Monitoring Data Based Model Updating And Rapid Seismic Damage Simulation Of Urban Buildings

A large number of population and wealth are gathered in cities.Once an earthquake occurs in cities,it is very likely to cause serious casualties and economic losses.Timely and accurate simulation of earthquake damage is of great significance for disaster preparedness,disaster mitigation and earthquake emergency response.In order to further improve the accuracy and speed of seismic damage simulation of urban buildings,this paper conducts research on the following two aspects based on the method for seismic damage simulation of urban buildings.On the one hand,the model updating method for urban buildings using the structural monitoring data is researched to improve the accuracy of the analysis model;on the other hand,two rapid earthquake damage simulation methods for urban buildings are established by using scenario matching method and clustering algorithm.The main contents of this paper are as follows.(1)From the perspective of improving the accuracy of the urban buildings model,a regional-level model updating method for urban buildings based on monitoring data is established,and the model update of the entire building group is realized by using monitoring data of a limited number of monitored buildings.First,the posterior modal of the densely monitored structure is applied in the model update of the sparsely monitored structure by introducing the similarity relationship of the modal shapes between the two structures,the efficiency and accuracy of model updating are improved effectively.Then,an update method for the empirical value formula of the parameters in urban buildings model is proposed based on Bayesian theory,and further relying on the updated empirical value formula,the update method of model parameters of the unmonitored structures is established,and the model updating of urban buildings based on limited structural monitoring data is realized.Finally,a numerical example is used to study the influence of the algorithm parameter setting on the update results,and the feasibility of the model update method for the regional-level buildings is verified.(2)In order to reduce the calculation time,a rapid seismic damage simulation method for urban buildings based on scenario matching method is proposed.In this method,a large number of nonlinear time history analysis using different ground motions are carried out in advance,and the calculation results are stored in the seismic damage scenario database of the city.When there is a need to rapidly construct an earthquake damage scenario,a matching algorithm is used to directly extract data from the database to generate a building damage scenario of the target earthquake.Three matching algorithms are developed,namely single wave damage matching algorithm,multiple waves damage splicing algorithm and multiple waves damage combination algorithm.Three matching algorithms are compared and studied from the following three aspects:accuracy,robustness and efficiency.Comparison results show that the multiple waves damage splicing algorithm is the best,the average matching error of the damage index is12.8%,and the calculation time is reduced by more than 95% compared with nonlinear time history analysis.The seismic damage scenario matching method based on the multiple waves damage splicing algorithm is used to calculate the structural damage index of building structures in a large city under non-uniform ground motion input,and the results show that,the average matching error of the damage index is 15.7%,77.7% of the structures are matched to the correct damage state,which verifies the effectiveness of the scenario matching method under non-uniform ground motion input.(3)In order to reduce the calculation amount and improve the calculation speed,a rapid seismic damage simulation method for urban buildings based on clustering algorithm is proposed.In this method,structures are firstly clustered and grouped based on the principle of having similar damage indices under ground motions,and then a representative structure is selected from each group,and finally representative structures are used to perform nonlinear time history response analysis,and the structural damage index of the representative structure is used as the damage index of other structures,thereby reducing the computational complexity and computation time.The K-means algorithm is selected and improved in the following way,the upper limit of sample difference within the cluster can be set to determine the number of clusters,and the method of clustering in batches is adopted to speed up the cluster analysis.The parameter setting of the improved K-means algorithm is studied by a numerical example,and the influence of the choice of ground motions on the clustering results is studied.The results show that the selection of ground motion has no significant effect on the number of clusters,the dispersion of model parameters of structures within the same group.The applicability of this method are verified in an example with a large urban buildings model and non-uniform ground motion input.The results show that the calculation time is reduced by 90.5% compared with nonlinear time history analysis,and the proportion of structures with right damage state is reached to 85.4%.