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Civil infrastructure, commonly referred to as the built environment, comprises essential systems such as buildings, roads, bridges, and dams that support societal governance, commerce, and economic development. However, these structures begin to deteriorate shortly after construction. In the United States, over 42% of bridge structures constructed post-1940 have been classified as structurally deficient, underscoring the urgent need for robust monitoring and maintenance systems. According to the recent ASCE Report Card, much of the U.S. infrastructure has exceeded its intended service life, requiring immediate attention in terms of rehabilitation, sustainability, and preservation to ensure public safety and functionality. Traditional methods such as visual inspections and non-destructive evaluation have served as the primary means of infrastructure assessment but often fail to detect critical subsurface or localized damage. This chapter provides a comprehensive review of the current state of civil infrastructure in the U.S. and selected developing regions, highlighting advancements in structural health monitoring systems. Emphasis is placed on the application and implementation of sensor-based instrumentation, including fiber optics, for real-time data collection, damage detection, and predictive analytics through data mining techniques. The chapter also explores future directions for enhancing infrastructure diagnostics and long-term performance monitoring.
School of Building Construction, Georgia Institute of Technology, Atlanta, USA
Jian Liu
School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, USA
Taofiq Mohammed
School of Building Construction, Georgia Institute of Technology, Atlanta, USA
*Address all correspondence to: ebenfanijo@gatech.edu
1. Introduction
Civil infrastructures sometimes referred to as the ‘built-environment” have been a fundamental part of modern societies for governance, commerce, economic and social growth [1, 2]. According to Sanghyeok et al., many of the civil structures constructed years ago are deteriorating and are under-performing [3]. Civil infrastructure such as buildings, highways, dams, tunnels bridges was constructed 50 or more years ago. The rate of infrastructure aging begins from the time of construction and the advert effect is felt many years after [1]. Over the previous decades, numerous reports have been documented on civil infrastructure deterioration in Korea, China, Europe, Asia, and North America. For instance, Asia countries have also experienced a decrease rate of infrastructure performance where 10% of her infrastructure is aging rapidly each year [3]. In the United States, 42% of the bridge structures built after the 1940s has been reported to be structurally deficient [3].
The quality and safety of human life are closely tied to the performance of civil infrastructure systems. As these systems age, their deterioration not only undermines national development efforts but also poses increasing social and economic risks [4, 5]. Cutter et al., 2003 highlight that infrastructure failures, particularly in transportation, bridges, water, and sewer systems, can escalate into disasters, leading to substantial loss of property and life [6]. Roadway conditions are a particularly pressing concern. Poorly maintained roads and highways contribute significantly to traffic accidents, surpassing many other causes of roadway fatalities [7, 8, 9, 10]. According to the American Society of Civil Engineers (ASCE), the overall state of U.S. infrastructure received a cumulative GPA of C in 2025, a modest improvement from the C- issued in 2021 [11, 12]. However, specific categories remain troubling: roads received a D and bridges a C. Nearly 45% of the nation’s 623,218 bridges are over 50 years old, with 7% classified as structurally deficient. Additionally, 20% of highway miles are reported to be in poor condition. The cost to rehabilitate and upgrade critical infrastructure is staggering: an estimated $151 billion for highways, $115 billion for bridges, and $165 billion for dams - each reflecting urgent needs as infrastructure elements rated D continue to degrade [11]. Table 1 provides a summary of ASCE ratings since 1988, revealing that the majority of U.S. civil infrastructure remains in poor to fair condition. This signals ongoing deterioration and a looming risk to both public safety and national resilience in the coming years.
Year
Bridges
Roads
Dams
Overall
1988
—
C+
—
C
1998
C-
D-
D
D
2001
C
D+
D
D+
2005
C
D
D+
D
2009
C
D-
D
D
2013
C+
D
D
D+
2017
C
D
D
D+
2021
C
D
D
C-
2025
C
D
D
C
Table 1.
ASCE ratings for bridges, roads and dams from 1988 [11, 12, 13].
The current state of civil infrastructure in the United States reflects a critical need for immediate investment and proactive management. Without intervention, the deteriorating condition of roads, bridges, water systems, and other key assets is projected to cost the U.S. economy over $3.9 trillion in lost GDP, $7 trillion in lost business sales, and 2.5 million job losses by 2025. This challenge is not unique to the United States. Other developed nations, including the United Kingdom, Australia, and Canada, also face similar issues with aging and underperforming infrastructure [11, 14, 15, 16, 17, 18, 19]. Global assessments consistently indicate that the overall condition of national infrastructure systems is below average and lacks long-term resilience strategies. Notably, as shown in Figure 1, only China and Singapore have received infrastructure scores above 0.65 on a normalized 1.0 scale [20].
Figure 1.
Infrastructure rating on scale 1–10 for each country [20].
