Highway bridges play a significant role in maintaining safety and functionality of the society. The immediate damage of highway bridges caused by natural hazards can disrupt transportation systems, impede rescue and recovery activities. This disruption may result in tremendous financial and societal losses. Therefore, assessing vulnerability, recovery capability, and potential losses of bridges under natural hazards becomes a primary concern to decision-makers to facilitate the emergency response and recovery efforts. Under these concerns, resilience is a paramount performance indicator to evaluate and recover the functionality of structural systems under extreme events. In this article, an integrated framework for long-term resilience and loss assessment of highway bridges under multiple independent natural hazards is presented. The impacts of extreme events such as earthquakes, hurricanes and floods on the life-cycle performance of bridges are illustrated. A stochastic renewal process model of the random occurrence of hazard events is used to compute the expected long-term resilience and damage loss by considering both time-independent and time varying occurrence characteristics. The proposed approach is applied to a bridge by considering the impacts of earthquake and hurricane hazards. The proposed framework can be implemented to the design, maintenance and retrofit optimisation of infrastructure systems under multiple extreme events.

It is of vital importance to incorporate sustainability and resilience in the performance-based decision-making of civil infrastructure under seismic hazard. However, a performance-based engineering framework utilizing component-level approach, integrating seismic loss, sustainability, and resilience in a multi-criteria decision-making (MCDM) is not yet extensively developed particularly for retrofit selection in a long-term perspective. This paper introduces a framework utilizing performance-based approach to couple seismic loss, sustainability, and resilience in decision-making framework for selection of different retrofit alternatives. A component-based probabilistic approach is developed for the seismic loss, sustainability, and resilience assessment in a long-term perspective. The resulting social, economic, and environmental consequences are converted to expected annual consequences (EACs) by considering the full range of seismic hazards, and are utilized as a multi-criterion in the technique for order preference by similarity to ideal solution (TOPSIS). The proposed long-term performance-based multi-criteria decision-making (PB-MCDM) framework considers five consequences, which include cost, casualties, equivalent carbon emissions, embodied energy, and repair time. Based on the illustrative example, it may be concluded that incorporating seismic sustainability and resilience in PB-MCDM approach in a long-term perspective can provide ideal solutions and better decision-making against retrofit alternatives.

This paper proposes a comprehensive analysis framework, combining three-dimensional (3D) numerical modelling and metamodeling, to investigate the probabilistic performance of retrofit actions on coastal bridges subjected to extreme wave forces. Specifically, a 3D Computational Fluid Dynamics (CFD) model is developed to calculate extreme wave load on the bridge superstructure. The established 3D model is validated by laboratory experiments. The characteristics of wave forces are parametrically investigated, and an Artificial Neural Network (ANN) metamodel is utilised to quantify the loading effects with multiple surge and wave parameters. Such a numerical-based ANN metamodel could predict wave forces under variable scenarios accurately, and significantly reduce the high computational cost of the 3D numerical model. Based on the numerical and metamodeling results, the bridge fragility curve is derived by considering uncertainties associated with structural demand, capacity, and hurricane hazard. Long-term failure risk is assessed under different climate change scenarios. Furthermore, different retrofit methods to improve structural performance and reduce failure risk are examined according to the proposed framework, including inserting air venting holes, enhancing connection strengths, and elevating bridge structures. The proposed framework could facilitate the optimal and robust design and maintenance of coastal infrastructures under hurricane effects in a long-term time interval.

Bridges, especially cable-stayed bridges, play an important role in modern transportation systems. The safety status of bridge cables, as an important component of cable-stayed bridges, determines the health status of the entire bridge. As a non-destructive real-time detection technology, acoustic emission has the advantages of high detection efficiency and low cost. This paper focuses on the issue that a large amount of data are generated during the process of health monitoring of bridge cables. A novel acoustic emission signal segmentation algorithm is proposed with the aim to facilitate the extraction of acoustic emission signal characteristics. The proposed algorithm can save data storage space efficiently. Moreover, it can be adapted to different working conditions according to the adjustment of parameters in order to accurately screen out the target acoustic emission signal. Through the acoustic emission signal acquisition experiments of three bridges, the characteristics of the noise signal in the acquisition process are extracted. A comprehensive analysis of the signal in the time domain, frequency domain and time-frequency domain is carried out. The noise signal filtering parameter thresholds are proposed according to the analysis results.