This paper introduces a novel indirect bridge modal identification framework for extracting higher-mode bridge modal parameters. The method involves processing the vehicle response using a newly proposed iterative vehicle response demodulation technique, which separates multi-component vehicle responses while preserving the physical meaning of variables. This property is crucial for ensuring accurate and robust modal identification from vehicle responses. The feasibility of this approach is initially tested through the analysis of vehicle responses generated by numerical simulations. This analysis includes a detailed parametric study and discussions on various bridge conditions and types. Subsequently, the same methodology is applied to an experimental bridge, where structural modal parameters are extracted from the measured responses of a passing instrumented vehicle. The key contributions of this study include the development of the iterative vehicle response demodulation formulation and the ability of this method to identify modal parameters using fewer processing steps compared to conventional indirect identification methods. Results from both numerical simulations and experimental tests demonstrate that the iterative demodulation method can effectively extract higher-mode bridge modal parameters, validating the proposed framework’s effectiveness.

The Bayesian framework in structural health monitoring includes both modal identification and model exploration. Probabilistic model exploration, also named as model updating, can effectively estimate the structural parameters and quantify their uncertainties. However, it can be computationally intensive on application to real-world large-scale structures. Meta-models, e.g. Kriging models, can help tackle this challenge but they also introduce more uncertainties. In this paper, a novel Bayesian framework combining the active learning Kriging approach is proposed. The framework comprises three major components: the improved fast Bayesian spectral density approach for modal identification, the active learning Kriging method for meta-modelling, and the Bayesian structural model exploration. The Transitional Markov Chain Monte Carlo algorithm is implemented throughout the framework to sample the posterior distributions. The uncertainties from three aspects, i.e., (1) measurements, (2) meta-model construction and (3) finite element modelling, are considered in definition of the likelihood function adopted in both the active learning and model exploration processes. Compared with the ordinary Kriging model and adaptive Kriging approach using U function, the proposed active learning method significantly reduces the uncertainties of the Kriging predictor and improves its local prediction performance with fewer samples. The proposed framework is validated by a continuous test beam in the laboratory and applied to a real-world cable-stayed bridge using structural health monitoring data. A mode-matching criterion is used to overcome the difficulty of closely spaced modes in model exploration of the cable-stayed bridge. As the proposed framework is data-driven, no weighting hyperparameters are required. The active learning Kriging-based Bayesian framework can directly process structural dynamic time history response and conduct probabilistic model exploration with multiple uncertainties included, and therefore is promising in application to major structures.

The implementation of machine learning techniques in structural health monitoring has garnered significant attention in recent years due to their performance in nonlinear modelling. Compared with the traditional artificial neural network (ANN) methods, deep learning (DL) technologies comprising multi-layer ANNs have become increasingly powerful for solving nonlinear problems over the past decade. Within the DL domain, recurrent neural networks are improved by long short-term memory (LSTM) networks, which can capture the long-term dependencies in sequential data prediction. This advancement in DL technology has significant implications for predicting complex structural behaviour under changing environmental conditions. As bridge structures will experience much higher temperatures due to climate change, LSTM models can help predict the nonlinear relationship between various ambient climatic conditions and the temperature behaviour of bridges based on the limited input, effectively leveraging their capability for time series analysis. Optimising the model hyperparameters of the neural network by the Bayesian optimization algorithm also improves the DL network. As the simple data-driven method cannot simulate the entire bridge temperature field, the nodal temperatures predicted by the DL model at limited locations will be combined with a suitable discrete numerical model for heat transfer analysis to further enhance the accuracy. The DL-based temperature simulation model is then applied to a steel bridge deck and verified by monitoring data.