These conditions highlight the pressing need for infrastructure rehabilitation, sustainable design, and life-extension practices, where many systems have already exceeded their intended service life. Safe and reliable civil infrastructure is essential not only for public well-being but also for economic security and resilience. One of the most promising strategies for reversing this decline is civil infrastructure monitoring. This proactive approach involves the integration of sensing, diagnostics, and analytics during and after construction, allowing for early detection of defects, streamlined maintenance, and lifecycle cost assessments. Such monitoring not only improves safety and performance but also reduces long-term costs and extends infrastructure service life. As a result, infrastructure health monitoring has become a central focus in civil engineering research and practice, underpinning efforts to modernize national infrastructure systems. Thus, this chapter provides a comprehensive review of the current state of civil infrastructure in the U.S. and selected developing regions, highlighting advancements in structural health monitoring systems. Emphasis is placed on the application and implementation of sensor-based instrumentation, including fiber optics, for real-time data collection, damage detection, and predictive analytics through data mining techniques. The chapter also explores future directions for enhancing infrastructure diagnostics and long-term performance monitoring.
2. The rising importance of civil Infrastructure monitoring
In recent years, civil infrastructure monitoring has gained significant attention in both the civil engineering community and at the national policy level. Many U.S. civil structures have exceeded their intended service life, prompting urgent needs to improve their sustainability, resilience, and maintenance strategies [21, 22]. Additionally, increasing exposure to extreme events, such as earthquakes, hurricanes, and tornadoes, has underscored the vulnerability of aging infrastructure and the need for tools to evaluate structural performance and recovery capacity [23, 24]. Two main approaches to structural monitoring are widely recognized: local and global monitoring. Local monitoring is designed to detect small-scale defects, such as micro-cracks, and often employs visual inspections or non-destructive evaluation (NDE) techniques. Global methods, by contrast, detect larger, system-level damage but may miss early-stage defects that compromise long-term integrity [23, 25]. A stark example is the I-35 W bridge collapse in Minneapolis. Although inspected regularly, structural weaknesses in hard-to-access gusset plates went undetected. Corrosion and damage levels were underestimated due to the limits of visual inspection, ultimately contributing to the catastrophic failure [23, 26, 27]. This event highlighted the need to move beyond visual assessments.
Despite its foundational role, traditional NDE, using techniques like X-ray imaging, ultrasonics, guided wave testing, and acoustic emission, has notable limitations. These include high labor demands, limited access to hidden components, safety risks, and difficulty detecting subsurface flaws [28, 29, 30]. In response, Structural Health Monitoring (SHM) systems have emerged as a transformative solution. SHM uses distributed networks of advanced sensors, often based on fiber optics, to continuously evaluate a structure’s real-time condition and remaining service life. These systems enable proactive maintenance, reduce risk, and enhance infrastructure safety and longevity. The evolution and implementation of SHM technologies will be explored in the following section.
SHM has become essential for aging infrastructure and for assessing damage from external events like earthquakes and hurricanes. SHM systems provide ongoing updates on structural condition, continuous monitoring and damage diagnostics, and performance under extreme loading, enabling extended service life and reduced life-cycle costs [31, 32, 33, 34, 35, 36, 37, 38, 39]. SHM provides a systematic framework that collects, transmits, and interprets data through a network of sensors. These systems assess real-time deterioration, estimate remaining service life, and inform maintenance decisions. SHM thus supports condition-based monitoring, replacing less effective time-based maintenance schedules [40, 41, 42]. SHM can be seen as an advanced tool that both detects a “patient” sickness, finds the precise location of illness, and the cause as well as the effect of the symptoms [43]. In this scenario, SHM is the acquisition of data, transmitting and interrogating this set of data, and then decide (decision making) from them. The critical element of this system can be categories into people, information, science and technology, and deployment. A detailed version of these SHM elements is presented in Table 2. In summary, a typical SHM system has a sensor system, a data processing system (which includes acquiring, transmission, interrogating, and storing data) and a health evaluation system (diagnostic algorithms and information management). This element made it suitable to be regarded as an optimal design in the future. Local and global health techniques are only used for monitoring structure damage.
In this chapter an overview of structural health monitoring in terms of its application to civil infrastructure together with data analysis which make-up for complete monitoring design. This entails the fundamental knowledge, technology needs, and the challenges of SHM applications. A highlight was also given to data analysis and interpretation which is an essential aspect of implementing SHM. These are debated in terms of system or structure characterization, sensing technology, data quality, presentation, and making quality decisions.
