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[Display omitted] • Cracks are monitored using distributed fiber optic sensor (DFOS) and deep learning. • A modified You Only Look Once (YOLO) model adequately interprets DFOS data. • Transfer learning is incorporated to improve the accuracy of the deep learning model. • The robustness of the proposed approach is evaluated in different test scenarios. • The mAP@0.5 of detecting spatially-distributed cracks reaches 0.968. Distributed fiber optic sensors (DFOSs) offer unique capabilities for crack monitoring via measuring strain distributions. However, manually interpreting strain distributions is labor-intensive and time-consuming. To address this challenge, this paper presents a deep learning approach for real-time automatic interpretation of strain distributions, aiming at monitoring spatially-distributed cracks. The proposed approach encompasses three key innovations. First, deep learning-based methods are developed to facilitate automatic detection and localization of spatially-distributed cracks. Second, transfer learning is incorporated to overcome the data scarcity issue in training deep learning models. This ensures robust performance even with limited data. Third, a split-and-merge method is developed, enhancing the accuracy of multi-crack detection. To evaluate the performance of the approach, experimental data from various cases were considered. The results demonstrate a mean average precision (mAP) of 0.968 for crack detection. The processing time for a set of DFOS data, containing 10,000 measurement points, was less than 0.05 s. [ABSTRACT FROM AUTHOR]

Abstract Corrosion causes mass loss of steel bars, concrete cracking, and interface degradation in reinforced concrete, highly compromising the safety and durability of civil infrastructure. This research proposes a smart reinforced concrete instrumented with a distributed fiber optic sensor for in-situ monitoring, presents an innovative method to quantify mass loss of steel bar using unique distributed sensor data, and studies corrosion-induced expansion of steel bar in steel fiber reinforced concrete. Electrochemical test and the distributed sensor data were used to understand the corrosion deterioration process of the steel-concrete composite. Effect of the steel fiber on the deterioration process is evaluated under different concrete surface conditions, and the underlying mechanisms are investigated. The results indicate that steel fibers reduced the corrosion rate of steel bars by mitigating electron transfer from the steel bar, delaying concrete cracking, and limiting the crack width. The effect of concrete surface defects shows a transition phenomenon. This research gains insights into the corrosion deterioration mechanism and strategies for improving the long-term durability of steel-concrete composite. [ABSTRACT FROM AUTHOR]

Distributed fiber optic sensors have exhibited superior capabilities in monitoring cracks in engineering structures through measuring detailed strain distributions. However, manually interpreting the measurements from long distributed sensors deployed in large-scale structures is time-consuming, labor-intensive, and subject to human errors. This paper proposes to automate the identification, localization, quantification, and visualization of cracks through intelligent interpretation of strain distributions measured from distributed fiber optic sensors based on machine learning. Based on these intelligent capabilities, a live digital twin model based on building information modeling is developed to visualize cracks. The digital twin model is updatable with real-time measurements from strain distributions from distributed fiber optic sensors. The proposed approach is evaluated via laboratory testing of a concrete beam. The results show that the proposed approach achieves high accuracy in interpretation of sensor data for crack monitoring. This research advances the capabilities of structural health monitoring using distributed fiber optic sensors. [Display omitted] • Automatic interpretation of strain distributions from distributed fiber optic sensors is enabled. • Machine learning approaches are presented to identify, locate, quantify, and visualize cracks. • A digital twin model is developed to visualize cracks and updated by real-time measurement. • Unsupervised and supervised learning approaches achieve high accuracy and high efficiency. [ABSTRACT FROM AUTHOR]

Monitoring corrosion in reinforced concrete is critical to assess the health condition of structures at an early stage and enable effective and efficient asset management. Various novel fiber optic sensors have been developed and demonstrated many advantages in monitoring corrosion in reinforced concrete under different conditions. However, currently, there is a gap between sensor technology innovation and engineering practices, and the gap hinders further development and wide applications of fiber optic sensors. This paper reviews representative types of fiber optic sensors for monitoring corrosion in reinforced concrete. The reviewed types of sensors include grating sensors, interferometer sensors, distributed sensors, and reflectometer sensors. For each type of sensors, the reviewed contents include the sensing principle and key features of applications, such as sensor design and installation, sensitivity, resolution, and sensor life. Emphasis is placed on discussing the functions and performance of the sensors in different corrosion stages. Based on the review, technical challenges and future research needs are identified and discussed. This review is expected to advance sensor development and promote effective applications of fiber optic sensors for intelligent structures and asset management. [ABSTRACT FROM AUTHOR]

