| Cold-formed steel framed structures with composite shear walls have the advantages of environmental protection,energy saving,and quick construction.The development of mid-rise cold-formed steel structures meets the needs of prefabricated buildings in China.When it is promoted and applied in practice,the study of strong earthquake collapse of mid-rise cold-formed steel structures is a key issue.Using artificial intelligence technology to analyze and predict the seismic performance of new structures based on the results of a large number of existing tests or numerical simulations has a high scientific and technological value.Cellular Automata(CA)technology has been used to predict the collapse of mid-rise cold-formed steel structures under strong earthquakes,but this method has a large prediction error for vertically irregular mid-rise cold-formed steel structures.On this basis,vertical irregular mid-rise cold-formed steel structures are taken in this paper as the research object,and the collapse prediction model of the structure is established by improving the original CA method and introducing the Relevance Vector Machine(RVM)technology.This study makes full use of artificial intelligence technology to investigate the collapse behavior of mid-rise cold-formed steel structures.In order to verify the universal applicability of the prediction method proposed in this paper,three types of story drift angle limits(2.0%,3.5% and 4.0%)are selected to determine the structure collapse.Considering that the test data of vertically irregular mid-rise cold-formed steel structures is less and based on the lateral performance tests of cold-formed steel composite shear wall and the simplified model of the wall completed by this research group,the numerical analysis results of the two-dimensional simplified model of mid-rise cold-formed steel structures are taken as the real collapse values to verify the reliability of the prediction method.According to the characteristics of vertically irregular mid-rise cold-formed steel structures that have sudden changes in stiffness and enter the elastoplastic stage under strong earthquakes,this paper revises the cell space,cell state values and matching criteria of the original CA method,and proposes an improved CA model.Among them,the neighbor cells in the cell space are increased to 2,and the calculation formula of the matching criterion is revised accordingly;two kinds of cell state values are defined: the normalized results of the equivalent mode of the structure and the normalized results of the story drift after Pushover analysis.The improved CA method corresponding to the two cell state values and the original CA method were used to predict the collapse of the 5-layer structure to be predicted,and the predicted results were compared with the results of time-history analysis.The results show that under the three collapse criteria,the prediction errors of the improved CA method corresponding to the normalized equivalent mode values defined cell state values and the original CA method are mostly within ±40%,and the maximum absolute error is more than 78%,which is too large.Compared to the CA method using elastic stage characteristic value as cell status value,the improved CA method put forward in this paper using the normalized Pushover story drift of the elastic-plastic stage eigenvalues as cell state values has a prediction error of ±20%,where absolute errors are no more than 32%.This method obviously improves the prediction accuracy of collapse of vertically irregular mid-rise cold-formed steel structures.Considering that the sudden change of structural rigidity will affect the story drift when the vertically irregular mid-rise cold-formed steel structure collapses,this paper uses relevance vector machine to establish the mapping relationship between the structural inter-story stiffness and the collapsed story drift.Gaussian kernel function and fast sequence sparse Bayesian algorithm are selected as the core part of the RVM model.The inter-story stiffness and the vertical stiffness variation of the structure are taken as the input of the model,and the time history analysis results of the displacement between the floors when the structure collapses are taken as the output of the model to train the RVM model.The collapse prediction of strong earthquakes is carried out for the 4-story vertically irregular cold-formed steel structure,and the prediction results are compared with the time history analysis results.The results show a good prediction accuracy under the three collapse criteria since the absolute value of the prediction error of the RVM method does not exceed 34%,and most of them are within 20%.Aiming at the problem that it is difficult to obtain more accurate prediction results from CA method through matching criteria due to the small number of samples,this paper uses relevance vector machine to replace the matching criteria of improved CA method,and thus to realize the joint prediction of CA and RVM.The cell state value(the story drift after structure normalization Pushover analysis)is used as the input data of the joint model,and the time history analysis results of the story drift when the structure collapses are used as the output data of the model.For the 6-story vertically irregular cold-formed steel structure,the combined method,the improved CA method,the RVM method and the original CA method were used to predict the collapse of strong earthquakes,and the prediction results were compared with the time history analysis results.The results show that under the three collapse criteria,most of the prediction errors of the original CA method are within ±40%,but the absolute value of the maximum error is more than 70%.The prediction errors of the improved CA method and RVM method are mainly distributed within ±20%,and the absolute value of the maximum error is no more than 40%,which is within the acceptable range.Compared with the above methods,the combined method of CA and RVM proposed in this paper has the highest prediction accuracy,with the prediction error mainly distributed within ±10% and the maximum absolute error less than 15%.This method can accurately obtain the story drift of the structure to be predicted under the action of different seismic waves.The prediction results show that the three prediction methods proposed in this paper are feasible and applicable for the collapse prediction of vertically irregular mid-rise cold-formed steel framed structures with composite shear walls under strong earthquakes.In the practical application,the prediction method can be selected according to the input data(inter-layer stiffness or story drift after Pushover analysis),training process(matching criterion or relevance vector machine)and prediction accuracy. |
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