4.1 SHM application and data analysis
Establishing clear monitoring objectives is essential to effective SHM system design. While universally accepted definitions of “health” and “performance” remain elusive, significant guidance is available from the ASCE SEI Technical Committee on performance-based design [44, 45]. In bridge assessments, condition indices such as displacement and frequency shifts are widely used. For pavements, the Present Serviceability Index (PSI) is common, rating conditions from 5 (excellent) to 0 (poor) [46, 47, 48]. Performance is evaluated based on limit-state criteria, where exceedance of predefined thresholds indicates functional failure. The reliability index is often used as a performance metric and can be derived from SHM data and simulation models [49, 50]. Thus, SHM not only quantifies structural condition (health) but also assesses structural integrity under service loads and extreme events (performance) [43, 51]. SHM has broad applications beyond the U.S.—notably in the U.K., China, and Korea—particularly for bridges, highways, and other transportation infrastructure. Its implementation extends to buildings, dams, water systems, and aerospace structures. Key monitoring parameters include deformation geometry, intrinsic and extrinsic forces, material degradation, and stress distribution across critical regions.
4.2 SHM application to new infrastructures
The application of SHM in new civil infrastructure serves as a proactive tool for capturing critical data during the fabrication and construction phases [52]. It enables the measurement of intrinsic forces, deformations, and stress distributions that may arise due to complex structural geometries or hazardous construction environments. Integrating SHM into the design phase allows anticipated construction-induced effects—such as stress, displacement, or distortion—to be accounted for from the outset [53]. This approach enhances construction safety, especially under high-risk conditions like hurricanes, seismic activity, or typhoons, which may occur during the construction lifecycle. For instance, several bridges in East Asia have incorporated SHM systems specifically to monitor seismic and typhoon-related stresses. Moreover, SHM during early stages helps identify errors or inconsistencies in fabrication and construction drawings, preventing delays and costly rework. By continuously monitoring structural response, SHM offers immediate feedback that supports informed decision-making and quality assurance. A notable example is the Incheon Bridge in South Korea, where SHM was embedded from the design stage to monitor performance throughout construction and service life.
4.3 SHM application to new infrastructures
Just as SHM enhances new construction, it plays a critical role in managing aging or deteriorating infrastructure. For structures that have surpassed their design life, SHM enables the identification of underlying causes of distress—such as excessive deflection, cracking, vibration, corrosion, or alkali-silica reaction (ASR), supporting informed decisions for maintenance or preservation. By providing continuous, real-time data on mechanical (e.g., deflection, geometry changes, stress) and durability-related issues (e.g., corrosion, sulfate attack), SHM allows engineers to: Diagnose the root cause of performance degradation, Design targeted repair or retrofit strategies that maintain structural integrity and Extend service life without unnecessary replacement [53].
In cases where a structure has reached the end of its fatigue life or exhibits poor system reliability, SHM data may prompt a retrofit. This enhances reliability and safety without compromising the original structure. Moreover, SHM is valuable when applied statistically across a population of similar infrastructure [54]. For example, reinforced concrete decks supported by steel girders can be grouped based on shared attributes—load rating, failure mode, or system reliability. Monitoring a representative sample enables extrapolation of results to the broader group, optimizing decision-making on a scale and minimizing monitoring costs.
4.4 Planning and implementation of SHM systems
A successful SHM application begins with clearly defined objectives, essential requirements, and realistic expectations. Establishing these foundations enables engineers to address specific structural questions such as load capacity, stress distribution, displacement behavior, and uncertainties related to construction processes, structural condition, or remaining service life. When well-articulated, such goals can help reveal early damage, support hazard and accident evaluations, inform efficient maintenance strategies, and provide an objective assessment of present and future conditions while also addressing structural security concerns [52].
Once objectives are set, several critical components guide the implementation of SHM. Structural system characterization is the first step, involving a clear understanding of the infrastructure and its behavior. This includes determining the spatial scope of monitoring—whether localized, region-specific, or distributed across the entire structure—as well as specifying the type of instrumentation and duration of monitoring [52]. For example, a major bridge may require detailed, long-term monitoring using multiple sensor nodes and controlled loading scenarios due to its complexity and scale.
Measurement identification is another essential task. It requires determining which parameters best align with project goals. These may include load-induced responses, serviceability indicators, construction impacts, environmental effects, or operational conditions. Typical measurements involve force, strain, displacement, rotation, temperature, vibration, and traffic volume. It is also important to evaluate the interactions between measured variables and ensure they are captured at the most informative physical locations.
Selecting appropriate sensing and data acquisition systems follows next. This includes specifying sensor types, installation procedures, calibration requirements, data transmission environments, and signal quality controls. A robust sensor plan ensures reliable data collection under both short-term and long-term monitoring conditions. Proper configuration, calibration, and validation protocols must also be implemented to ensure the system performs as intended throughout the project duration.
Data quality assurance and processing depend heavily on the robustness of the sensing infrastructure and the volume of collected data. Effective strategies must be in place at each step of the signal pathway—from sensor calibration to installation verification and signal validation—to minimize error propagation and reduce measurement uncertainty. These multi-level checks ensure data integrity and improve the reliability of conclusions drawn from the SHM system.
Finally, the collected data must be effectively presented and used to support decision-making. The SHM system should be able to interpret incoming signals, identify when thresholds are exceeded, compare results against predefined criteria, and, ideally, support automated or semi-automated decision execution. This data-to-decision process ensures the monitoring system functions not just as a passive observer but as an active tool for infrastructure management.