The ever-increasing demand for accelerated construction of urban bridges or viaducts calls for more prefabrication with various joint schemes, while satisfying the need for seismic resilience. On the other hand, segmentation into components of various shapes and morphologies is common for multifunctional structures found in living organisms and ancient Chinese timber structures, which also inspires the development of resilient segmental bridge column design. To provide further insights into the cost-effective resilient design of precast bridge columns, this paper suggests the use of resettable sliding joints (RSJ) comprising lubricated gentlyinclined joint interface with partially debonded tendon arrangement, essentially forming a hybrid sliding and rocking seismic isolation system of precast segmental structures. The deformability of the proposed design is high. Similitude analysis was conducted considering the rigid body behavior of individual segments under seismic input. It presents the conceptual shaking table testing of four 1:12 prestressed segmental column models fabricated with special three-dimensional (3D) printed plastic molds and interface treatment. A motion capture system was installed for accurate 3D displacement data acquisition and five typical major near-fault earthquake records were selected. Among all specimens, the RSJ segmental column could reach a high damping ratio of about 20 % during the tests, and it outperformed the others in many aspects: (i) a tolerable maximum top drift of about 1 %; (ii) a moderate percentage of transient rocking of about 20 %; (iii) an evenly distributed sliding displacement among all sliding joints; and (iv) negligible residual sliding drift at sliding joints. These superior attributes of RSJ segmental column are consistent under excitation of consecutive rounds of different major earthquake records, indicating high robustness as well as high seismic resilience in the design of RSJ segmental columns.

Under continuous environmental erosion and long-time high tension, fatigue micro-cracks of bridge cables appear frequently to challenge the reliability of cables. The vibro-acoustic modulation analysis is adopted because of its sensitivity to contact-type defects, especially fatigue cracks. In this paper, using multiple sets of low-frequency vibration and high-frequency ultrasonic excitation, the propagation of the ultrasonic wave in the steel strands is studied. Then, the modulated signal bispectrum is proposed to improve the detection reliability of nonlinear modulation components in the nonlinear vibro-acoustic modulation signal of the contact-type defect. The numerical simulations and laboratory tests of straight wire, helical wire, and multi-wire strands are carried out for micro-crack detection to verify the robustness of the proposed method. Compared with the traditional method, the modulated signal bispectrum can still produce clearer peak detection under high noise conditions. The results show that it provides a promising, dynamic and rapid cable defect detection technique.

This study introduces an active learning-guided online cable force monitoring system based on a modified S-transform approach, addressing key challenges in real-time system identification: complex non-stationary excitations, computational efficiency, and robustness. The framework identifies cable tension during non-stationary wind loads, significantly improving accuracy and efficiency via an extended active learning Kriging method. It effectively detects potential outliers, identifies the cable's fundamental frequency using a data fusion technique, and calculates real-time cable force and tensile stress with empirical formulae. A comprehensive analysis, including numerical and sensitivity studies, shows an error rate of less than 4 % in all cases, proving the proposed framework's superior accuracy, efficiency, and robustness compared to traditional methods. Laboratory validations using cable test data and Jiu Zhou Bridge data demonstrate the system's stability, even under extreme conditions, such as during Super Typhoon Mangkhut, providing a reliable solution for real-world cable force online monitoring.

In this paper, an active learning framework for structural baseline model exploration is proposed based on the Kriging method. The framework is built to solve the problem that when the traditional Kriging approach needs to calibrate more structural parameters, the performance of Kriging predictors hinges very much upon the number of samples obtained from the finite element analysis. A novel information entropy-probability learning function is derived based on Bayesian inference and information entropy. To give a full picture, the uncertainties of different responses should also be quantified during the updating process. Then the effects of uncertainties are considered in both the learning function and objective function constructed from the posterior probabilities. The proposed algorithm is first verified experimentally by a two-span continuous beam considering different types of responses. The framework is then further improved for updating the baseline model of a cable-stayed bridge using field data. The active learning approach, as compared to the ordinary Kriging method, can achieve good performance without undue computational cost and the need for imposing weights on different responses. The proposed baseline model exploration method can be extensively applied to bridge engineering because it facilitates the calibration of numerical models using field measurements and response simulation of extreme loading with significant improvement in computational efficiency and performance of Kriging predictors.