Optical fibers with protective coatings have been used as distributed strain sensors for automated inspection in the construction, operation, and maintenance of various engineering structures. The presence of the protective coatings causes strain transfer effect, which can change the strain measurement of the distributed sensors. This study quantitatively evaluates the strain transfer for distributed fiber optic sensors subjected to an arbitrary strain field for the first time. Theoretical studies are performed to derive closed-form solutions for describing the strain transfer, and high-resolution (sub-millimeter) strain distributions were measured to validate the theoretical study. This study demonstrates that the strain transfer effect is dependent on the strain field in the host matrix, and the derived formulae enable correct interpretation of the strain measurement from the distributed sensor. This study provides theoretical foundations for using distributed fiber optic sensors to accurately measure strain distributions in engineering structures. Image 1 • The strain transfer in an optical fiber under an arbitrary strain field of host matrix is studied. • It is the first time to discover that the strain transfer ratio in distributed sensor can exceed 1. • The proposed method is validated using high-resolution measurement of strain distributions. • The study provides theoretical foundation for using distributed fiber optic strain sensors. [ABSTRACT FROM AUTHOR]

• A method is proposed to measure crack widths using a single distributed fiber optic sensor. • The measurement accuracy of crack width using the distributed sensor is as fine as 5.6 µm. • The widths of multiple cracks can be measured using the same distributed sensor. • Influences of spatial resolution, coating thickness, and crack spacing are investigated. • The tradeoff of the maximum measurable crack width and the spacing is quantified. This study develops a method to measure crack width using a distributed fiber optic sensor. To this end, a customized specimen was designed to manipulate cracks under precise displacement control. An optical fiber was attached on the specimen and served as a distributed sensor which measured strain distributions along the fiber based on optical frequency domain reflectometry. An algorithm was presented to analyze crack width based on the measured strain distribution. Compared with the crack width measured by an extensometer, the crack width measured by the distributed sensor had an accuracy of 5.6 µm. Parametric studies were conducted and revealed that the coating thickness of optical fiber, the spatial resolution of strain distribution, and the spacing of cracks had significant effects on the measurement of cracks using the distributed sensor. This study is expected to enhance the capability of detecting, locating, and quantifying cracks by using distributed fiber optic sensors. [ABSTRACT FROM AUTHOR]

Detailed information about temperature distribution can be important to understand structural behavior in fire. This study develops a method to image three-dimensional temperature distributions in steel–concrete composite slabs using distributed fiber optic sensors. The feasibility of the method is explored using six 1.2 m × 0.9 m steel–concrete composite slabs instrumented with distributed sensors and thermocouples subjected to fire for over 3 h. Dense point clouds of temperature in the slabs were measured using the distributed sensors. The results show that the distributed sensors operated at material temperatures up to 960 °C with acceptable accuracy for many structural fire applications. The measured non-uniform temperature distributions indicate a spatially distributed thermal response in steel–concrete composite slabs, which can only be adequately captured using approaches that provide a high density of through-depth data points. [ABSTRACT FROM AUTHOR]

