This paper investigates the efficiency of three surrogate model-based dynamic finite element model updating methods, the response surface, the support vector regression, and the radial basis function neural network, using the engineering background of the Tsing Ma Bridge tower. The influences of two different sampling methods (central composite design sampling and Box–Behnken design sampling) on the model updating results are also assessed. It was deduced that the impact of the surrogate model type on the updating results is not significant. More precisely, the models updated using the response surface method and the support vector regression method are similar in terms of reproducing the dynamic characteristics of the physical truth. However, the effects of the employed sampling method on the model updating results are significant as the model updating quality using the central composite design sampling method is higher than that using the Box–Behnken design sampling method in some considered cases.

For this paper, full-scale/model test comparisons to validate the traditional atmospheric boundary layer (ABL) wind-tunnel simulation technique performed until now by the wind engineering community are systematically reviewed. The engineering background includes some benchmark low-rise buildings specifically established for use in wind engineering research (the Aylesbury experimental buildings, the Texas Tech University experimental building, the Silsoe buildings, etc.), several high-rise buildings in North America and East Asia, long-span bridges, large-span structures, and cooling towers. These structures are of different geometries, are located in different wind environments, and are equipped with various transducers and anemometers. By summarizing the different articles in the literature, it is evident that notable discrepancies between the full-scale measurement and the model test results were observed in most full-scale/model test comparisons, which usually took certain forms: the mean and/or the peak negative pressures at the flow separation regions on buildings were underestimated in the wind tunnel; differences in the root-mean-square (rms) values of the acceleration samples between the full-scale measurements and the force balance model tests were non-negligible; the vertical vortex-induced vibration amplitudes of bridges measured using section models and aero-elastic models were much lower than those observed on the prototypes, etc. Most scholars subjectively inferred that inherent technical issues with the ABL wind tunnel simulation technique could be responsible for the observed full-scale/model test discrepancies, including the Reynolds number effects, the turbulent flow characteristics effects, and the non-stationarity effects. However, based on the authors’ years of experience and after discussion with experienced researchers, it was found that some of the full-scale measurements performed in earlier research were inherently less accurate and deterministic than the wind tunnel experiments they were supposed to validate, which could also be a significant cause of the full-scale/model test discrepancies observed. It is suggested herein that future studies in this field should regard full-scale measurements only as benchmarks, and that future works should focus on synthesizing the results from different schools of physical experiments and formulating universal empirical models of high theoretical significance to properly validate future wind tunnel tests.

To rigorously evaluate the health of a steel bridge subjected to vehicle-induced fatigue, both a detailed numerical model and effective fatigue analysis methods are needed. In this paper, the process for establishing the structural health monitoring (SHM)-oriented finite element (FE) model and assessing the vehicle-induced fatigue damage is presented for a large, specially shaped steel arch bridge. First, the bridge is meticulously modeled using multiple FEs to facilitate the exploration of the local structural behavior. Second, manual tuning and model updating are conducted according to the modal parameters measured at the bridge’s location. Since the numerical model comprises a large number of FEs, two surrogate-model-based methods are employed to update the model. Third, the established models are validated by using them to predict the structure’s mode shapes and the actual structural behavior for the case in which the whole bridge is subjected to static vehicle loads. Fourth, using the numerical model, a new fatigue analysis method based on the high-circle fatigue damage accumulation theory is employed to further analyze the vehicle-induced fatigue damage to the bridge. The results indicate that manual tuning and model updating are indispensable for SHM-oriented FE models with erroneous configurations, and one surrogate-model-based model updating method is effective. In addition, it is shown that the fatigue analysis method based on the high-circle fatigue damage accumulation theory is applicable to real-world engineering cases.

As an important issue for establishing structural health monitoring–oriented finite element models for large steel pylons, identification of beam-end stiffness draws the attention of the engineering circle. Since the methods adopted by other researchers for parameter identification are impracticable, a beam-end stiffness identification method which combines in situ measurements for the structure’s global dynamic properties with effective multi-variable optimization methods is utilized to improve the accuracy of established finite element models. A 131-m-high large transmission tower is employed as a case study to validate the method. In situ measurements for the tower’s global dynamic characteristics are performed, and identifications of Young’s modulus for 20 semi-rigid connections distributed along each of the tower’s four main chords are undertaken utilizing three multi-variable optimization methods, that is, the first-order method, the subproblem approximation method, and the response surface method. Static numerical simulations on two detailed connection models prove that multiple uncertain parameters can be correctly and simultaneously identified when appropriate optimization techniques are chosen. Finally, the influence of beam-end stiffness identification on the structural health monitoring–oriented structural safety assessment is revealed by calculating the wind-induced dynamic structural responses for the single tower and the transmission tower-line system, which indicates that identification of the correct beam-end stiffness and updating the structural health monitoring–oriented finite element model are indispensable procedures for reliable structural health monitoring.