The deterioration of bridge networks poses a major threat to the availability and function of transportation systems and ultimately affects social development. Deep reinforcement learning is expected to provide intelligent decision support for bridge network maintenance. However, existing studies have neglected to explicitly consider the impact of maintenance behavior on the cost-effectiveness of bridge networks. The complex traffic environment and the interconnection of bridge networks also pose unique challenges in balancing maintenance costs and benefits. It is necessary to explore how to use the specific traffic data of each bridge in the bridge network to effectively balance cost-effectiveness and rationalize maintenance decisions. Aiming at the maintenance requirements of the bridge network, a multi-agent ranking proximal policy optimization framework is proposed. The performance of the proposed framework is rigorously evaluated using a real bridge network example. The results show that the maintenance policy based on the proposed framework can maximize the cost-effectiveness of the bridge network in its life cycle, effectively reduce the excessive risk cost and achieve a harmonious balance between different costs. In addition, the proposed framework is superior to the traditional maintenance policy and provides higher performance and efficiency.

On the basis of the steel-concrete composite orthotropic bridge deck, corrugated steel plate is introduced as the longitudinal bottom plate, onto which shear connectors are welded to form a new type of composite bridge deck, namely corrugated steel-concrete composite bridge deck. The integration of the steel sheeting and concrete together can make full use of the performance of steel in carrying the load. This paper describes an investigation of a real bridge in Anhui Province, China, focusing on the load-carrying capacity of the corrugated steel-concrete composite deck and the failure mode of the shear connectors. First, the mechanical properties of a shear connector welded onto a corrugated steel-concrete composite bridge deck are simulated and explored experimentally. A set of comparative tests on three groups of push-out specimens are designed and carried out. The test results show that the ultimate load-carrying capacity of the corrugated steel-concrete composite deck is increased by about 45% as compared with the standard specimens and the slip at the interface is reduced by about 34%. A comprehensive parametric study of the three sets of push-out tests is then conducted using the nonlinear finite element method. The parametric analysis shows that the ultimate shear capacity of the shear connector increases with the strengths of the concrete and corrugated steel plate as well as with the diameter of the stud, albeit with diminishing return.

The relatively uncontrolled dynamic behavior of a rigid block resting on a flat surface under ground motion has been well studied. In contrast, a block with a gently inclined V- or W-shaped sliding key would ensure dynamic self-centering or resetting performance under various seismic conditions. To better understand the nonlinear responses of rigid blocks having this type of interface where both rocking and sliding movements are possible, an event-based algorithm is put forward to cover various types of motions, including the transition stages between different types of motions. A comprehensive study is carried out to examine the dynamic responses of blocks of different sizes and aspect ratios. Moreover, the simplified pure rocking model and pure sliding model are also adopted to obtain rough estimates of dynamic response for comparison. The results show that the simplified models are reliable for some special cases only, e.g. squat blocks and slender blocks. The study also evaluated the influence of the coefficient of friction and slope inclination on the resetting capabilities of typical sliding-prone and rocking-prone systems. For those cases that are prone to toppling or excessive sliding under strong earthquakes, the provision of vertical post-tensioning can effectively ensure stability and resetting performance. Such findings provide insight into the performance of precast segmental bridge columns with resettable sliding joints.

The presence of deterministic harmonic excitation, such as that induced by rotating machinery, violates the classical operational modal analysis (OMA) assumption that the system output is strictly ergodic. This paper proves that the presence of harmonic excitation has no effect on the identification of structural modes except in the case where some of the harmonic excitation frequencies are close to the structural frequencies. Therefore, the harmonic component must be removed from the mixed random and harmonic system output before further processing. This paper proposes a Ramanujan subspace projection (RSP) method for harmonic removal, which is realized by projecting the raw system output onto the complex conjugate division (CCD) of the Ramanujan subspace. The novelty of this study is that it reveals the relationship between the frequencies defined in the period and frequency domains, allowing the RSP method to directly extract the harmonic component from the specific CCD without any frequency domain analysis. In addition, an energy indicator is proposed to select the underlying CCDs with the most robust harmonic feature. Using the indicator, one only needs to project the raw system output onto a subset of the CCDs rather than all of them, thereby significantly reducing the computational effort. After removing the harmonic components from the raw output, the remainder can be fed into the covariance-driven stochastic subspace identification (Cov-SSI) method for OMA. The numerical, experimental and field test results show that the proposed RSP method is not only resistant to random noise but also capable of precisely extracting the weak harmonic component from the raw output with less computation. Furthermore, the modal parameters of the structures subjected to mixed random and harmonic excitation can be accurately identified by combining the Cov-SSI and RSP methods.