Summary: In this study, the strain transfer rate from an axially loaded, inelastic concrete tube to a glass fiber reinforced polymer (GFRP) packaged optical fiber with Bragg gratings is derived when the radial deformation of an "equivalent elastic" concrete tube is constrained by the packaged fiber. The concrete strains, both undisturbed and disturbed by the presence of the fiber Bragg gratings sensor, are analytically evaluated, and their difference (up to over 30%) is related to the development length at two free ends of the GFRP package. The mechanism of strain transfer is dominated by a ratio of average fiber and concrete strains in elastic range and by the averaging effect and a ratio of disturbed and undisturbed concrete strains in inelastic range. The analytical strain transfer rate was significantly reduced from 0.95, when concrete behaved elastically, to less than 0.4, when concrete damaged severely. This result was experimentally validated with less than 10% difference prior to concrete fracture. The validated model is applicable to fiber optic sensors that are embedded into concrete structures by a concrete cover of at least 10 times of the radius of the optic fiber. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of distributed fiber optic sensor for corrosion monitoring of steel bars embedded in concrete. Two sensor installation methods are compared: (1) attaching the sensor along the bar and (2) winding the sensor on the bar. For the second method, optical fibers were winded spirally on steel bars with different spacings: 0 mm, 2 mm, 5 mm, and 10 mm. Steel bar pull-out testing was conducted to evaluate the effect of presence of distributed sensor on the bond strength of steel–concrete interface. Electrochemical testing was carried out to assess the influence of the installation methods on the corrosion resistance of the reinforced concrete. Winding the optical fiber on steel bars with a 10-mm spacing does not affect the bond strength and corrosion resistance and allows real-time corrosion monitoring. The distributed sensor data can be used to estimate the corrosion induced steel loss and predict concrete cracking. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pipeline corrosion is monitored using distributed fiber optic sensors and machine learning. • Distributed fiber optic sensor data are automatically interpreted for monitoring corrosion. • Bayesian optimization is used to enhance the accuracy of the machine learning model. • Mass loss due to steel corrosion in pipelines is quantified. • The robustness of the proposed approach is investigated in various scenarios. Distributed fiber optic sensor (DFOS) offers unique capabilities of monitoring corrosion for long pipelines. However, manually interpreting DFOS data is labor-intensive and time-consuming. To address this challenge, this paper presents a machine learning approach for real-time automatic interpretation of DFOS data used to monitor both uniform and non-uniform corrosion in pipeline. A machine learning model is developed to automatically detect corrosion based on DFOS data, and a corrosion quantification method is developed based on the output of the machine learning model. The proposed approaches are evaluated using laboratory experiments in terms of accuracy and robustness to pipeline diameter, spatial resolution of DFOS, type of fiber optic cable, and sensor installation methods. The results show that the F1 score for corrosion detection and the R2 value for corrosion quantification are 0.986 and 0.953, respectively. This research will facilitate pipeline corrosion monitoring by enabling automatic distributed sensor data interpretation. [ABSTRACT FROM AUTHOR]

• This paper developed the generalized fiber optic-based sensing models for precise quantification of corrosion severity and its growth rate under impact loads. • A time-dependent corrosion assessment model using Weibull function fits well with the collected data of the mean pit depth. • It demonstrates the potential of fiber optic sensors (FOSs) for real-time structural health monitoring in multi-factor challenging conditions. Structural integrity can be compromised by the simultaneous presence of mechanical loads and corrosive agents. This study investigates the complex interplay between corrosion and impact loads in steel plates, utilizing discrete Fiber Bragg Grating (FBG) and distributed Optical Frequency Domain Reflectometry (OFDR) sensing technology. Generalized fiber optic-based sensing models are developed to quantify corrosion severity and rate. The experimental study was conducted using twelve epoxy-coated steel plates equipped with FBG and OFDR sensors, covering scenarios of individual exposure to corrosion, impact loads, as well as their combination. Test results reveal that specimens subjected to combined conditioning exhibit more corrosion damage than those subjected to individual corrosion. Both pit depth and its growth rate were exacerbated due to the impact loads. The study demonstrates the potential of fiber optic sensors (FOSs) for real-time monitoring and assessment of structural health under different simultaneous multiple factors in challenging conditions. [ABSTRACT FROM AUTHOR]

