Structural Health Monitoring Strategies Research Articles

Vibration monitoring of masonry bridges to assess damage under changing temperature

Structural Health Monitoring (SHM) is of utmost importance for the preservation and safe operation of historical arch bridges. This paper presents the development of a SHM strategy aimed at the model-based damage assessment of masonry bridges using frequency data. Structural damage induces natural frequency changes that are strictly related to the damage location. Consequently, a numerical model capable of reproducing the intact dynamic characteristics should allow to simulate damage scenarios, including the observed one, with the anomaly localisation being performed through the similarity between the experimentally detected frequency changes and the numerically simulated ones. The proposed methodology is based on the availability of an appropriate knowledge of the investigated structure, allowing to define a Finite Element (FE) model that accurately reproduces the system dynamic characteristics. Hence, the SHM strategy involves: (a) the use of the calibrated model to simulate different damage scenarios, so that a Damage Location Reference Matrix (DLRM) is defined through the associated frequency shifts; (b) the damage detection through statistical pattern recognition of vibration data; (c) the damage localisation through the comparison between the identified frequency changes and those defined in the DLRM matrix. Pseudo-experimental monitoring data, referring to a historical masonry viaduct, were generated and used to exemplify the reliability and accuracy of the developed algorithms in detecting and localizing damage.

Acoustic emission-based delamination investigation in drilled GFRP under mixed tensile and bending cyclic loading

The drilling process can cause various defects in glass fiber-reinforced polymer (GFRP), such as delamination, which ultimately leads to damage progression, especially under fatigue loading. Therefore, the current study aims to investigate the progression of failure in drilled composites subjected to cyclic bending and tensile loads using acoustic emission (AE), image processing, and mechanical behavior monitoring. The results show tyhat the fatigue diagram consists of three stages associated with resistance of fibers and matrix against delamination propagation, stable delamination progression, and ultimate spread of failure which are accurately identified by AE and mechanical methods. Additionally, the pattern and procedure of all fatigue diagrams under all loading conditions were similar and independent of loading parameters. Results from this study provide a strategy for real-time structural health monitoring (SHM) of delamination propagation in GFRPs under multi-axial fatigue loading.

Smart and autonomous monitoring of cracking in concrete structures with PZT sensor array-based hybrid sensing

Detecting microcracks in concrete structures is crucial for maintaining structural integrity; however, current methods often lack the precision, objectivity, and sensitivity needed for early-stage detection and monitoring. This study introduces a pioneering hybrid sensing approach that combines coherent and incoherent ultrasonic wave fields with surface-mounted PZT-based sensors, significantly advancing the state-of-the-art. The sensors are positioned at specified locations on a notched concrete beam and operated in the actuator-receiver (AR) mode, allowing for the manipulation of fracture width at the notch to achieve distinct degrees of damage. By evaluating the ultrasonic waves received from various AR configurations, we developed robust damage indices: the attenuation factor and diffusivity ratio. These indices provide objective metrics for evaluating the degree of damage, addressing the limitations of existing techniques. Key results demonstrate that the attenuation factor can detect crack widths as small as 10 µm at a 120 kHz frequency, surpassing traditional methods. Nevertheless, its sensitivity diminishes if the crack width exceeds 100 µm. Additionally, the diffusivity ratio remains sensitive to cracks beyond 200 µm and shows a substantial 40 % decrease even for non-visible fractures, offering a comprehensive assessment of fracture propagation. These advancements not only enhance early detection and monitoring precision but also offer a deeper understanding of fracture mechanics, paving the way for improved structural health monitoring and maintenance strategies.

Assessing structural homogeneity and heterogeneity in offshore wind farms: A population-based structural health monitoring approach

This paper presents an in-depth analysis of population-based structural health monitoring applied to offshore wind farms, with a specific focus on a real wind farm. The study quantifies the heterogeneity within the wind turbine population utilising advanced numerical methods. The Fréchet distance is introduced as a key metric to assess the similarity in terms of structural integrity among the turbines considering factors, such as geometry, material properties, topology, and operational conditions. The results revealed a predominant homogeneity with respect across the wind turbines, despite variations in environmental exposure and operational states. Furthermore, the scope of the study is extended to explore the implications of environmental influences, such as corrosion, scour, and marine growth, which are typically unmonitored yet crucial for comprehensive structural assessment. The present paper contributes to the field by demonstrating the feasibility of knowledge transfer in homogeneous wind turbine populations and highlighting the need for detailed similarity assessments in heterogeneous settings. The findings of this study pave the way for better-targeted, smart, and efficient structural health monitoring strategies in renewable energy systems.

Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

Abstract Physics-based multi-scale corrosion simulation plays a vital role in predicting the evolution of pitting corrosion on large civil infrastructure such as miter gates, contributing to a model-informed structural health monitoring (SHM) strategy for risk-based asset health management. The physics-based analysis, however, may not accurately reflect the underlying true physics due to various uncertainty sources and needs to be updated using Bayesian inference methods based on observations to make the prediction closer to field observations. However, traditional Bayesian inference requires the evaluation of a likelihood function, which is often unavailable due to the complex model architecture and various surrogate models used in the analysis. Therefore, likelihood-free inference approaches are required for the updating of the multi-scale corrosion simulation models. This paper meets this need by proposing a conditional invertible neural network (cINN)-based Bayesian model updating method for an existing corrosion simulation model. We first train an cINN model based on simulated observations generated from a high-fidelity forward corrosion analysis model. A convolutional neural network (CNN)-based feature extraction algorithm is then employed to extract key features from corrosion images. After that, the extracted corrosion features from CNN is used as inputs of the cINN model to directly obtain posterior distributions of uncertain corrosion model parameters without evaluating the likelihood function. The results show that the proposed cINN-based model updating approach can provide more accurate inference results with a reduced computational cost in comparison to the classical approximate Bayesian computation (ABC) approach.

A dimensionless feature for continuous bridge health monitoring with tiltmeter measurements

Structural Health Monitoring (SHM) is an essential tool for bridges especially if they have a long period of activity. The bridge considered in this work allows the crossing of the river Po between Lombardia and Emilia-Romagna in Italy. It is about 1.2 km long and has 35 main spans: 25 simply supported beams and 10 cantilevers. The bridge, in use since 1958, underwent a closure period and extraordinary maintenance in 2019 due to severe structural problems. After reopening, the bridge is continuously monitored with a system of 88 bi-axial tiltmeters installed in May 2022. The sensors measure static inclinations (< 1Hz) providing information on the deflection and torsion of the bridge spans. The monitoring system returns a set of summary statistics for each inclination signal gathered in the last hour. Statistics like the mean are used to examine anomalous span drifts in the long term. Instead, the Interpercentile Range is used to evaluate span movements in the short term. In fact, the latter proved to be a good indicator of the hourly traffic load on the bridge, assuming a constant structure stiffness. Therefore, an SHM strategy is to monitor span movements, on a daily basis, using traffic as an active load. This makes it possible to identify any increases in bridge oscillations in relation to a reference condition, calculated over a given monitoring period. For this purpose, a dimensionless metric is proposed as monitoring feature. First, a theoretical analysis of metrics is presented. Then, results obtained from experimental bridge monitoring data are shown.

On Conserving Privacy in Structural Health Monitoring

The ability to make informed decisions with regards to maintenance and service life is critical across many high-value industries. Making accurate predictions about the condition of assets is a key goal and one supported by structural health monitoring (SHM) strategies. To help support this aim, the development of a population-based approach to structural health monitoring (PBSHM), has been making progress in recent years. This solution for dealing with structures, aims to group them together in “populations” and transfer information between individuals, within the population. PBSHM necessitates the sharing of information between structures, which may not all be controlled by a single stakeholder. Therefore, a concern arises that PBSHM may compromise “private” information between different organisations or individuals if their assets are included in a PBSHM scheme. This work will seek to demonstrate a solution to this problem. Differential Privacy is a probabilistic privacy regime that retain population trends in a dataset whilst keeping individual data private from others by the injection of random noise into the data. In this context, privacy is a one-way mapping which obscures sensitive information in a dataset, in a non-reversible manner; as opposed to encryption which may be “broken”. Being differentially-private gives a bound on the amount of information which is ever recoverable. Being able to protect discriminating features of different structure designs from other parties will promote and accelerate the adoptions of PBSHM strategies in industrial settings, where protecting information is a key concern. This paper will demonstrate the application of differential privacy to a simulated homogeneous population of structures.

