Crack generation starts at the crack tip, which bears the highest stress concentration. Under further stress, the crack propagates and leads to severe structural damage. To avoid such damage, the identification of the crack tips, and monitoring of the surrounding stress and strain fields, are very important. In this work, the location of, and strain distribution monitoring around, the crack tip are achieved using a dense ultra-short (DUS) fiber Bragg grating (FBG) array together with an improved optical frequency domain reflectometry (OFDR) interrogator. The adjacent grating interference correlation algorithm helps overcome the limitation on the demodulation precision, which is imposed by the inherently broad reflection spectra of individual ultra-short gratings. High spatial resolution measurement of the strain profile around the crack tip is performed at different levels of induced strain. Furthermore, the vertical-crossed layout is adopted to avoid the omission of cracks, which usually occurs in the case of the one direction layout. We achieve 1 mm spatial resolution and 7.5 m detection distance. Location of a single crack, multiple cracks, and an oblique crack was realized experimentally by locating the crack tips. The experimental results are consistent with the theoretical analysis, verifying the feasibility of the DUS-FBG system for high-density distributed crack tip sensing.

Distributed fiber optic sensing (DFOS) has emerged as a critical technology for structural health monitoring of large-scale infrastructure, offering unique advantages in terms of coverage and environmental adaptability. This review presents a comprehensive analysis of the two dominant technical routes: fully distributed sensing based on intrinsic backscattering and massive-capacity sensing based on ultra-weak fiber Bragg grating (UWFBG) networks. For backscattering-based systems—encompassing Raman, Brillouin, and Rayleigh scattering—the inherent trade-offs among signal-to-noise ratio (SNR), spatial resolution, and sensing range constitute major performance bottlenecks. This review systematically summarizes advanced demodulation and signal processing strategies designed to overcome these physical barriers, including pulse coding sequences, chaotic laser compressed correlation, and deep learning-enhanced noise reduction algorithms. In parallel, for UWFBG-based technologies, the evolution from traditional multiple-point fiber Bragg grating (FBG) array to quasi-distributed and fully distributed UWFBG network is discussed. This review highlights key breakthroughs in achieving high spatial resolution and high-speed interrogation through hybrid multiplexing, aliased spectrum reconstruction, and dispersion-based demodulation techniques. By synthesizing recent advances in modulation schemes, detection hardware, and algorithmic processing, this paper outlines the trajectory of DFOS technologies toward high-precision, long-distance, and real-time sensing networking.

Distributed optical fiber sensing (DOFS), along with its capabilities of long-range coverage, multi-parameter monitoring, and completely passive detection, emerges as one of the most promising non-destructive detection techniques for structural health monitoring (SHM) and operational assessment of linear transportation infrastructures. In this paper, we provide a state-of-the-art review on DOFS applications across typical linear infrastructure systems, encompassing highways, long-span bridges, rail transit networks, airport runways, and analogous linear structures. The comprehensive discussion consists of four critical research dimensions: 1) optical fiber selection for multi-parameter sensing and robust cable packaging techniques, 2) distributed sensing principles and signal processing algorithms, 3) diverse application scenarios in SHM and related fields, and 4) anomaly detection and event classification methodologies. Building upon the foundational introduction of DOFS technical principles and monitoring solutions for intelligent transportation infrastructure, this paper elaborates on system design approaches, sensing data analytics algorithms, and future research directions.