• Strain distributions sensed by distributed fiber optic sensors are subject to strain transfer effect. • An inverse analysis is presented to convert measured strains into real strain distributions. • The method is applicable when the type and location of loading and strain field are unknown. • Multiple metaheuristic optimization algorithms are used and compared for the inverse analysis. • The method has been implemented to a prestressed concrete beam under four-point bending test. Strains measured by distributed fiber optic sensors with protective coatings can be different from the real strains in host matrix due to strain transfer effect. This study presents a novel inverse method to determine the real strain distributions in host matrix using strain distributions measured from distributed fiber optic sensors. Two different scenarios of strain distributions in host matrix are investigated, which have known and unknown types of strain fields, respectively. Three representative metaheuristic algorithms are respectively utilized in the inverse analysis, which are colliding bodies optimization algorithm, particle swarm optimization algorithm, and genetic algorithm. The performance of the proposed method was evaluated by analytical case studies and experimental testing of a prestressed concrete beam subjected to four-point bending. The results demonstrated high accuracy and efficiency of the proposed method. This study is expected to greatly advance distributed fiber optic sensing capabilities in measuring strain distributions for structural health monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a method to measure the shrinkage and cracks of ultra-high-performance concrete (UHPC) using distributed fiber optic sensors. UHPC prisms and rings were prepared and instrumented with fiber optic cables which served as distributed sensors to assess restrained shrinkage based on the Optical Frequency Domain Reflectometry (OFDR) technology with a 0.65-millimeter resolution. The initiation and development of shrinkage cracks were detected, located, quantified, and visualized from the strain distributions. The effect of steel fibers, lightweight sand, and shrinkage reducing agent on shrinkage distribution and crack pattern was investigated. The results indicate that the restrained shrinkage of UHPC decreased from 809 µɛ to 245 µɛ by using steel fibers, lightweight sand, and shrinkage reduce agent. The maximum crack width decreased from 1.605 mm to 0.065 mm. This research enhances the capability of monitoring shrinkage and cracks and supports the development of low-shrinkage UHPC. • A single fiber optic cable is utilized as a distributed sensor for measuring strain distributions. • Ring specimens are instrumented with distributed sensors to monitor restrained shrinkage. • Early-age shrinkage cracks in UHPC rings are detected, located, quantified, and visualized. • The effect of steel fibers, lightweight sand, and shrinkage reducer on shrinkage is investigated. • The development of low-shrinkage UHPC helps enhance durability of concrete structures. [ABSTRACT FROM AUTHOR]

Interactive anomalies of pipelines represent important contributors to pipeline incidents, but monitoring interactive anomalies is challenging. This paper presents an approach to monitor interactive bending and dent deformations that occur at the same pipeline section. The proposed approach utilizes distributed fiber optic sensors to measure strain distributions of pipelines with a high resolution (0.65 mm) in real time. Steel pipes instrumented with fiber optic cables along their length and circumference directions were tested under four-point bending to generate interactive bending and dent deformations. The strain distributions measured from the distributed sensors were analyzed to detect, locate, quantify, visualize, and distinguish interactive bending and dent deformations in the loading process. The strains measured from distributed fiber optic sensors aligned with those measured from fiber Bragg grating sensors, displacement sensors, and computer vision. This study enhances the ability to monitor interactive threats in pipelines, contributing to improved condition assessments and infrastructure resilience. • A method for monitoring interactive bending and dent deformations of pipelines is presented. • A single fiber optic cable is used as a distributed sensor for measuring strain distributions. • Strain distributions of pipelines are used to assess interactive bending and dent deformations. • Interactive effects on pipelines are detected, located, quantified, visualized, and distinguished. • The proposed method is beneficial for automation in pipeline maintenance and management. [ABSTRACT FROM AUTHOR]

Fiber optic sensors represent an innovative technology for automated measurement of cable forces which are critical in construction and operation of many civil engineering structures. This paper reviews the fiber optic sensors that have been developed and applied to measure cable forces, including fiber Bragg grating, interferometer, and fully distributed sensors. The reviewed contents of each sensor technology include the sensing principle, sensor design, deployment on cables, data analytics, and sensing performance. The interactive behaviors between the sensor and the cable are discussed regarding the impacts on the measurement performance and mechanical properties of the cable, considering the sensor package and deployment methods. The performance and technology readiness levels of the different fiber optic sensors are compared. A guide is provided to select appropriate sensors for specific applications. This review is expected to promote further development and applications of fiber optic sensors for intelligent measurement of cable forces. [Display omitted] • Measurement of cable forces by using point and distributed fiber optic sensors is reviewed. • Fiber optic sensors measure the cable force along cable length in construction and operation. • Different types of fiber optic sensors and deployment methods are compared and discussed. • Technology readiness levels and selection of appropriate sensors are discussed. • Current challenges and future opportunities of fiber optic sensor technologies are reviewed. [ABSTRACT FROM AUTHOR]