Multi-Task AI-driven undetermined blind source identification for modal identification of large scale structures

The effective management of aging infrastructure represents a critical concern globally, exerting profound socio-economic influences and occupying a prominent position on the political agenda. The escalating occurrences of catastrophic collapses in recent years underscore the pressing requirement for efficient Structural Health Monitoring (SHM) strategies. These strategies play a pivotal role in aiding decision-making processes related to prioritizing interventions and rehabilitation efforts. Among SHM systems, those utilizing Operational Modal Analysis (OMA) through vibration-based techniques have gained popularity due to their non-destructive nature, comprehensive damage assessment capabilities, and relative ease of automation. Despite their advantages, current OMA methods encounter significant scalability challenges, primarily attributable to extensive computational demands and the necessity for substantial expert involvement. In this context, recent advancements in the field of artificial intelligence (AI) present a promising solution to address these scalability issues, opening the door for the development of next-generation SHM systems. This study introduces an innovative Multitask Learning Deep Neural Network (MTL-DNN) model designed for rapid and automated blind source modal identification of structures. Integrating the concepts of undetermined second-order blind source identification (SOBI) into the architecture of the network, the proposed model can reveal the independent modal components present in the raw response acceleration data with minimum computational burden. The presented results demonstrate the quasi-instantaneous modal identification capabilities of the proposed architecture, enabling the estimation of more components than the number of sensors.

A multivocal literature review of digital twins, architectures, and elements in civil engineering

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.

Two-stage residual networks for damage identification and location of stiffened composite panel based on guided waves

The damage detection of the stiffened composite panel, as a typical aircraft structure, is a research hotspot in Structural Health Monitoring (SHM). where guided waves propagate with multi-modal and dispersion characteristics. The traditional damage detection method manually extracts the potential discriminative features of the signal to achieve damage identification, depending on expert experience. In this paper, we propose a two-stage residual networks (ResNets) framework based on guided waves to locate damage in the stiffened composite panel, which automatically mines the high-dimensional features with sensitive discriminant information. The guided wave signal acquisition system collects four types of data: health data, stringer damage data, damage data on the skin of the stringer-side, and damage data on the skin-side. The first-stage utilizes a ResNet to classify the structure condition, while in the second-stage, three separate ResNets are employed to locate the damage according to the classification results of the first-stage. The experimental results show that the accuracy of the first-stage damage classification and the damage localization of the stringer and the skin of the stringer-side in the second-stage has reached 100%, and that of the skin-side is 99.13%, which significantly outperforms single-stage methods. This strategy of inter-class discrimination and intra-class precise localization of damage can not only identify the damaged regions but also determine the specific location of the damage, which greatly increases the performance of SHM. The present two-stage method is a potential solution for future SHM strategies and further investigation is warranted.

Experimental validation of automated OMA and mode tracking for structural health monitoring of transmission towers

This article presents a cost-effective method to monitor the structural health of transmission towers, a critical yet aging infrastructure that plays an important role in the overall reliability of the electrical grid. The method is validated experimentally on a real-world transmission tower which was subjected to several (exaggerated) damage scenarios. The proposed monitoring strategy relies on four accelerometers installed on the four faces of the rectangular base of the transmission tower. The collected vibration data is processed using a classic operational modal analysis (OMA)-based structural health monitoring scheme, comprising; automated OMA, tracking, data normalization, and decision-making. The proposed algorithm processes the four faces independently to maximize the likelihood of detecting (local) damage near the sensors in the quasi-symmetric structure. Furthermore, with widespread deployment in mind, the current article introduces a semi-automated tracking algorithm using “Density-based spatial clustering of applications with noise.” Environmental effects were removed using principal component analysis, eliminating the need for additional (environmental) sensors. Finally, Q and T2 statistics were used to assess damage on each face of the structure using all tracked modes. The experimental results of this study demonstrate that this workflow can effectively track a large number of modes; in the current study, 10 modes per face of the structure, and from them detect and to some level localize the majority of structural damage-inducing events, such as the removal of a bolt or a bar.

Advancing a Non-Contact Structural and Prognostic Health Assessment of Large Critical Structures.

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover.

Monitoring the changes in the dynamic properties of an RC building experiencing column loss

In this study, an existing six-storey reinforced concrete building with an asymmetric structural plan and soft storey irregularity was used as a test specimen and subjected to three-step progressive structural damages to detect the variations in its dynamic properties. Mode shapes and dominant frequencies of the undamaged building were determined by the ambient vibration survey (AVS) and it was seen that its first three modes were torsion coupled. Besides, soft storey irregularity was evident due to the lack of masonry infill walls on its ground floor. Later on, three-step progressive damages were applied to the building. The first step targeted three columns and one beam of the building, located on a corner region of its ground floor to peel off their clear covers. The second step razed two adjacent corner columns which were already moderately damaged in the first step, while the third step knocked the third moderately damaged column down. After each damage step, AVS was repeated with the same details as applied for the undamaged building. The obtained dynamic properties for the four phases of the building were evaluated with the sustained damage. Numerical analyses with the finite element model of the building representing its four different phases were also performed and the unique responses due to damage effects on the structure were investigated numerically. As a result of induced damage, the quantified amount of frequency change in modes and the new mode observed after particularly column loss scenarios can be utilized for efficient structural health-monitoring strategies of plan-asymmetric buildings and post-earthquake assessment of partially damaged buildings where timely objective assessment is important.

Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring

Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper, we propose an SHM scheme where IMU and multi-antenna GNSS are tightly integrated. The phase centers of multiple GNSS antennas are transformed into the IMU center, which increases the observation redundancy and strengthens the positioning model. To evaluate the performance of tight integration of IMU and multiple GNSS antennas, high-rate vibrational signals are simulated using a shaking table, and the errors of horizontal displacement of different positioning schemes are analyzed using recordings of a high-precision ranging laser as the reference. The results demonstrate that applying triple-antenna GNSS/IMU integration for measuring the displacement can achieve an accuracy of 2.6 mm, which is about 33.0% and 30.3% superior than the accuracy achieved by the conventional single-antenna GNSS-only and GNSS/IMU solutions, respectively.

Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement.

This paper presents the design, implementation, and validation of an on-blade sensor system for remote vibration measurement for low-capacity wind turbines. The autonomous sensor system was deployed on three wind turbines, with one of them operating in harsh weather conditions in the far south of Chile. The system recorded the acceleration response of the blades in the flapwise and edgewise directions, data that could be used for extracting the dynamic characteristics of the blades, information useful for damage diagnosis and prognosis. The proposed sensor system demonstrated reliable data acquisition and transmission from wind turbines in remote locations, proving the ability to create a fully autonomous system capable of recording data for monitoring and evaluating the state of health of wind turbine blades for extended periods without human intervention. The data collected by the sensor system presented in this study can serve as a foundation for developing vibration-based strategies for real-time structural health monitoring.

Heteroscedastic Gaussian processes for data normalisation in probabilistic novelty detection of a wind turbine

This study investigates the data normalisation of modal parameters of an operating concrete–steel hybrid onshore wind turbine tower considering also the identification uncertainty. In order to take into account the Environmental and Operational Condition (EOC)-dependent variance, sparse heteroscedastic Gaussian processes (GPs) are used for the data normalisation. Following a typical vibration-based Structural Health Monitoring (SHM) scheme, data normalisation of the natural frequencies and the mode shapes is performed first. Subsequently, a metric is defined which takes into account both the identification uncertainty and the operation-dependent uncertainty in order to enable novelty detection.The data normalisation methods must be able to handle uncertainties of different magnitudes due to EOCs in the data. In this context, GPs can be a suitable tool. However, standard GPs assume homoscedasticity, which is an unrealistic assumption in the case of EOC-dependent variance. Using a heteroscedastic GP instead, the variance of the data is better mapped and allows comparison with the identification uncertainties of Bayesian operational modal analysis (BAYOMA), taking into account the specifics of closely spaced modes of the tower structure. This leads to a better interpretation of the data and enables the introduction of a probabilistic novelty metric.This data normalisation approach, taking into account EOC-dependent uncertainties using heteroscedastic GPs, is being applied for the first time to a tower of a full scale 3.4 MW wind turbine in operation. Following this approach, it is possible to detect smaller changes in natural frequencies and second-order modal assurance criterion (S2MAC) compared to the assumption of homoscedasticity within the GP. In addition, a novelty was detected using the S2MAC during the period under study. Therefore, it can be illustrated that mode shape-based metrics tend to be more sensitive than purely frequency-based ones. However, it is difficult to assess the significance of such changes for structural integrity without further information.

Identification of damages in a concrete beam: a modal analysis based method

Structural Health monitoring (SHM) strategies can play a pivotal role in the perspective of enhancing structures and infrastructures life cycle and maintenance operations. A plethora of sensors and technologies can be employed in this field; in a seismic context, vibrational tests are particularly relevant, being able to give an insight on the dynamic characteristics of the structure itself. In particular, modal parameters can be considered in order to detect damages. A comparison between a certain test time and 0 test time (i.e., undamaged structure) is commonly performed; to this aim numerical models result particularly useful to provide baseline data (often unavailable in pre-existing structures), but they need to be validated before use. Non-contact techniques, like scanning laser Doppler vibrometry, can be exploited to do this. In this paper a numerical model of a scaled concrete beam is realized and validated through LDV data, then it is used to design load tests for progressive damages generation. Modal analysis is conducted after different load trials to evaluate changes of modal parameters in relation to the damage occurred; also, damage-related indices are proposed. The results confirmed the suitability of LDV for dynamic analyses of cement-based structures and this can be particularly useful when big structures (e.g., bridges) have to be monitored in-field. The numerical model was validated with acceptable absolute errors in terms of natural frequencies (between 26 Hz and 131 Hz) and high Modal Assurance Criterion (MAC) values (0.85-0.93). Moreover, the proposed methodology allows to detect damages also in a concise way through synthetic indices (with changes >50% in damaged vs undamaged conditions) and early warnings could be generated according to their values, hence supporting decision-making procedures in the building management scenario.

