The hybrid simulation (HS) testing method combines physical test and numerical simulation, and provides a viable alternative to evaluate the structural seismic performance. Most studies focused on the accuracy, stability and reliability of the HS method in the small-scale tests. It is a challenge to evaluate the seismic performance of a twelve-story pre-cast reinforced concrete shear-wall structure using this HS method which takes the full-scale bottom three-story structural model as the physical substructure and the elastic non-linear model as the numerical substructure. This paper employs an equivalent force control (EFC) method with implicit integration algorithm to deal with the numerical integration of the equation of motion (EOM) and the control of the loading device. Because of the arrangement of the test model, an elastic non-linear numerical model is used to simulate the numerical substructure. And non-subdivision strategy for the displacement inflection point of numerical substructure is used to easily realize the simulation of the numerical substructure and thus reduce the measured error. The parameters of the EFC method are calculated basing on analytical and numerical studies and used to the actual full-scale HS test. Finally, the accuracy and feasibility of the EFC-based HS method is verified experimentally through the substructure HS tests of the pre-cast reinforced concrete shear-wall structure model. And the testing results of the descending stage can be conveniently obtained from the EFC-based HS method.

Large waves pose a significant threat to the safety of low-laying coastal bridges. In 2004 and 2005, many coastal bridges were destroyed by the large waves generated by Hurricane Ivan and Katrina. A series of studies have been conducted to investigate the wave action on bridge decks. However, the effect of seabed topography on wave forces is seldom considered. In this study, a numerical model of wave impact on bridge decks located close to a sloping seabed was set up based on OpenFOAM-v1912. The reliability of the present numerical model was checked against published experimental results. The wave forces acting on the bridge decks located before and after the sloping seabed were compared with a bridge deck located above a flat bottom. With the validated numerical model, the effect of the wave properties, coefficient of submergence, and the inclination on wave forces were investigated. The results show that the wave forces on the bridge are affected by the interaction among the incident and reflected waves, the position of the bridge relative to the sloping seabed, and the inclination. These findings emphasize the complex dynamic effects of wave forces on bridges and the importance of considering seabed topography in bridge design and strengthening strategies. This research contributes to an improvement in the safety of coastal bridges, especially in the face of increasing threats from climate change and sea level rise, as well as promotes the development of more resilient infrastructures.

As civil engineering projects grow increasingly complex, ensuring pile integrity is essential for pile bearing capacity and structural safety. Pile integrity testing (PIT) has long been a focal point for researchers and engineers. With the rapid development of industrial-level advancements and artificial intelligence technology, PIT methods have undergone significant technological advancements. This paper reviews traditional PIT techniques, including low-strain integrity testing and thermal integrity profiling. The review covers the principles, advantages, limitations, and recent developments of various testing techniques. Additionally, recent advances in artificial intelligence (AI) techniques, particularly in signal processing and data-driven recognition methods, are discussed. Finally, the advantages, limitations, and potential future research directions of existing methods are summarized. This paper aims to offer a systematic reference for researchers and engineers in PIT, synthesizing technical details of traditional methods and their AI-enabled advancements. Furthermore, it explores potential directions for integrating AI with PIT, with a focus on key challenges such as noisy signal interpretation and regulatory barriers in applications.

In some extreme corrosion environments, the erosion of chloride ions and carbon dioxide can occur simultaneously, causing deterioration of reinforced concrete (RC) structures. This study presents a probabilistic model for the sustainability prediction of the service life of RC structures, taking into account that combined deterioration. Because of the high computational cost, we also present a series of simplifications to improve the model. Meanwhile, a semi-empirical method is also developed for this combined effect. By probabilistic generalization, this simplified method can swiftly handle the original reliability analysis which needs to be based on large amounts of data. A comparison of results obtained by the models with and without the above simplifications supports the significance of these improvements.