Prediction For Failure Mode Of Grouted Concrete Block Wallette With Uniaxial Compressive Stress

With the development of society, science and technology, more and more new masonry types have gotten rid of the stale and brought forth the fresh. Because of its drawback of low strength, high energy expense, and consuming great soil, the brick masonry gradually loses the leading status. Concrete small hollow block, a kind of high technology mechanization wall materials, has the merits of high strength, protecting soil and forming reinforced masonry by placing the rebar in the cavity. The new masonry type is suitable to build multi-storey, media-storey and high storey structure, and can achieve great social benefit. So concrete small hollow block is gradually instead of brick masonry becoming the basic materials of infilled wall of building structure especially masonry structure.Grouted concrete block wallette is made of block, mortar and concrete. Compared with plain concrete, it exhibits distinct directional characteristics on account of the different properties of the anisotropy of the block, its component materials and the regular layout pattern. Its behavior is different from brick masonry and concrete. It is reasonless to use the two latter's constitutive relationship and failure criterion in design of GCBM. Therefore in this paper, non-traditional technology is considered to predict failure modes of masonry wallettes.Firstly, this paper establishes the CA model, which describes the influence of boundary load on each unit of a predicted wallette, of the predicted wallette whose material axis are not accordance with that of the base wallette. It develops the former method, which only predicts the wallette whose material axis are accordance with that of the base wallette by considering to map the transfer functions of predicted wallette to the material axis of the base wallette with the trigonometric function.Secondly, a virtual wallette is proposed in this paper and used in the prediction of the failure mode of a masonry wallette.Then, in order to improve drawbacks of two methods mentioned above, the united method which integrates both of advantages is proposed. Loading cases, configuration of wallette and variation in masonry property which are important factors affecting the behavior of masonry wallettes are considered in predicting the failure modes. Based on the experimental failure mode of base wallette, failure modes of new wallettes are obtained, by using the proposed CA model and the rules for matching similar zones: if a zone in the base wallette is cracked, its similar zone in the new wallette is also assumed to have cracked.At last, this paper establishes BP model of failure modes of small grouted concrete block masonry wallettes, using the technique of cellular automata (CA) and artificial neural networks (ANN). In the structure of BP neural networks in which the optimal hidden lawyers and learnrate are obtained through repetitive training, x and y coordinates of each unit divided in the wallette, corresponding value of transfer function and clamp between two material axis of new wallettes and base wallette are set as input data and whether the unit is failure (0 is no-failure, 1 is failure) is set as output data. The failure modes predicted by neural networks are much closer to their corresponding experimental failure modes. So it is feasible that BP networks predict the failure mode of masonry wallette.It is a new way to predict the failure mode of small grouted concrete block masonry wallette by CA and ANN.