4.5 SHM data analysis
Numerous algorithms have been developed for analyzing and interpreting SHM data [25]. Effective SHM implementation requires integrating one or more of these methods across four key data analysis levels. These levels are structured to progressively extract meaningful insights from raw sensor outputs to actionable future performance predictions. Level 1 involves raw data indicators—direct readings from sensors without processing. Level 2, the identification stage, employs analytical methods to detect significant changes, such as damage or deterioration, over time. Level 3 incorporates detailed data analysis to localize and quantify these changes, yielding high-confidence assessments. Level 4 extends to prediction, using historical patterns to forecast future structural performance. Common techniques in SHM data analysis include statistical pattern recognition, parameter estimation via model updating, reliability and risk analysis, Bayesian inference, and computational modeling [25, 55, 56].
4.5.1 Statistical pattern recognition
This technique is adept at managing large datasets and identifying temporal changes. It typically includes three stages: data collection, feature extraction, and classification [57, 58, 59, 60]. Features, quantitative descriptors, are then analyzed using supervised or unsupervised learning algorithms (Figure 2). Supervised learning is applied when labeled data from damaged and undamaged states are available, using classification or regression models. In contrast, unsupervised learning handles data from unknown states, using outlier detection or clustering. Sohn et al. summarized the process into operational evaluation, data acquisition, feature extraction, and statistical modeling [61]. Metrics like natural frequency, AR model coefficients, and MAC are commonly used.
Figure 2.
Supervised and Unsupervised learning Approach for SHM.
4.5.2 Parameter estimation with sensor fusion and model updating
At Level 3 analysis, sensor fusion and analytical model updating improve data reliability. This method of data analysis has been adopted by many researchers [62, 63, 64, 65]. This approach estimates structural properties such as stiffness and Young’s modules using optimization techniques or empirical data. The result serves as a reference model for further assessment. Studies by Francoforte (2007) and Sanayei et al. (2006), demonstrated the value of this method in producing consistent and reliable structural insights [64, 66].
4.5.3 Reliability and risk analysis
Reliability analysis, although less explored in SHM, it plays a vital role in predicting failure probabilities and informing lifecycle management [59, 67]. It uses SHM data to assess whether structures remain within defined safety margins. Limit-state functions, constructed using stress and capacity parameters, distinguish between healthy and failed states. The reliability index (beta) quantifies the probability of failure and is calculated from the mean and standard deviation of structural parameters. Sensor-based models allow real-time monitoring and trigger alerts when reliability thresholds are breached.
4.5.4 Prediction and Bayesian analysis
Bayesian inference offers a powerful probabilistic framework to model uncertainty and predict future performance. It updates predictions as new SHM data becomes available, supporting real-time prognosis. Bayesian networks are especially useful in assessing current conditions and projecting future degradation in structures like bridges, using prior and sampling distributions to generate posterior outcomes.
In bridges, the technique is employed to determine the critical current state (structural performance and the structural health) from newly available data. In making quality decisions, good prediction (prognostic evaluation) is necessary, with the Bayesian model and sensor-based data this prognostic evaluation can be estimated. This is an invaluable tool for the management of structures such as bridges, dams, buildings, etc. [38]. A typical Bayesian model is expressed below.
gθ|x=fx|θgθ∫fx|θfgθdθE1
where g(θ|x) = posterior distribution (prognosis).
f(x|θ) is the sampling distribution.
g(θ) is the prior distribution, θ is the continuous parameter vector and x is sample data.
4.5.5 Computational analysis, modeling, and simulation
Computer vision and simulation have revolutionized SHM by enabling image-based analysis of structural behavior. Streamed video and sensor data are integrated to assess real-time parameters such as traffic loads and stress distribution. By correlating visual data with simulation models, engineers can detect and track deterioration, facilitating timely interventions. Applications by [68] have demonstrated how such systems can automate maintenance scheduling and failure prevention through continuous visual monitoring. A typical integration of computer vision into SHM is shown in Figure 3.
Figure 3.
Integration of computational vision into SHM data [68].
SHM systems were introduced to replace traditional visual inspections, offering real-time, continuous assessment of structural integrity, damage, and remaining service life through sensor networks. Conventional SHM setups, relying on wired sensors such as accelerometers and vibrators, often suffer from high costs and logistical challenges, particularly in large infrastructure systems. For instance, monitoring a multi-story building can cost $5000 per sensing channel [69], while installing 84 wired accelerometers on the Bill Emerson Memorial Bridge costs approximately $15,000 per channel [70]. Similarly, installing 350 sensors on the Tsing Ma Bridge in Hong Kong reached $8 million [67]. Maintenance costs also escalate over time, further limiting scalability.
5.1 Wireless sensor networks (WSN)
To address these limitations, WSNs have emerged as a cost-effective and scalable solution. Enabled by advances in micro-electro-mechanical system MEMS, integrated circuits, and wireless communication, WSNs simplify installation, facilitate flexible sensor placement, and streamline data acquisition [71]. A WSN system typically comprises software for data collection and processing, and hardware including sensors and central servers (Figure 4) [72]. While performance depends on sensor placement, environmental conditions, and communication bandwidth, WSNs significantly reduce SHM implementation costs [73].
Figure 4.
The subsystem of wireless sensors (adopted from Ref. [72]).
Smart wireless sensors further enhance SHM capabilities. These sensors integrate onboard microprocessors, wireless communication, sensing modules, and low-power batteries to enable self-diagnostics and in-situ processing [74]. Their decision-making capacity improves data quality and enables localized damage detection. Over the past two decades, smart sensors have been widely deployed in civil infrastructure applications [75, 76, 77, 78, 79, 80].
The iMote2, developed at the University of California-Berkeley and commercialized by Crossbow, is among the most advanced SHM sensor platforms. It offers superior storage, reduced synchronization errors, and middleware support for large-scale deployments [81]. WSN platforms such as MICA2, WiMMS, RIMS, Husky, and DuraNode have also been implemented successfully [82]. A notable full-scale deployment occurred on the second Jindo Bridge in South Korea, where 71 iMote2-based WSN nodes provided 427 sensing channels to monitor bridge components [36, 83]. Similarly, the Meriden Bridge project demonstrated the integration of 5 iMote2 WSNs with 38 wired sensors for comprehensive health monitoring [84].
5.2 Application of wireless sensor
Wireless sensors are not conventional standalone devices but rather integrated data acquisition nodes comprising various structural sensing elements, onboard microprocessors, and wireless communication modules. These nodes typically incorporate accelerometers, strain gauges, seismometers, velocity gauges, displacement transducers, and inclinometers to assess the physical state of structures [72]. Their application within smart wireless sensor networks varies depending on structural type and required performance metrics.
A prime example is the Akashi Kaikyo Bridge in Japan, the world’s longest suspension bridge, which employs a comprehensive sensor suite—including accelerometers, seismometers, velocity and displacement gauges, thermometers, GPS units, and tuned mass dampers—to monitor its condition [85]. Sensors are deployed at critical structural locations, where they operate under command from a central server. After synchronization, each sensor node collects and processes structural data, which are transmitted back to the server for analysis. These data are then used to assess current structural performance and predict the remaining service life. The schematic workflow of wireless sensor application is illustrated in Figure 5.
Figure 5.
The schematic approach of wireless sensors application.
5.2.1 Accelerometer
Accelerometers are among the most widely used sensors in SHM due to their cost-effectiveness, ease of installation, robustness, and suitability for both local and global monitoring applications. Their high sampling frequency capabilities make them ideal for capturing dynamic responses of structures [86]. For example, sampling rates above 1000 Hz enable precise dynamic analysis [87], provided the Nyquist theorem is satisfied: Fs ≥ 2Fo, where Fs is the sampling frequency and Fo is the monitored frequency [88]. Accelerometers are largely unaffected by external environmental conditions, offering reliable and accurate structural performance data [89]. The ETNA high-dynamic-range accelerometer is a commonly used model, featuring a 108 dB dynamic range, 18-bit resolution, 0.5 msec timing accuracy, and integrated alerting and diagnostics systems. Wireless MEMS-based accelerometers have also gained popularity, categorized as single-hop or multi-hop systems [90]. Although accelerometers provide valuable data, integrating them with other systems like GPS can mitigate minor errors introduced during numerical integration [87]. For instance, 84 accelerometer channels, each costing approximately $15,000, were installed on the Bill Emerson Memorial Bridge for comprehensive monitoring [83]. Accelerometers have also been widely used in earthquake and wind-load monitoring, providing critical insights into structural responses [91].
5.2.2 Global positioning system (GPS)
GPS receiver is also implemented in SHM applications for civil engineering structures, making GOS an advanced tool for monitoring system [92, 93, 94, 95, 96, 97]. GPS technology enhances SHM by enabling direct displacement measurements and tracking dynamic, static, and semi-static movements without requiring acceleration integration [92]. GPS sensors provide autonomous operation, high accuracy, and do not require line-of-sight between receivers. Sampling rates typically range from 50 Hz to 100 Hz [98].
Geodetic-grade GPS units have been successfully integrated with accelerometers in SHM studies [92, 94, 95, 96, 97]. Researchers have also explored embedding these sensors into smartphones for cost-effective monitoring [99]. However, GPS accuracy can be affected by atmospheric interference and multipath effects. Signal processing techniques can mitigate these issues.
5.2.3 Smartphones
Modern smartphones, equipped with sensors such as gyroscopes, accelerometers, and magnetometers, are increasingly used in SHM due to their portability, affordability, and ease of use [99]. Studies have validated the embedded accelerometers for structural monitoring, demonstrating performance comparable to commercial sensors [100]. Despite their potential, challenges such as structural attachment, data preprocessing, and battery limitations must be addressed [99, 101]. For example, the Samsung Galaxy was employed as an SHM device, utilizing its onboard LSM6DSL accelerometer and an analyzer app to collect time-series data [99]. Smartphones have also been applied to bridge monitoring, cable force measurement, and seismic response validation [102, 103].
5.2.4 Acoustic emission (AE)
Among non-destructive testing methods, acoustic emission (AE) is a prominent technique for real-time monitoring of highway and bridge structures. Unlike other NDT methods used before or after loading, AE operates during loading by detecting stress waves generated from internal structural changes [104]. AE systems consist of a stress-wave source, sensor (transducer), and data processing unit. Emissions are categorized as primary (from the structure) or secondary (external noise) sources [105, 106]. Wireless AE systems, enhanced by MEMS technology, reduce installation costs and improve data acquisition [107]. AE has been successfully used for laboratory tests and in-situ evaluations of concrete and steel structures [108]. AE-based systems have also been applied to monitor wire breaks in bridge cables [109].
5.2.5 Linear variable displacement transducer (LVDT)
LVDTs are traditional sensors used for direct displacement measurements and are often mounted between fixed points on structures to track relative motion [110]. They are well-suited for long-term monitoring of structural deformation and crack opening due to their excellent stability [111]. This is the reason while they are suitable for measuring long-term degradation of structure crack opening displacement LVDTs have been used to validate other displacement monitoring methods [112]. A notable example is the Kishwaukee River Bridge, where seven LVDT sensors have been tracking shear crack displacement since 2001 [113].
Optical Fiber Sensors (OFS) are increasingly adopted in SHM due to their high precision, electromagnetic immunity, corrosion resistance, durability, and compatibility with harsh environments. OFS is known to be one fast-developing technology and most assuring research area in both short-term and long-term structural health monitoring of civil engineering infrastructures due to their outstanding features of stability, durability, compatibility, and insensitivity to external electromagnetic agitations and corrosion [114, 115, 116, 117, 118, 119]. Unlike traditional sensors (e.g., accelerometers), OFS offers distributed, quasi-distributed, and point sensing over long distances, making them well-suited for global strain mapping and damage detection in civil infrastructures [120]. While conventional sensors provide discrete data at limited locations, OFS offers thousands of sensing points along a single fiber, allowing continuous, distributed measurements that capture the comprehensive structural state. Through light scattering techniques, OFS can measure temperature, strain, and vibration across large-scale infrastructures in real time. Extensive research has documented the effectiveness and versatility of OFS in both the general field of sensing [121, 122, 123, 124] and their specific applications in civil engineering [37, 38, 125, 126, 127]. These capabilities make OFS a superior choice for structural damage detection and comprehensive infrastructure health assessment.
OFS operates by converting structural changes—such as strain or temperature—into modulated light signals within optical fibers made of silica or polymers. An OFS system is a typical cylindrical structure made of 6 typical components which are light transmitters, a receiver, an optical fiber (the central core part), a modulator, and a signal processing unit. The optical fiber which is the core element in the OFS has a diameter ranging between 4 and 600 mm [128]. It is either made from silica glass or polymer materials, hence possessing a uniform reflective index that receives and transfers light from a source to its modulator component. For instance, a change in temperature or strain in a structure creates a respective expansion and contraction of the optical fiber. This change in length (contraction and expansion) of optical fiber is sent to the modulator which in turn controls the light and reflects it to the analytical or processing unit where the derivation of the current performance of the structure is produced [129].
Based on their applications, sensing technology, modulations, measurement points, etc. OFS can be classified into different groups. A brief discussion is presented for the three commonly adopted sensing-based devices which are grating-based sensors, Interferometric-based sensors, and the Distributed-based sensors are presented in the following section [130, 131].
6.1 Gratin-based sensor
One of the most adopted Gratin-based sensors is the Fiber Bragg Grating (FBG) Sensor. This system works in a way that the optical fiber of this type of FOS has various reflective indices. It operates according to the Bragg’s law (white light beam written on the beams), at a wavelength, the Bragg wavelength is reflected when a beam of light from sources passes through the grating. This reflected Bragg wavelength (expressed in the equation below) is correlated to the grating period.
λB=2neffΛ,E2
Where neff is the effective refraction wavelength of refraction and Λ is the gating period.
In the presence of a change in strain and temperature from measurement, this 𝜆 shift and Λ is changed. Then the overall Bragg wavelength can be expressed as’.
ΔλB=λBα+ξΔT+1−peΔε.E3
Where Δ𝜀 is the change in strain, ΔT is the change in temperature, 𝛼 is the thermal expansion coefficient, 𝜉 is the coefficient of strain-optic, and pe is the thermo-optic coefficient.
