Feasibility study on sinkhole monitoring with fiber optic strain sensing nerves

Abstract

Anthropogenic activity-induced sinkholes pose a serious threat to building safety and human life nowadays. Real-time detection and early warning of sinkhole formation are a key and urgent problem in urban areas. This paper presents an experimental study to evaluate the feasibility of fiber optic strain sensing nerves in sinkhole monitoring. Combining the artificial neural network (ANN) and particle image velocimetry (PIV) techniques, a series of model tests have been performed to explore the relationship between strain measurements and sinkhole development and to establish a conversion model from strain data to ground settlements. It is demonstrated that the failure mechanism of the soil above the sinkhole developed from a triangle failure plane to a vertical failure plane with increasing collapse volume. Meanwhile, the soil-embedded fiber optic strain sensing nerves allowed deformation monitoring of the ground soil in real time. Furthermore, the characteristics of the measured strain profiles indicate the locations of sinkholes and the associated shear bands. Based on the strain data, the ANN model predicts the ground settlement well. Additionally, micro-anchored fiber optic cables have been proven to increase the soil-to-fiber strain transfer efficiency for large deformation monitoring of ground collapse.

Keywords

• Sinkhole
• Geotechnical monitoring
• Distributed fiber optic sensing (DFOS)
• Artificial neural network (ANN)
• Ground settlement
• Soil arching