Building structure vulnerability assessment method based on seismic oscillation parameter zoning map

Abstract

The invention discloses a building structure vulnerability assessment method based on a seismic oscillation parameter zoning map. The method comprises two parts of content: random artificial vibration generation based on the seismic oscillation parameter zoning map and building structure seismic vulnerability analysis based on a recurrence period. Obtaining target response spectrums of each street in five types of sites and four return periods; generating random man-made vibration based on a spectrum representation method, and matching a mean response spectrum and a target response spectrum of the random man-made vibration by adopting an optimization algorithm; and establishing a multi-degree-of-freedom shear type layer model based on the BWBN model. And determining parameter values of the multi-degree-of-freedom shear type layer model and maximum inter-layer displacement angle thresholds and loss ratios when different types of building structures are in different damage states. And establishing earthquake vulnerability curves when different types of building structures in different streets are located in different sites. According to the invention, the method is suitable for the unified assessment of the earthquake vulnerability of different regions and different types of building structures in China, and improves the applicability of the assessment method, the accuracy of the assessment result and the structural calculation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake resistance and disaster prevention of building structures, and in particular to a building structure vulnerability assessment method based on a seismic parameter zoning map. Background Art

[0002] The current methods for assessing the seismic vulnerability of building structures have the following main problems:

[0003] (1) When designing the seismic performance of building structures in different regions of the country, the seismic performance under major earthquakes was only verified according to the requirements of the Code for Seismic Design of Buildings (GB50011), without comprehensively evaluating the seismic performance of different types of building structures under different earthquake recurrence periods.

[0004] (2) The current assessment of seismic vulnerability of building structures mainly targets single buildings. When determining the seismic motion input, it only targets large earthquake scenarios. The generated artificial seismic motion time history cannot reflect the random characteristics of seismic motion and is incompatible with the results of local probabilistic seismic hazard analysis.

[0005] (3) Current building structure seismic vulnerability assessments mainly use peak ground acceleration (PGA) or spectral acceleration (Sa) as seismic intensity indicators. Due to the large number of different types of building structures on the same street, different types of building structures are suitable for different seismic intensity indicators. For example, PGA is more suitable for the seismic vulnerability assessment of low-rise masonry structures, while Sa is more suitable for the seismic vulnerability assessment of high-rise reinforced concrete structures. Therefore, the current building structure seismic vulnerability assessment method is difficult to compare the seismic performance of different types of building structures on the same street.

[0006] (4) Current assessments of building structural seismic vulnerability are primarily based on detailed finite element simulations, which generate seismic vulnerability curves for several typical and representative building structures. This approach not only suffers from a lack of standards for selecting typical and representative buildings, but also generates identical seismic vulnerability curves for the same type of building structures located in different regions, which is inconsistent with actual earthquake damage scenarios. Summary of the Invention

[0007] In response to the shortcomings of the prior art, the present invention aims to provide a building structure vulnerability assessment method based on a seismic parameter zoning map. