| description abstract | In-service cables of structures, such as those in cable-supported buildings and cable bridges (e.g., stay cables and suspenders), suffer from cumulative fatigue damage caused by dynamic loads (e.g., the cyclic traffic loads on cable bridges) and wind excitation (on the cable-supported buildings and bridges). Monitoring the time history of time-varying cable tension for assessing their fatigue damage is thus essential to diagnose their health condition and predict their future performance. Currently, embedded measurement devices such as anchor load cells, elastomagnetic (EM) sensors, and optical fiber Bragg grating (OFBG) sensors are able to directly record the time-varying cable tension time history; however, poor durability, high costs, and intensive labor of installation significantly hinder their applicability in practice. On the other hand, a vibration-based technique manifests itself as a convenient, cost-effective, and reliable approach to determine the cable tension, and is widely used; it is based on an established formula (taut-string theory) between the cable tension and its frequency, which can be identified through the measured cable vibration responses. Existing research based on this approach, nevertheless, assume that the cable tension is time-invariant over a long time segment; real-time (online) identification of the time-varying cable tension has not yet been addressed. This paper develops a new computational framework to identify the time-varying cable tension time history through an unsupervised learning algorithm termed | |
