Early tied‐arch bridges cannot satisfy the current traffic load demand due to their load grades and maintenance levels. Also, these tied‐arch bridges have accumulated structural damage with increasingly prominent safety risks. In order to accurately evaluate the characteristics of tied‐arch bridges’ structural‐influenced lines and identify damage of their arch ribs and suspenders, two analytical solutions are derived and established in this paper for the thrust‐influenced lines of parabola and catenary two‐hinged arches with a tie beam. A new method and an index for identifying damage of arch ribs and suspenders of tied‐arch bridges are proposed. The results of numerical simulation in this paper verified that the proposed analytical solution has good analytical accuracy in practice on those two‐hinged nonflat arches. With the use of equivalent thrust‐influenced line difference curvature, the effectiveness of damage identification and the verification method were verified on the tie beam, suspender, and arch rib of plane tie arch structure as well as the suspender and arch rib of tied‐arch bridges in this research. Furthermore, combining with grey relation analysis, the noise immunity of the proposed index method can be verified. Also, thrust‐influenced line recognition based on VMD (variational mode decomposition) is introduced, and a practical process of bridge health assessment based on the quasistatic‐influenced line loading of three‐axle heavy vehicles is established. Theoretical analysis and numerical verification show that the calculated error of the analytic solution of two‐hinged arches with a rise‐span ratio greater than 1/8 is less than 9.57%, and the error decreases with the increase of the rise‐span ratio. Therefore, it can be applied to the calculation and analysis of tied‐arch bridges with a rise‐span ratio range between 1/4 and 1/5. With the equivalent thrust‐influenced line index proposed in this paper, the damage of suspenders and arch ribs of tied‐arch bridges can be accurately located. It has been found that the proposed method is more effective to identify the damage of suspenders than the damage of arch ribs does, showing good noise immunity. In summary, this research has provided theoretical support for arch bridge design and evaluation. Combining with existing bridge‐monitoring methods, the new bridge damage identification method proposed in this paper has the prospect of realizing the normal health status assessment of existing tied‐arch bridges.

The economic impact of Structural Health Monitoring Systems based on optical fibre sensors is assessed in the development of composite helicopter rotor blades. Hence, the focus of this analysis is on the helicopter’s Beginning Of Life stage. Two applications of the Structural Health Monitoring System are considered in the development of composite blades: curing cycle development and accomplishment of laboratory and flight certification tests. Optical fibre sensors measure the temperature field during the curing cycle and strain field during the laboratory tests and allow load identification during the load survey activity. It was found that Structural Health Monitoring Systems can potentially lead to economic benefits during the development of the blade provide that a reduction in the number of curing cycles and number of blades tested is achieved as a consequence of the improvement of the temperature and strain field quality. Moreover, an economic benefit could be achieved during the load survey activity, needed to complete the certification of the composite blade, avoiding the periodical maintenance of the applied strain gauges acquiring the strains during the flight.

The deformation of the main girder is an important manifestation of the overall stiffness of suspension bridges, which is essential for assessing bridge performance. Nevertheless, it is difficult to achieve satisfied prediction without fully considering the overall operational loads. To this end, this paper proposes a method to predict the deflection considering multiple operational loads using the monitoring data of a high‐speed rail‐cum‐road suspension bridge. Initially, an improved Gaussian process regression (GPR) model utilizing Bayesian optimization was employed to predict the deformation of the main girder under the condition of nontrain loads. Furthermore, the distinct contributions of temperature, wind, and vehicle load were analyzed. Subsequently, based on the strain and deflection induced by train loads, the sum of sinusoids method was proposed to construct fitting and shape function (FSF) for predicting the main girder deformation under the influence of train loads. Ultimately, the deformation considering overall loads was obtained by adding the deformation under the nontrain and train loads, and the predicted deformation result was verified using the measured data. When compared to other state‐of‐the‐art machine learning algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and decision tree (DT), the improved GPR demonstrates the highest accuracy in predicting the deformation of the main girder under nontrain loads with R2 of 0.9478. In addition, the proposed sum of sinusoids FSF method accurately predicted the deformation of the main girder caused by train loads, with R2 of 0.934. The deformation of the main girder under the influence of overall loads can lay a foundation for the early warning and evaluation of the suspension bridges.

