Real-Time Damage Self-Diagnosing and Self-Localizing Parallel Steel Wire Smart Cables

Abstract

Parallel steel wire cables and parallel steel strand cables are the most commonly used cable types in current stages, often serving as crucial load-bearing components in large-span spatial structures, cable-supported bridges, and similar constructions. However, they frequently encounter issues such as steel wire breakage, corrosion, and loosening of anchor heads, posing significant threats to structural safety. Therefore, timely identification of these damages is crucial. To address this issue, based on the previously proposed parallel steel strand smart cables by the authors, this paper proposes real-time damage self-diagnosis and self-localization parallel steel wire smart cables, replacing a portion of the steel wires with optical fiber–steel composite intelligent steel wires. These intelligent steel wires are composed of ordinary steel wires with a groove and embedded sensing optical fibers, encapsulated with material, providing distributed stress monitoring capabilities. The study investigates the stress redistribution mechanism of parallel steel wires after steel wire breakage and its application in detecting steel wire breakage. First, various finite element models of parallel steel wire cables under different steel wire breakage cases are established. Identification, localization, and quantification methods for steel wire breakage damage are summarized, and the sensitivity of different parameters to the recognition of the number of broken steel wires is studied. Second, to further validate the effectiveness of the proposed smart cable in steel wire breakage identification, localization, quantification, and monitoring, experiments were conducted on a 54-m-long parallel steel wire smart cable. Various numbers of steel wires at different locations were sawed, and then loaded to simulate actual steel wire breakage damages. Based on numerical simulation results and experimental data, steel wire breakage can be identified, localized, and quantified by observing the strain waveform and the increase in peak strain of intelligent steel wires near the broken steel wires. Third, the proposed parallel steel wire smart cable’s real-time self-localization function as a suspender cable for suspension bridges is validated. The smart cable offers real-time stress self-sensing, damage self-diagnosis, localization, and damage warning capabilities, making it applicable throughout the entire lifecycle of cable structures. This significantly reduces the likelihood of cable failures, providing support for intelligent civil infrastructure, particularly smart cable structures.