This study explores an instrumentation strategy using distributed fiber optic sensors to measure strain and temperature through the concrete volume in large-scale structures. Single-mode optical fibers were deployed in three 12.8 m long steel and concrete composite floor specimens tested under mechanical or combined mechanical and fire loading. The concrete slab in each specimen was instrumented with five strain and temperature fiber optic sensors along the centerline of the slab to determine the variation of the measurands through the depth of the concrete. Two additional fiber optic temperature sensors were arranged in a zigzag pattern at mid-depth in the concrete to map the horizontal spatial temperature distribution across each slab. Pulse pre-pump Brillouin optical time domain analysis (PPP-BOTDA) was used to determine strains and temperatures at thousands of locations at time intervals of a few minutes. Comparisons with co-located strain gauges and theoretical calculations indicate good agreement in overall spatial distribution along the length of the beam tested at ambient temperature, while the fiber optic sensors additionally capture strain fluctuations associated with local geometric variations in the specimen. Strain measurements with the distributed fiber optic sensors at elevated temperatures were unsuccessful. Comparisons with co-located thermocouples show that while the increased spatial resolution provides new insights about temperature phenomena, challenges for local temperature measurements were encountered during this first attempt at application to large-scale specimens. [ABSTRACT FROM AUTHOR]

This paper presents a method to measure and visualize strains and cracks in high-performance fiber-reinforced concrete using distributed fiber optic sensors based on optical frequency domain reflectometry. Two beams were prepared using high-performance concrete with two types of fibers and post-tensioned with carbon-fiber-reinforced polymer (CFRP) tendons. The beams were instrumented with distributed sensors which were installed using two methods compatible with realistic construction. The beams were tested under four-point bending until failure. The distributed sensors measured strain distributions over the length of the beams in real time. The strain distributions are analyzed to detect, locate, trace, quantify, and visualize cracks during the processes of their initiation and propagation. The crack widths measured from the distributed sensors as well as a crack microscope are in a good agreement. This study is expected to promote distributed sensing technology for monitoring and control of construction and operation automation in new and existing structures. Unlabelled Image • Cracks are detected, located, quantified, and visualized using distributed fiber optic sensors. • Distributed fiber optic sensors can detect microcracks before they can be visually observed. • Practical methods are developed to install distributed sensors in new and existing structures. • Distributed cracks occur in high-performance fiber-reinforced concrete and are monitored. [ABSTRACT FROM AUTHOR]

[Display omitted] • The interfacial behavior between fused silica fiber and polymer coating is investigated. • A unified cohesive law is present to model shear hardening and softening interfaces. • A metaheuristic inverse analysis method is presented to calibrate the presented model. • A fully-distributed fiber optic sensing technology is used to evaluate the model. • Distributed fiber optic sensors with fiber-coating debonding are enabled to measure crack width. Distributed fiber optic sensors with protective packages have shown unique capabilities in measuring strain and crack distributions for structural health monitoring. However, the mechanics at the fiber-package interface remain unclear when debonding occurs. This paper investigates the interfacial mechanics for distributed fiber optic sensors undergoing debonding through mechanical analysis and metaheuristic-based inverse analysis. First, the governing equation of the interfacial mechanics is established and solved with assistance from metaheuristic-based inverse analysis on the interfacial properties of fiber optic cables. Then, experiments were conducted to validate the analysis results by measuring the strain distributions in distributed fiber optic sensors based on optical frequency domain reflectometry. The results showed that the proposed approach accurately quantified the interfacial mechanics, interfacial properties, strain transfer, and debonding behavior in distributed fiber optic sensors. This research advances the fundamental understandings of the sensing mechanisms of distributed fiber optic sensors undergoing inelastic behaviors for structural health monitoring. [ABSTRACT FROM AUTHOR]

• Distributed fiber optic sensors are used to detect buckling for thin-walled structures. • An effective and practical method is developed for shape reconstruction using strain fields. • Strain fields due to buckling are measured by fully distributed sensors with sub-mm resolution. • The proposed shape reconstruction method is compared with a computer-vision method. • Effects of spatial resolution and sensor deployment on shape reconstruction are discussed. This paper proposes to detect buckling and reconstruct three-dimensional deformations using distributed fiber optic sensors. The distributed sensors measured strain distributions with sub-millimeter resolutions based on optical frequency domain reflectometry in real time. Buckling was detected from high-resolution strain distributions measured from specimens. An effective and practical shape reconstruction approach was developed to derive nonsymmetrical deformations based on the strain distributions. The reconstructed shape was validated using a computer vision method that measured point cloud from the specimens. A parametric study was conducted to investigate the effects of the key sensing parameters such as the spatial resolution and sensor deployment scheme on the performance of the shape reconstruction approach, and used to optimize the resolution and deployment of distributed sensors. This research will advance the capability of buckling detection and shape reconstruction through distributed sensing for engineering structures under complex loading conditions. [ABSTRACT FROM AUTHOR]

