To study the influence of masonry infill walls on the hysteretic performance of reinforced concrete frames, a cyclic experiment was conducted for three two-story and two-span reinforced concrete frame structures, including one reinforced concrete frame without infill walls and two frames with infill walls. Whether the infill walls were constructed in the frames and the type of infilled material were the main parameters of the test. The major results reveal that: the infill walls clearly changed the mechanical mechanism of the frame structure at the early stage of loading, magnified the stiffness and horizontal bearing capacity of the frame structure, and enhanced the energy dissipation capacity of the frame structure, but reduced the deformation performance of the frame structure. In the later stage of loading, the infill walls would no longer work as one with the frame gradually with the failure of the infill walls, and the above performance of the structure would approach the empty frame structure. Moreover, the initial stiffness, energy dissipation capacity, and horizontal bearing capacity of the frame with infill walls of clay hollow bricks were the highest among the three specimens. But due to the strong diagonal bracing effect, the damage to the top of the columns and beam-column joints was serious, the yield displacement was reduced significantly, and the shear failure of the top of the columns and the joints occurred prematurely, which showed poor performance of deformation and ductility. However, the frame with infill walls of relatively soft aerated lightweight concrete blocks showed better performance of deformation and ductility.

Artificial intelligence is predicted to play a big part in self-learning, industrial automation that will negotiate the bandwidth of structural health and control systems. The industrial structural health and control system based on discrete sensors possesses insufficient spatial coverage of sensing information, while the distributed condition monitoring has been mainly studied at the sensor level, relatively few studies have been conducted at the artificial intelligence level. This paper presents an innovative method for distributed structural health and control systems based on artificial intelligence. The structural condition was divided into regional and local features, the feature extraction and characterization are performed separately. Structural abnormality recognition and risk factor calculation method were proposed by considering the response values and the distribution patterns of both the regional and the local structural behaviours. The test results show that the method can effectively identify the full-scale and local damage of the structure, respectively. Subsequently, structural safety assessment method for long-span structures at kilometres level in view of fully length strain distributions measured by distributed fiber optic sensors were developed. A series of load tests on the long-span structure were carried out. Finite element (FE) model was developed using finite element code, ABAQUS, and an extensive parametric study was conduct to explore the effect of load cases on the structural responses. The differences in the structural response results among load test, structural safety assessment and FE simulation were investigated. It is shown that AI-based self-learning system could offer suitable speed in deployment, reliability in solution and flexibility to adjust in distributed structural health monitoring and control.