Deepsea mining risers, which transport mineral resources from the seafloor to surface facilities, are critical components of deepsea mining systems. This article presents a novel cointegration-based method for localizing structural damage in risers subjected to random excitations from waves, currents, and moving floating facilities. Although the dynamic responses of a riser (usually consisting of multiple segments) are often nonstationary, those within an individual pipe segment often have synchronous fluctuations with a common trend, allowing the application of a multivariate time series analysis. Cointegration can thus be performed for a pipe segment by treating the internal responses as the endogenous variables and those of the other pipe segments as the exogenous variables. Consequently, a stationary cointegrating residual for the dynamic responses purged of the influence of nonstationary excitations can be obtained. The cointegrating residual in conjunction with a statistical hypothesis test scheme is used to create an output-only damage indicator. The effectiveness of the proposed damage localization method is demonstrated with a numerical and experimental multi-segment riser. The results indicate that the strain responses of both risers exhibit nonstationary fluctuations due to the changeable environmental excitations. As the proposed method is robust to the external loading, it can accurately identify the damage location.

With the completion of several pilot projects in Hong Kong, Modular Integrated Construction (MiC) is being promoted to boost the productivity and cope with the labour shortage in the local construction industry. However, the possible adverse effects of differential axial shortening due to the time-dependent behaviour arising from concrete creep and shrinkage will only become obvious after a long time. In this study, simplified numerical models are established based on typical hybrid MiC buildings in Hong Kong. Time-dependent analyses considering the creep and shrinkage of concrete are carried out. The construction schedules of the pilot MiC projects are used as a reference to model the staged construction. The results for a 20-storey hybrid MiC building indicate, owing to the concrete creep and shrinkage, noticeable stresses at the module-to-wall connections at the corners of core walls 30 years after construction. The compressive stresses at the bottom of the steel MiC modules will increase substantially over time, which could reduce the amount of material strength that can be utilised by other loads. Hence checking of long-term performance should be conducted at the design stage, particularly focusing on the connections.

The incorporation of corrugated steel webs in composite box-girder bridges has greatly improved their structural performance. However, the low axial stiffness of the shear-deformable corrugated steel webs also affects the structural behavior under the commonly encountered eccentric loading. In this study, experiments were carried out to investigate the behavior of this type of bridge under eccentric loading. Based on the experimental study, the basic assumptions in existing torsion theories are examined. In addition, the proposed formula to estimate the torsion constant and amplification factor considering the additional sectional normal stress due to distortion and warping under eccentric loading in design codes is also checked. From the study, some design recommendations are provided.

Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging‐based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle‐bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.

Time-series interferometric synthetic aperture radar (InSAR) provides a unique tool for measuring large-scale and long-term land surface deformation. Under the assumption of a single linear deformation model in conventional InSAR, it is difficult to quantify and interpret the impacts of multiple environmental factors that presumably induce nonlinear deformations. In this paper, we propose a SAR-Transformer method to decompose InSAR time-series signals into various physics-related components and apply the method to evaluate the deformation of the world's longest cross-sea bridge, the Hong Kong-Zhuhai-Macao Bridge (HZMB). We first developed an improved bridge geometry-based InSAR network to monitor the deformation of the HZMB using Sentinel-1 and COSMO-SkyMed images from 2019 to 2022, which were validated using the leveling and GPS data. The SAR-Transformer model was trained using synthetic InSAR time-series samples and applied to decompose the monitored InSAR measurements. Compared with that of conventional curve-fitting and seasonal-trend decomposition using LOESS, SAR-Transformer reduced the mean absolute error at least by 58.32% and mean absolute percentage error at least by 8.84% for time-series signal reconstruction. We evaluated the decomposed patterns according to the geotechnical, meteorological, and marine processes, and found that: 1) Seasonal thermal expansion owing to temperature changes was significant in all parts of the bridge, and deflection due to concrete shrinkage and creep was observed on cable-stayed bridges. 2) The artificial islands experienced evident ground subsidence with a decelerating trend. In particular, the newly adopted non-dredged reclamation method resulted in a lower decelerated settlement than that of fully-dredged reclamation areas. 3) The seawall showed linear horizontal movement from the outward stretching of the reclaimed soil consolidation and periodic displacement related to sea tidal loading. Furthermore, typhoons and coastal earthquakes had limited effects on the permanent movement of the bridge. These results improve the understanding of the interactions between artificial super-infrastructures and environmental factors, and provide valuable guidelines for the maintenance and management of the HZMB.

