall stress situation. The developed sensor was installed in a case study, and the ML model was formulated based on the field-monitoring data. A series of experiments were conducted to derive the stress distribution of the roadway roof in the study case. Furthermore, a numerical simulation was conducted to compare the reasonability of the deduction results. The experimental results indicated that the deduction results of roof stress were reasonable, and thus the proposed sensing method is reliable.