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Top1. Introduction
Autoclaved aerated concrete (AAC) slab is a new type of light and porous green building material, which is made of cement, lime and sand as the main raw materials and is equipped with different numbers of anticorrosive steel mesh according to the structural performance requirements. It has the advantages of light weight, fire resistance, sound insulation, heat insulation and so on. At present, the reinforced concrete plate with autoclaved aerated concrete plate used to strengthen the bending resistance and crack resistance of the autoclaved aerated concrete plate has been more and more applied in the high-rise building structures and industrial factory buildings. The main structures of autoclaved aerated concrete partition board are thin shell structure, cast-in-place large formwork structure and various kinds of aerated concrete interlayer structure with sliding membrane. Autoclaved aerated concrete partition board is mainly used to optimize the tensile strength. Autoclaved aerated concrete partition board has high structural strength, good durability, high temperature resistance, saving steel raw materials and lower cost than single concrete partition board (Kai X , Qingshan D , Lujun C , et al.(2018)). The applicable regulations for the design of autoclaved aerated concrete slab in China mainly include JGJ/T17-2008 "Technical Regulations for the application of autoclaved Aerated Concrete slab" and GB 15762-2008 "Autoclaved Aerated Concrete Slab". These two regulations and standards have made relevant provisions for the inspection of autoclaved aerated concrete products and the test and measurement of material performance, but only for the design of the reinforcement of autoclaved aerated concrete slabs for designers to refer to the guiding provisions. In the actual production process, it is necessary for the manufacturer to refer to the regulations for the commonly used plate specifications for reinforcement design, theoretical reinforcement design results can be compared and verified through experimental research and finite element simulation analysis, so as to promote the large-scale industrial production of autoclaved aerated concrete slab and its application in practical engineering.
In literature (Kamil G M , Liang Q Q , Hadi M N S. (2018)), HPB300 circular steel bar and CRB600H high-ductility cold-rolled ribbed steel bar were used as floor bearing steel bars, and the experimental study on bending performance was carried out by using the centralized loading method. The crack condition of autoclaved aerated concrete floor slab is observed, and the strain of autoclaved aerated concrete and steel bar, as well as the mid-span deflection of floor slab are measured. However, this method cannot obtain the yield conditions of autoclaved aerated concrete partitions, which leads to the unconvincing bending performance results of the partitions. Literature (Zhang D , Gu X L , Yu Q Q , et al. (2018)) established a three-dimensional finite element numerical model of autoclavic aerated concrete clapboard based on ABAQUS software, and compared the simulation results of mid-span deflection, cracking load and ultimate load with the test results to verify the rationality of the finite element model. The influence of width, thickness, length and reinforcement ratio of autoclaved aerated concrete slab on its mechanical properties was further studied. However, the study neglected the analysis of the relationship between the joint force and the joint displacement of the concrete partition slab, which led to some limitations of the experimental results. Literature (Ding F X , Luo L , Wang L , et al.(2018)) proposed that in the process of quality inspection of autoclavic aerated concrete, inspectors should conduct visual evaluation of defects according to experience, skills and engineering judgment. However, this process is subjective, laborious, time-consuming, and hampered by requiring access to many parts of a complex structure. Aiming at this problem, an almost automatic detection model based on image processing and deep learning is proposed to detect the defects in typical inaccessible areas in concrete structures, so as to improve the compressive performance of autoclac aerated concrete partitions. However, this method does not calculate the stiffness of autoclaved aerated concrete, so the effect of improving the bearing capacity is not obvious.