With transportation's rapid growth, ship-bridge collisions occur frequently, causing substantial losses. Ship-bridge anticollision facilities should not only protect the structural integrity of bridges but also minimise ship damage. This paper designs a novel ship-bridge anti-collision device based on a trapezoidal foam-filled composite sandwich structure. Using the finite element software LS-DYNA, a ship-anti-collision device-bridge collision model was established, taking into account pile-water-soil coupling. The study investigates the selection of box materials, filling materials and wall thickness for the novel anti-collision device. By analysing the damage characteristics of the ship, anti-collision device and pier under typical collision loads, the optimal material properties were determined. The impact resistance of the optimised device was evaluated under different ship speeds and collision angles, demonstrating that the novel anti-collision device exhibits excellent buffering and energy absorption, effectively reducing the peak collision force, extending the collision duration and reducing damage to the ship's bow structure.

Vessel collisions on bridge piers have become a potential threat to the safety of bridges crossing navigation waterways. Such collision will cause inevitable damage on bridge piers and hence reduce the performance of the whole structure. It is therefore critical to identify the condition of abridge pier after a vessel collision event to judge whether it can still be used or certain rehabilitation is required to recover its normal operation. This paper develops an intelligent approach based on machine learning algorithms to identify the evolution process of damage on abridge pier during collision using sensor-measured acceleration time-history data considering the effects of multi-hazards. A barge vessel is employed and atypical reinforced concrete (RC) bridge pier is considered in this study. A coupled vessel-pier collision model (CVCM) considering soil-pile interactions and material non-linearity of RC components is developed and employed to generate pseudo-experimental data to assess the accuracy of the proposed damage identification strategy. The results demonstrate the potential of the proposed strategy for intelligent damage identification of waterway-crossing bridge piers after vessel collision.

Vessel collisions pose significant threats on the safety of cross-channel bridges. Previous studies have paid little attention on the impact performance of common arch bridges with gravity foundations in inland waterways. This study aims to comprehensively investigate the anti-impact resistance and analyze the damage and failure mechanisms of arch bridges under vessel collisions. The entire process of vessel-bridge collision is simulated using three-dimensional explicit finite element technique. The damage characteristics, as well as the progressive collapse process of arch bridge are investigated thoroughly. Moreover, the rational calculation method for bridge lateral resistance against vessel collisions (BRaVC) is discussed. The results show that the gravity foundation bottom of arch bridge can be fixed in vessel-bridge collision numerical analysis due to insignificant foundation-soil interaction. The head-on barge collision on the bridge pier leads to indistinctive lateral displacement, while obvious local damage can be observed. The impact displacement of the bridge pier is not positively correlated with the impact energy according to the impact load spectra analysis. Barge collision on the main arch results in the progressive collapse of the bridge due to unbalanced horizontal thrust from the arch on the other side. The rational BRaVC can be calculated by using sectional strength based on elastoplastic analysis.