A set of benchmark, medium scale, shaking table tests on corroded reinforced concrete (RC) columns is conducted with the aim of investigating the effects of corrosion damage on the nonlinear dynamic behaviour of RC bridge piers. The experimental programme consists of an uncorroded control specimen and two corroded RC column specimens, with identical structural details. An accelerated corrosion procedure is used to corrode the RC columns. The uncorroded and corroded specimens are subjected to far-field long duration ground motion excitations. The two corroded columns had 51% and 65% average mass loss ratios. The testing sequence includes slight, extensive, and complete damage levels, followed by an aftershock to examine the cascade effect on the nonlinear dynamic response of the proposed RC columns. The experimental results show that corrosion changes the failure mode of the RC columns, and has a significant negative impact on the residual strength (about 50% mass loss results in about 80% strength reduction) and drift capacity of RC columns.

This paper reports the results of a set of benchmark medium-scale shaking table tests to investigate the significance of the non-stationary characteristics of ground-motion on nonlinear dynamic responses and the structural damage of reinforced concrete (RC) columns. To examine the influence of ground-motion characteristics, four RC columns are tested under (1) near-field without pulse, (2) near-field pulse-like, and (3) far-field ground-motions. These ground-motion records were spectrally matched by the reweighted Volterra series algorithm without changing non-ergodic characteristics. To explore the confinement effects, two sets of column specimens are designed to represent the modern well-confined and older lightly-confined RC columns. Each column is tested in slight, extensive and complete damage limit states. Then aftershock excitations are conducted to investigate the performance of severely damaged RC columns. Low amplitude white-noise tests are conducted on pristine columns and after each damage limit state experiment to detect natural frequency variant of damaged columns using transfer function estimate. Furthermore, using time–frequency analysis, the real-time variant frequency of test specimens is estimated. The significant duration of ground-motions accounting for the effect of non-stationary characteristics of ground-motion is also estimated by time-cumulative damage analysis of the test results. Finally, the time-variant stiffness degradation of RC columns is estimated.

The role of signal-based nonlinear system identification methods for the rapid post-earthquake damage assessment of reinforced concrete (RC) bridge piers is explored. Experimental data from the shaking table tests of six RC columns with and without corrosion damage are used as benchmark data. The specimens are excited under three different ground motions with different time-series characteristics, structural detailing, and corrosion levels. The proposed system identification methods make use of accelerations alone (but not displacements as these are costly in-situ) to estimate the instantaneous frequency. The Wigner-Ville distribution and Hilbert transform are utilised due to their high resolution in both time and frequency domains. A combination of modal filtering and thresholding, using instantaneous amplitudes, is employed to attenuate the unreliable spikes in the Hilbert transform’s instantaneous frequency estimates. Their performance is benchmarked against a moving linear regression and standard white-noise tests. The comparison of the experimental results and time-frequency analysis indicates that the Wigner-Ville distribution and the Hilbert transform can produce reliable rapid damage detection when the response amplitude is large. The Wigner-Ville distribution has better robustness and higher resolution. The robustness of the more computationally efficient Hilbert transform can be significantly improved by the introduction of modal filtering and thresholding.

The buckling mechanism of diagonally stiffened plates under the combined action of shear, bending, and compression is a complex phenomenon that is difficult to describe with simple and clear explicit expressions. Predicting the elastic buckling coefficient accurately is crucial for calculating the buckling load of these plates. Several factors influence the buckling load of diagonally stiffened plates, including the plate’s aspect ratio, the stiffener’s flexural and torsional rigidity, and the in-plane load. Traditional analysis methods rely on fitting a large number of finite element numerical simulations to obtain an empirical formula for the buckling coefficient of stiffened plates under a single load. However, this cannot be applied to diagonally stiffened plates under combined loads. To address these limitations, several machine learning (ML) models were developed using the ML method and the SHAP to predict the buckling coefficient of diagonally stiffened plates. Eight ML models were trained, including decision tree (DT), k-nearest neighbor (K-NN), artificial neural network (ANN), random forest (RF), AdaBoost, LightGBM, XGBoost, and CatBoost. The performance of these models was evaluated and found to be highly accurate in predicting the buckling coefficient of diagonally stiffened plates under combined loading. Among the eight models, XGBoost was found to be the best. Further analysis using the SHAP method revealed that the aspect ratio of the plate is the most important feature influencing the elastic buckling coefficient. This was followed by the combined action ratio, as well as the flexure and torsional rigidity of the stiffener. Based on these findings, it is recommended that the stiffener-to-plate flexural stiffness ratio be greater than 20 and that the stiffener’s torsional-to-flexural stiffness ratio be greater than 0.4. This will improve the elastic buckling coefficient of diagonally stiffened plates and enable them to achieve higher load capacity.