Oversized vehicles have the potential to collide with walls or ceilings when passing through tunnels and bridges, posing a serious threat to the health of transportation infrastructure and public safety. Hence, it is crucial to accurately and immediately detect vehicle dimensions, including length, width, and height, to avoid such accidents. Using computer vision and view geometry, this study presents a framework for automatically detecting and quantifying the outer contours of vehicles through monocular vision. First, traffic scene images are captured which are then used to create a transformation matrix of the ground surface. Second, a modified Mask region‐based convolutional neural network (Mask R‐CNN) is constructed to detect and segment the vehicle instances from the video frames. Finally, a view geometry‐based algorithm was developed to detect the outer contours of passing vehicles and quantify their dimensions. In the field test, the accuracy of the vehicle segmentation and the identified vehicle dimensions was validated. In addition, the proposed method’s superiority was confirmed by comparing it with two other existing approaches. The comparison results show that the proposed method has better accuracy and is more convenient to use since it does not require a premeasured reference. In addition, the developed method can accurately identify not only the dimensions of vehicles parallel to the road but also vehicles that are changing lanes or making a U‐turn.

The reliability of corroded reinforced concrete (RC) structures relies on the accurate minimum cross‐sectional area of corroded rebar. In this study, the accurate morphologies and self‐magnetic flux leakage (SMFL) field strengths of twenty‐eight non‐uniformly corroded rebars were obtained using 3D structural light scanning and micromagnetic detection technologies, based on which three indices of the SMFL field variation ratio dH, the corrosion non‐uniformity degree dSn, and the cross‐sectional area ratio K0.25 are proposed. The statistical results show that the probability densities of dSn and K0.25 obey the Weibull distribution and Gamma distribution at the 95% confidence level, respectively, and their distribution parameters are linearly or inversely proportional to dH. The probability density distribution of the minimum cross‐sectional area of corroded rebar can be determined using indices dSn and K0.25, based on which a feasible SMFL‐based reliability assessment method of corroded RC structures is proposed. The case study of a real specific corroded RC beam shows that the reliability assessment error of the SMFL‐based method is only 1.2%, which is much lower than the 20.7% error of the existing method. This SMFL‐based method provides a novel idea that can automatically and accurately assess the effect of rebars’ corrosion non‐uniformity on the reliability of specific in‐serviceRC structures.

This paper presents an on‐site anchor bolt tension‐force versus deformation detection method, which enables the assessment and evaluation of the extent of damage in the anchored zone of anchor bolts buried deep within concrete. In addition, considering different toppling modes and their respective force mechanisms, the paper proposes using the entire cross‐sectional plastic bending moment with a 0.4 impact coefficient as the actual effective bending moment for the anchor bolt foundation. This is employed to calculate the tensile forces in the anchor bolts and the local compressive bearing capacity of the concrete above the lower anchor plate. The computational results indicate that the plastic damage in the anchored zone of problematic anchor bolts is primarily caused by the crushing of concrete above the lower anchor plate rather than axial plastic deformation of the anchor bolts. Accordingly, targeted reinforcement schemes for repairing lower anchor plate deformations and replacing locally crushed concrete are proposed and successfully applied, providing practical reference for engineering applications.