The incorporation of corrugated steel webs in prestressed concrete bridges has improved their structural performance. However, the low axial stiffness of the shear deformable corrugated steel webs affects the shear distribution in the section and its potential shear failure mode. Design engineers often assume that the corrugated steel webs resist all the shear force; however, there is the risk of shear failure in the concrete flanges near the diaphragms and point loads due to the interaction between the local flexure of the concrete flanges and the shear deformation of corrugated steel webs. However, there is limited research on the shear failure of concrete flanges. The shear distribution in the composite section and the shear failure in the concrete flanges were studied experimentally and numerically in this study. Tests showed that local shear failure could occur in the concrete flanges. A formula is proposed to estimate the shear capacity of the composite section considering a potential shear failure in the concrete flanges together with some design recommendations.

This paper proposes a novel approach of time series analysis to identify the potential changes in structural conditions, e.g., degradation owing to accumulated damage. Although the damage-sensitive features (DSFs) of structures depend on the environmental and operational conditions and thus vary over time, they usually have a common trend when the effects of environmental and operational variations (EOVs) are linear or quasi-linear. Therefore, cointegration analysis, which can combine several time series into a stationary residual purged of the common trend, is used to remove the effects of EOVs and assess the structural condition. The main contribution of this study is that a low rank matrix approximation (LRMA) algorithm is introduced to constrain the rank of the stacked DSF matrix, thereby suppressing the random errors inevitable in structural damage detection and both the linear and nonlinear effects induced by EOVs. The nonlinear effects have been observed in the identified natural frequency data of several bridges in service and cannot be easily handled by the general cointegration due to its linear nature. Another advantage of this process is that the missing entities in the DSF series can be automatically imputed, taking full advantage of incomplete data acquired for analysis. The effectiveness of the proposed method is demonstrated by using the benchmark data of the KW51 Railway Bridge in structural condition identification. Results indicate that the changes in the structural condition can be correctly detected despite the existence of random errors and nonlinear effects induced by EOVs. The proposed method can also work when only a small amount of incomplete data is available.

Planar sliding joints have been introduced to precast segmental bridge columns to alleviate the detrimental effects of earthquakes. However, residual sliding movements may result from strong shaking. Resettable sliding joints comprising V-shaped or W-shaped interfaces of gentle inclinations have been proposed to achieve better resilience against seismic actions in regions of moderate to high seismicity. However, possible deterioration of the frictional performance at the concrete contact surfaces may affect the resetting performance of these joints. In this regard, the tribological features, performance and durability at the interface of resettable sliding joints in precast segmental bridge columns were investigated experimentally utilizing 3D printed moulds and computed tomography scanning technology. Various surface treatment and lubrication methods were evaluated monthly up to 360 days after casting based on a modified cyclic direct shear apparatus at high loading pressure. Results show that high-performance PTFE grease lubricant is useful in achieving and maintaining favourable coefficients of friction as low as 0.05.

Cointegration has been used to distinguish the changes in dynamic features of a structure caused by environmental variations from those related to structural damage. This paper describes the development of a novel low-rank filter to suppress the noise in damage-sensitive features and enhance the identifiability of cointegration. It has been confirmed that cointegration is highly dependent on the presence of a vector error-correction model composed of a rank-deficient long-run impact matrix Π. Therefore, the low-rank filter employs a low-rank matrix pencil A+BKC to reconstruct Π iteratively, where A is the noise-free counterpart of Π, and the matrix product BKC accounts for the influence of noise. By minimizing the Frobenius norm of the gain matrix K, the random noise in the damage-sensitive feature series can be suppressed while the cointegration relationship among the damage-sensitive features can be recovered. Comparison between the low-rank filter and two widely used denoising methods, i.e., wavelet and empirical mode decomposition, is performed with the numerical model of an offshore platform and an experimental lattice structure. Results indicate that the low-rank filter is more compatible with cointegration for simultaneously suppressing noise and revealing the damage state of the structures in the presence of environmental variations.