This system is well known and widely employed for civil engineering structural monitoring providing structural conditions using a varied refractive index [132, 133, 134, 135, 136]. A measurement principle of FBG sensor on civil structures is presented in Figure 6. Another form of Gratin-Based sensors are Long Period Grating Sensors and Tilted Fiber Bragg Grating Sensors.
Figure 6.
FRG sensor measurement principle (Image recreated from Ref. [37]).
6.2 Interferometric sensors
This form of optic sensor has been adopted and the most common type on Interferometric is the Extrinsic Fabry-Perot Interferometric sensors (EFPI). The EFPI sensor is made of two optical sensors and a reflective fiber (also with hollow-core fiber). The former is performing as the input and output path in which light passes through them to the sensing part, then to the modulation while the former creates an air cavity called a Fabry-Perot cavity. During application, reflections are generated at both fibers’ ends. R1 and R2 are the reference and sensing reflection respectively. The reference reflection, R1 is a strain or temperature-dependent while R2 depends on the cavity length. When R1 and R2 interfere, a sinusoidal (sine curve) signal is generated as the output. Based on the output signals, and the modulation of cavity length (Δl, the EFPI can be employed to measure an applied strain or temperature (perturbation). The expression below shows an example of measured strain on a structure. Another form of this sensor system is SOFO, Mach-Zehnder, Sagnac, and Intensity/Micro bending Interferometric Sensor.
ε=ΔlairgapL.E4
6.3 Distributed fiber optic sensor (DFOS)
Compared to other forms of FOS, DFOS is cost-effective and can continuously monitor structure in real-time. This is sole because it only requires a single connection cable along with the optical fiber which measures the variation of one-dimensional structural in a distributed way. In the DOFS application, the perturbation changes (such as stain and temperature changes) are transferred to the optical fiber embedded inside or surface-bonded to the structure and modulated by the scattered (light and optical medium interaction signal in the fiber. The signal from this modulation is measured and the DFOS is achieved [120]. DOGS depends on different principles and techniques, in which the most adopted techniques form practice is the optical time-domain reflectometry (OTDR). OTDR simply works by detecting interacted position according to the propagation time as the light pulse (with a specific wavelength) propagates along with the optical fiber [137, 138]. The Brillouin optical time-domain reflectometer (BOTDR) and Raman are the two common DFOS based on Brillouin and Raman scattering.
6.4 Applications of OFS in civil structures
6.4.1 Bridges highway
One of the essential structures in transportation engineering is bridges and the structural monitoring of bridges has been a major concern around the world to increase their serviceability. The ability to continually monitor the operational loadings, structural performance, damage detection, environmental effect on bridge structure in real-time could be achieved by using optical fiber sensing technology. The FOS has served as an advance tool for monitoring various structural components (such as cables, deck, girders, piles, piers, abutment, etc.) and distress (such as stain, temperature, stress, deflection, corrosion, cracks, etc.) during the entire life cycle of the bridge (construction, operation, maintenance). From the literature, the application of FOS to monitor and estimate the structural condition and performance condition assessment [37]. The application of optical fiber sensors in the integrated bridge monitoring system during construction and post-construction; in the rehabilitated bridge; also, in the monitoring of bridge cables and suspenders have been deployed across the world as shown in Table 3.
Application of FOS
Country
Application of Fiber Optic Sensor (FOS)
References
The integrated bridge monitoring system
USA
Incorporation of remote monitoring approach based on FOS during construction stage for structural performance evaluation condition assessment of bridge in Florida
The composite bridge was monitored using FBG sensors. Deployed FOS for monitoring strain and structural assessment of a 50-year-old concrete bridge Application of FBG-based sensor on an arch bridge for strain and full temperature monitoring
Structural stability and reliability of FBG-based sensing through monitoring during the construction of a stay cable bridge for half a year was investigated.
Application of various FOS has been employed on aged, deteriorated bridges for maintenance evaluation and repairs. Application of FBG-based sensing system on 100 years old steel bridge
In China, The FOS was applied to evaluate various fatigue damaged bridge monitored a cable force using distributed BOTDA sensing techniques and high-precision FBG sensors. Li et al. monitored a glass fiber reinforced polymer (GFRP) bar by employing a smart stay cable built with FBG-based strain and temperature sensors.
A review of FOS applications in bridge structures.
6.4.2 Other form of civil infrastructures
Other essential structures in transportation engineering where the fiber optical sensing is utilized are in building stricture, tunnel and pipeline; railway structures, and geotechnical and foundations structures. The ability to continually monitor the operational loadings, structural performance, damage detection, environmental effect on these civil engineering infrastructures in real-time could be achieved by using optical fiber sensing technology. Similar to bridge application, the FOS also serves as an advanced tool for monitoring various structural components during the entire life cycle of the structure (construction, operation, maintenance). Table 4 below illustrates the application of FOS on other structures.