Strain transfer phenomenon in distributed fiber optic sensors (DFOS) has shown significant effects on sensor survival and measurement of strain distributions as well as detection and quantification of cracks in health monitoring and condition assessment of civil infrastructure. This review aims to establish a holistic understanding on the strain transfer effect for measurement using DFOS. The reviewed contents cover the fundamental mechanisms, influencing factors, practical solutions, and applications of strain transfer models. Both forward and inverse strain transfer analysis of DFOS are elaborated. Challenges and opportunities of strain transfer analysis for DFOS are discussed. This review shows that the forward and inverse strain transfer analysis are capable of accurately determining the strain distributions and cracks in host structures subjected to arbitrary strain fields. The clarification of the strain transfer effect will facilitate the applications of fiber optic sensors in civil infrastructure. [Display omitted] • Strain transfer in fiber optic sensors plays critical roles in sensor survival and measurement. • The mechanisms, key factors, solutions, and applications of strain transfer models are reviewed. • The key factors include the sensor and host materials, sensor deployment, and temperature. • Forward and inverse strain transfer analysis determine strains and cracks in arbitrary fields. • It is promising to incorporate machine learning techniques for automatic strain transfer analysis. [ABSTRACT FROM AUTHOR]

This paper presents a distributed monitoring approach for detection, visualization, quantification, and warning for pipe corrosion using a single-mode telecommunication-grade fiber optic cable as a distributed sensor. The distributed sensor can be deployed on the surface of a pipe to measure corrosion along the pipe based on optical frequency domain reflectometry. To validate this distributed pipe corrosion monitoring approach, steel pipes were immersed in sodium chloride solution (concentration: 3.5 wt%) for corrosion tests. Strain distributions were measured and used to detect and map corrosion along the pipe. A meso-scale analytical model was developed to link the measured strains to the mass loss of pipe under corrosion. The effects of cable thickness, spatial resolution, and deployment pattern on corrosion assessment were investigated systematically. A threshold-based warning method is proposed based on the unique sensor data. [Display omitted] • A method is proposed for monitoring pipe corrosion using distributed fiber optic sensor. • Distributed sensor can be used to detect, locate, visualize, quantify, and warn pipe corrosion. • An analytical model is developed to quantify mass loss due to corrosion using the sensor data. • Effects of sensor deployment scheme and parameters on corrosion monitoring are investigated. • A threshold-based warning method is proposed based on the data for corrosion management. [ABSTRACT FROM AUTHOR]

• A distributed fiber optic sensor is used to monitor steel bar corrosion in concrete. • Degradation of steel-concrete interface is assessed using real-time in-situ sensor data. • Electrochemical and fiber optic measurements were simultaneous conducted with a novel setup. • A steel bar corrosion model is proposed to describe corrosion-induced volume expansion. • Unique distributed sensor data is used to quantify corrosion-induced volume expansion. Corrosion of steel bars compromises the safety and service life of reinforced concrete structures. This study develops an in-situ corrosion monitoring method for reinforced concrete with a distributed fiber optic sensor through experimentation. Reinforced concrete beams instrumented with distributed fiber optic sensors were prepared. A constant current was impressed to the beams immersed in a NaCl solution for accelerated corrosion. The distributed fiber optic sensor was deployed in a helix pattern on the steel bar to measure expansive strains generated by corrosion of the steel bar. The corrosion process of the steel bar was assessed in an electrochemical test. The strain measured from the sensor was utilized to evaluate the volume of the corrosion products surrounding the steel bars and predict the cracking of the concrete cover. To investigate the deterioration process of reinforced concrete, different levels of concrete cover thickness (28 mm, 35 mm, and 43 mm) and water-to-cement ratio (0.4, 0.5, and 0.6) were studied. The relationship between the mass loss of steel bars and the volume of corrosion products is established to provide a method for evaluating the effects of steel corrosion on the deterioration of reinforced concrete. [ABSTRACT FROM AUTHOR]