Piers and bearings influence each other in earthquakes, and the failure of any component will affect the whole function of bridges. Thus, it is critical to consider the correlations between multiple components in the seismic vulnerability analysis of the overall bridge system. In this paper, a method of overall seismic vulnerability analysis with nested copula model was proposed to model the correlations between multiple components. The components were combined according to their types to construct the hierarchical structure of the system. The correlation of components was modeled with copula functions, and the nested copula model of the system was eventually developed. Maximum likelihood estimation and goodness-of-fit test were used to select and optimize the copula functions. The overall seismic vulnerability of the multispan bridge system was established using the nested copula model. A four-span continuous rigid frame bridge was used to illustrate the application of the proposed method. The results show that the nested copula model accurately simulated the correlations between multiple components, which would eliminate the overestimation or underestimation by the first-order boundary method.

Finite element model modification based on response surface method is an effective engineering method that can significantly improve the accuracy of finite element model and structural evaluation. In order to solve the shortcomings of the response surface method based finite element model correction, which exists that the polynomial order selection rule is unknown and the correction result is easily affected by the quality of the optimization target, based on the relationship between the polynomial order and the response surface accuracy and computation amount, a robust estimation algorithm was introduced to optimize the response surface solution process, which improves the reliability of the response surface based model correction. Taking an actual project of a cable‐stayed bridge as an example, the response surface‐based finite element model modification method was adopted to compare the response surface accuracy and computation amount of three kinds of response surface models, namely, second‐mode polynomials, third‐mode polynomials and fourth‐mode polynomials. The optimization results of the robust estimation method were compared with the traditional solution method under the conditions of large fluctuation and small fluctuation of optimization target between finite element index. The results show that with the increase of the polynomial order of the response surface model, the unknown covariates to be solved in the response surface model increase sharply, which increases the computational cost, but the effect of improving the accuracy of the response surface model is limited. When the target frequency fluctuates greatly, compared with the least‐squares method, robust estimation will not transfer the error of the correction result of the order with the large fluctuation of the index difference to the other orders, which ensures the stability of the correction results.

Bridges are prone to damage from various factors, impacting the overall safety of transportation networks. Accurate damage identification is crucial for maintaining bridge integrity. This study proposes a novel method using encoded images and a convolutional neural network (CNN) for bridge damage identification. By converting raw acceleration data into encoded images, the data can be represented from multiple perspectives, enhancing the extraction of essential features related to bridge damage states. The method was validated using data simulated from a continuous rigid-frame bridge model. The results demonstrate that using encoded images as inputs yields a higher recall rate, precision, and F1-score compared to using acceleration responses as inputs, achieving a comprehensive accuracy of 92%. This study concludes that the combination of encoded images and CNN provides a robust approach for accurate and efficient bridge damage identification.

For medium- and small-span bridges, the weight of the superstructure in steel–concrete composite girder bridges is lighter than that of a reinforced concrete girder bridge. However, it is still uncertain whether steel–concrete composite girder bridges exhibit superior seismic performance compared to reinforced concrete girder bridges. This study quantitatively compared the seismic performance of the two types of bridges. Using the theory of probabilistic seismic demand analysis, the seismic vulnerability curves of bridges were derived. To conduct seismic demand analysis for probabilistic analysis on the OpenSEES platform, bridge samples were generated using the Latin hypercube stratified sampling method, which considers the uncertainties associated with the two types of bridges. The vulnerability curves of the piers, bearings, abutments, and the system of the two bridges were established using probabilistic analysis of the time history analyses. The results showed that the seismic vulnerabilities of components and the overall system of the steel–concrete composite girder bridge were both lower than those of the reinforced concrete girder bridge. When the peak ground acceleration (PGA) of the ground motion was 0.3 g, the moderate and serious damage probabilities of the piers in the steel–concrete composite bridge were only 54.61% and 60.89%, respectively, of those of the reinforced concrete bridge. Consequently, replacing the upper reinforced concrete girders with steel–concrete composite girders can significantly improve the seismic performance of a large number of existing bridges.

Water pollution exacerbates water stress and poses a great threat to the ecosystem and human health. Construction and demolition waste (CDW) due to rapid urbanization also causes heavy environmental burdens. A major proportion of CDW can be effectively converted into recycled aggregates, which can be reused in many fields, including environment remediation. In this study, a nano ecological recycled concrete (nano-ERC) was produced with recycled aggregates and copper oxide nanoparticles (nCuO) to remove heavy metals (HMs) from a simulated wastewater effluent (SWE) for further treatment. Recycled aggregates were obtained from CDW, thereby simultaneously reducing the treatment cost of the SWE and the environmental burden of solid waste. The adsorption capacity of nano-ERC was presumed to be significantly enhanced by the addition of nCuO due to the unique large surface-to-volume ratio and other properties of nanoparticles. The SWE containing five common HMs, arsenic (As), chromium (Cr), cadmium (Cd), manganese (Mn) and lead (Pb), was filtered through a control ERC and nano-ERCs, and the concentrations of these HMs were determined with ICP-MS in the SWE and the filtrates. Results showed the nano-ERCs could significantly remove these HMs from the SWE compared to the control ERC, due to the enhanced adsorption capacity by nCuO. The relative weighted average removal percentage (RWAR%) was in the range of 53.05–71.83% for nano-ERCs and 39.27–61.65% for control ERC. Except for Cr, concentrations of these HMs in the treated wastewater effluent met the requirements for crop irrigation or scenic water supplementation; the Cr may be removed by multiple filtrations. In conclusion, nano-ERC can serve as a cost-effective approach for the further treatment of wastewater effluent and may be applied more widely in wastewater treatment to help relieve water stress.

To study the strengthening effect of carbon fiber composite materials (CFRP) on recycled concrete columns subjected to different levels of seismic damage, four column specimens were designed for pseudo-static tests. The four specimens were categorized as non-destructive without strengthening (prototype), non-destructive strengthening, medium seismic damage strengthening, and severe seismic damage strengthening based on the replacement rate of recycled aggregates and the level of seismic damage. The characteristics of the deformation damage and seismic performance indicators of each specimen were compared and analyzed. The results were verified on the OpenSees platform. A decrease was observed in the initial stiffness of the seismically damaged recycled concrete column specimens strengthened with CFRP, while the ductility, peak bearing capacity, and energy dissipation capacity of the specimens were improved. In addition, with the reduction in seismic damage, the ductility and energy dissipation capacity of the strengthened seismically damaged recycled concrete column specimens were enhanced to different degrees. In particular, the cumulative energy dissipation of the strengthened specimens exposed to medium seismic damage increased most significantly, by 32.5%. In general, the hysteretic curves of the strengthened specimens were full, and the average ductility coefficients were 4.1–6.8. CFRP strengthening was more effective for restoring and enhancing the performance of the recycled concrete column specimens with medium and lower seismic damage levels (displacement ratio ≤ 3%).