Application of FOS
Application of Fiber Optic Sensor (FOS)
References
Building For safety condition monitoring (both during construction, or in-service)
Bastianini et al. monitored the strain and cracks distresses around historical building structures by employing embedded optical fiber Brillourin sensors. Through the employment of FBG based biaxial accelerometer, Antumes et al., monitored a reinforced concrete water reservoir and a slender metallic telecommunication tower. Ni et al., deployed the FBG sensor to monitor the strain and temperature changes of the Canton Tower in China. During construction, Li et al., also performed FBG monitoring techniques on an 18-floor building. By monitoring the temperature and strain at three stages of each floor construction (i.e. before concrete pouring, during concrete pouring and after pouring)
Ye et al., deployed the use of FBG sensors to monitor the safety of a tunnel construction (i.e the temperature and strain) Metje et al., monitor the displacement/deflection (both lateral and rotational movement) of a tunnel lining using the new fiber optical sensing. Also, during backfilling and traffic-operating periods, Li et al., used a differential FBG strain sensor for monitoring the tunnel stability For pipelines, Glisic and Yao develop a real-time assessment of buried pipeline health conditions using distributed FOS. With the right support of the Brillouin spectrum strengthened with carbon-coated fibers and excellent communication fibers, Zhang et al., determined the locations of buckling and also predicted a progressive sequence of a pipe buckling.
Recently, the use of FOS as now being blooming in the area of railway structures. Yan et al., monitored various high-speed railway systems using strain and axle-counting measurement of the FBG-based method. In the Hong Kong transit railway, Wei et al., employed the FBG sensors on rail tracks. This assisted in generating an effective condition index reflecting the wheel situation and produced a track strains on rail-wheel contact. The FBG-based was also employed to monitor the Spanish high-speed line by Filograno et al. This examined the train wheels, dynamic loads, strain train identification, axle counting, speed, and acceleration detection. Bocciolone et al. also employed the FBG sensor to monitor the contact force and the vertical acceleration of the pantograph head in the underground pantograph-catenary system. Boffi et al. also used this method. Other authors that deployed the optic sensor are Pimentel et al., monitoring the train speed, acceleration, and weight distribution in Portugal.
In the geotechnical and soil stability field, Pei et al. monitored the lateral displacement of slopes in China using the FBG-based method. Lu et al. also employed the BOTDR-based optical fiber sensing method to monitor the stress and strain in precast pile.
The monitoring and maintenance of civil infrastructure have emerged as critical topics in both the engineering community and national discourse, particularly in light of aging infrastructure across the United States. As reported by ASCE, many infrastructure systems have exceeded their intended service life, underscoring the urgent need for sustainable rehabilitation, resilience enhancement, and long-term maintenance. Ensuring the safety and reliability of civil structures is essential for public well-being. To address the limitations of conventional visual inspections, SHM has been widely adopted. This chapter has provided a comprehensive overview of SHM technologies, with an emphasis on their application to both existing and newly constructed infrastructure. Special attention was given to the integration of wireless smart sensors as a cost-effective alternative to traditional wired systems. Technologies such as accelerometers, GPS, LVDTs, and especially OFS have demonstrated significant promise in capturing the real-time behavior and performance of bridges, highways, tunnels, buildings, and geotechnical systems. The chapter also highlighted the advantages of OFS including their capacity for distributed, high-resolution measurements and explored their current application in advancing infrastructure diagnostics and service life extension. Collectively, these developments signal a shift toward intelligent, data-driven infrastructure management.
Despite the growing adoption of wireless sensor networks (WSNs), several technological limitations remain—particularly for long-term and large-scale SHM applications. These include challenges related to power supply longevity, data transmission reliability, bandwidth constraints, and large-volume data processing and storage. Additionally, the bespoke design of wireless hardware and software often demands significant expertise, making widespread implementation a challenge for many transportation agencies. To overcome these barriers, future research should focus on developing robust, scalable wireless sensing systems tailored for long-duration, multi-parameter monitoring. Continued efforts are also needed to integrate SHM systems within the broader framework of the Internet of Things (IoT), addressing key concerns such as cybersecurity, energy efficiency, edge/cloud computing integration, and real-time visualization. Another transformative avenue is the application of artificial intelligence (AI) in SHM. AI-driven platforms can enhance data interpretation, automate anomaly detection, and support predictive maintenance. Advanced machine learning models, agent-based systems, and intelligent data visualization tools will be essential in translating complex sensor data into actionable insights. The convergence of AI, IoT, and advanced sensing technologies holds immense potential to enable smart, resilient, and adaptive infrastructure systems. In summary, realizing the full potential of SHM for next-generation infrastructure will require interdisciplinary collaboration among academia, industry, and government agencies. With continued innovation and strategic investment, SHM can play a pivotal role in ensuring the sustainability and safety of global civil infrastructure networks.
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Written By
Ebenezer Fanijo, Jian Liu and Taofiq Mohammed
Submitted: 18 July 2025Reviewed: 04 August 2025Published: 03 October 2025
Open access peer-reviewed chapter