Seismic isolation has become a widely accepted method for the protection of structures and nonstructural components. However, this control strategy is unfavorable against near‐fault earthquakes, particularly those featuring velocity‐pulse effects. Excessive isolation displacements and accelerations can occur during such earthquakes, resulting in amplified responses of the superstructure. To resolve this problem, this study develops a prototype of the eccentric rolling isolation system consisting of one platform eccentrically pin‐connected to four circular rollers. The eccentric pin connection yields a nonlinear restoring force of the proposed system and results in displacement‐dependent resonances, and the inherent mechanical friction also yields an energy dissipation capability of the system. As the magnitude of ground excitation increases, the prototype system generates a lower resonant frequency away from the dominant frequencies of earthquakes. This behavior is experimentally investigated and verified for mechanical behavior and seismic performance. In the experiment, sinusoidal, far‐field, and near‐fault ground motions are considered in shaking table testing. Some parameters, such as the eccentricity, roller size, and inertial force, are also experimentally investigated. As found in the experimental result, the feasibility of the prototype system is successfully verified. Meanwhile, the comparable simulation results further validate the mathematical model of the prototype system. Consequently, the eccentric rolling isolation system has demonstrated isolation effectiveness against far‐field ground motions and has good potential to perform better than a linear system under near‐fault ground motions.

This paper deals with edgewise vibration mitigation of rotating wind turbine blades by means of inerter‐based vibration absorber (IDVA), which can be realized both mechanically and electromagnetically. Introducing the electromagnetically‐realizable IDVA to the blade forms a 3‐degree‐of‐freedom (3‐DOF) blade‐IDVA system consisting of the rotating blade, an absorber, and a series inerter‐dashpot‐spring subsystem. Analytical optimal design formulas of the rotating blade‐installed IDVA are then derived using a pole‐placement method where the equal‐modal‐damping‐ratio principle and the triple‐root‐bifurcation condition are applied. The analytical formulas show that the optimal parameters for the blade‐IDVA system merely depend on the spinning speed of the rotor given the IDVA location and the absorber mass. Numerical results of the NREL 5 MW wind turbine with optimal IDVA show that optimal IDVA leads to superior performance than optimal TMD in mitigating the blade edgewise vibration and behaves nearly as same as optimal RIDTMD, along with slightly optimal damper parameters variation. This means that the inerter‐dashpot‐spring system can be deployed flexibly for damping edgewise vibrations of rotating blades.

As the combination of springs and vertical stabilizers, the movable downward vertical central stabilizer (MDVCS) is proposed to further control the nonlinear flutter of super long‐span suspension bridges in this paper. A series of flutter suppression tests of closed‐box girders and twin‐box steel girders with various MDVCSs are conducted. Based on the coupled flutter theoretical method, the sensitivity analysis of two important parameters including the height and stiffness of MDVCS are carried out to compare their nonlinear flutter control mechanism. The results show that the flutter critical wind speed (Ucr) of the closed‐box girder continued to increase with the decrease of the height of the DVCS and the increase of spring stiffness, whereas the Ucr of the twin‐box girder increased at first and then decreased. The cubic polynomial function and quadratic Holliday function are suitable to modify the correction coefficients of Ucr for the closed‐box girder with various stiffnesses and heights of MDVCS, while the Lorentz peak‐value function and cubic polynomial function are suitable to modify the Ucr of the twin‐box girder. Furthermore, the MDVCS significantly changes the rules of two positive and negative aerodynamic damping ratios for the closed‐box girder and two negative aerodynamic damping ratios for the twin‐box girder. Besides, the peak vertical displacement amplitudes of the box girder are about half of the MDVCS, since both the height and stiffness of MDVCS alter the elliptical radius of vertical phase planes to affect the limit cycle oscillation of soft flutter, especially for the leeward MDVCS for the twin‐box girder.

In this study, a novel delamination detection method for composite materials is proposed through the innovative use of You Only Look Once v8 (YOLOv8), vibration analysis, and 2D continuous wavelet transform techniques. The method detects the location and size of damage more accurately than existing methods and avoids manual intervention in the detection process. Damage detection performed on the simulation dataset shows that the method is able to accurately identify the delamination location with IoU = 0.9906 and an average accuracy of 91.32%. The proposed method is then compared with the widely used YOLOv5 model, and the superior performance of the YOLOv8 model is verified, with a 37.93% improvement in training speed and 0.81% improvement in detection accuracy. In addition, an experimental dataset of four composite laminates with delamination damage is constructed. By using transfer learning, the performance of the pretrained network achieves a good precision up to 1. The method proposed in this study expands the range of tasks that can be accomplished by mode shape analysis and is very effective in real experiments.