On the hierarchical Bayesian modelling of frequency response functions

Structural health monitoring (SHM) strategies seek to evaluate, predict, and maintain structural integrity, to improve the safety and design service life of structures in operation. Many of these strategies involve monitoring changes in structural dynamics, as damage can affect modal properties and present as changes in the characteristics of the resonance peaks of the frequency response function (FRF). While recent advances have improved the safety and reliability of structures, a number of challenges remain, impeding the practical implementation and generalisation of these systems. Like damage, benign variations, such as those caused by changes in temperature or other environmental fluctuations, can affect dynamic properties, making it difficult to distinguish between damage and normal operating conditions. In addition, newly-deployed structures can have insufficient data to describe the normal operating conditions (i.e., data scarcity), which can impair the development of data-based prediction models. Another common challenge is data loss (i.e., data sparsity), which may result from transmission issues, sensor failure, a sample-rate mismatch between sensors, and other causes. Missing data in the time domain will result in decreased resolution in the frequency domain, which can impair dynamic characterisation.For situations that may benefit from information sharing among datasets, e.g., population-based SHM of similar structures, the hierarchical Bayesian approach provides a useful modelling structure. Hierarchical Bayesian models learn statistical distributions at the population (or parent) and the domain levels simultaneously, to bolster statistical strength among the parameters. As a result, variance is reduced among the parameter estimates, particularly when data are limited. In this paper, a combined probabilistic FRF model is developed for a small population of nominally-identical helicopter blades, using a hierarchical Bayesian structure, to support information transfer in the context of sparse data. The modelling approach is also demonstrated in a traditional SHM context, for a single helicopter blade exposed to varying temperatures, to show how the inclusion of physics-based knowledge can improve generalisation beyond the training data, in the context of scarce data. These models address critical challenges in SHM, by accommodating benign variations that present as differences in the underlying dynamics, while also considering (and utilising), the similarities among the domains.

Generalized Beam Theory for the static and dynamic response of a bridge deck with Structural Health Monitoring purposes

In recent years Structural Health Monitoring (SHM) has been assuming a fundamental role for existing bridges and viaducts, primarily for the aged ones, making compelling the need to resort to adequate techniques that enable the user to correctly process and interpret the amount of produced data. In the present work, with the purpose of defining SHM strategies for viaducts, a mechanical model of an existing bridge deck is realized on the basis of the Generalized Beam Theory (GBT). With the aim of catching the static and dynamic behavior of the structure up to prescribed tolerances, the model takes into account, for instance, the possible in-plane and out-of-plane distortion of the cross-sections and, at the same time, turns out to be easy to use and adapt, according to the level of detail one wants to achieve. Possible implementation of geometric and elastic nonlinearities is discussed as well. Moreover, data gained through static loading tests on the bridge, almost up to the elastic limit of the structure, and dynamic tests in operational conditions, processed in the framework of the Operational Modal Analysis (OMA), are used as benchmark for the calibration of the parameters of the mechanical model. A convergence analysis is then performed, so as to define the minimal number of degrees of freedom required to consistently simulate the mechanical behavior of the structure. Discussion on the effects of either point or distributed support conditions is proposed, with consequences in the implementation of the boundary conditions of the mechanical model.

Advancements in Optimal Sensor Placement for Enhanced Structural Health Monitoring: Current Insights and Future Prospects

Structural health monitoring (SHM) is critical to maintaining safe and reliable civil infrastructure, but the optimal design of an SHM sensing system, i.e., optimal sensor placement (OSP), remains a complex challenge. Based on the existing literature, this paper presents a comprehensive review of OSP strategies for SHM. It covers the key steps in OSP, from evaluation criteria to efficient optimization algorithms. The evaluation criteria are classified into six groups, while the optimization algorithms are roughly categorized into three classes. The advantages and disadvantages of each group of methods have been summarized, aiming to benefit the OSP strategy selection in future projects. Then, the real-world implementation of OSP on bridges, high-rise buildings, and other engineering structures, is presented. Based on the current progress, the challenges of OSP are recognized; its future development directions are recommended. This study equips researchers/practitioners with an integrated perspective on state-of-the-art OSP. By highlighting key developments, persistent challenges, and prospects, it is expected to bridge the gap between theory and practice.