The artificial intelligence of things sensing system of real‐time bridge scour monitoring for early warning during floods

Abstract

Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross‐river structures, causing bridge col-lapse and a severe threat to property and life. Reductions in bridge‐safety capacity need to be mon-itored during flood periods to protect the traveling public. In the present study, a scour monitoring system designed with vibration‐based arrayed sensors consisting of a combination of Internet of Things (IoT) and artificial intelligence (AI) is developed and implemented to obtain real‐time scour depth measurements. These vibration‐based micro‐electro‐mechanical systems (MEMS) sensors are packaged in a waterproof stainless steel ball within a rebar cage to resist a harsh environment in floods. The floodwater‐level changes around the bridge pier are performed using real‐time CCTV images by the Mask R‐CNN deep learning model. The scour‐depth evolution is simulated using the hydrodynamic model with the selected local scour formulas and the sediment transport equation. The laboratory and field measurement results demonstrated the success of the early warning system for monitoring the real‐time bridge scour‐depth evolution.