Reliable and accurate measurements of temperature and strain in structures subjected to fire can be difficult to obtain using traditional sensing technologies based on electrical signals. Fiber optic sensors, which are based on light signals, solve many of the problems of monitoring structures in high temperature environments; however, they present their own challenges. This paper, which is intended for structural engineers new to fiber optic sensors, reviews various fiber optic sensors that have been used to make measurements in structure fires, including the sensing principles, fabrication, key characteristics, and recently-reported applications. Three categories of fiber optic sensors are reviewed: Grating-based sensors, interferometer sensors, and distributed sensors.

This study develops a delamination detection system for smart ultra-high-performance concrete (UHPC) overlays using a fully distributed fiber optic sensor. Three 450 mm (length) × 200 mm (width) × 25 mm (thickness) UHPC overlays were cast over an existing 200 mm thick concrete substrate. The initiation and propagation of delamination due to early-age shrinkage of the UHPC overlay were detected as sudden increases and their extension in spatial distribution of shrinkage-induced strains measured from the sensor based on pulse pre-pump Brillouin optical time domain analysis. The distributed sensor is demonstrated effective in detecting delamination openings from microns to hundreds of microns. A three-dimensional finite element model with experimental material properties is proposed to understand the complete delamination process measured from the distributed sensor. The model is validated using the distributed sensor data. The finite element model with cohesive elements for the overlay-substrate interface can predict the complete delamination process.

In this study, a lightweight prestressed steel jacket (PSJ) was proposed and developed for rapid and cost-effective repair of a severely damaged circular reinforced concrete column. The PSJ is composed of several prestressed strands, and a thin steel sheet is restrained by these strands, which can be manually wrapped around and jointed to form a jacket on the column as part of a 12-h repair job by two workers. The prestressed strands restrain the thin sheet from buckling, while the steel sheet in turn prevents the strands from cutting into cracked concrete and thus preserves the prestressing forces. The PSJ was validated with cyclic (reversed) testing of two large-scale columns with lap-splice deficiency under incrementally increased displacements every three cycles. The ultimate strength and displacement ductility of the damaged column were restored to 115% and 140%, respectively, of those of the as-built column. The initial stiffness of the damaged column, however, was restored to only 84% of t...

This Letter reports a Fe-C coated long period fiber gratings sensor with a grating period of $387\pm 0.1\mathrm{\mu m}$ for corrosion monitoring of low carbon steel in a 3.5 wt. % NaCl solution. An LPFG sensor was first deposited with a 0.8 μm thick layer of silver (Ag) and then electroplated with a 20 μm thick Fe-C coating. The chemical composition of the Fe-C coating was designed to include the main elements of low carbon steel. The resonant wavelength of the coated sensor was correlated with the mass loss of steel over time. Test results indicated a corrosion sensitivity of 0.0423 nm per 1% mass loss up to 80% Fe-C mass loss and 0.576 nm per 1% mass loss between 80% and 95% Fe-C mass loss. The corrosion sensitivity of such a Fe-C coated LPFG sensor was a trade-off for the service life of the sensor, both depending on thicknesses of the inner silver layer and the outer Fe-C coating.

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research.

In this study, Brillouin scattering-based distributed fiber optic sensor is implemented to measure temperature distributions and detect cracks in concrete structures subjected to fire for the first time. A telecommunication-grade optical fiber is characterized as a high temperature sensor with pulse pre-pump Brillouin optical time domain analysis (PPP-BODTA), and implemented to measure spatially-distributed temperatures in reinforced concrete beams in fire. Four beams were tested to failure in a natural gas fueled compartment fire, each instrumented with one fused silica, single-mode optical fiber as a distributed sensor and four thermocouples. Prior to concrete cracking, the distributed temperature was validated at locations of the thermocouples by a relative difference of less than 9%. The cracks in concrete can be identified as sharp peaks in the temperature distribution since the cracks are locally filled with hot air. Concrete cracking did not affect the sensitivity of the distributed sensor but concrete spalling broke the optical fiber loop required for PPP-BOTDA measurements.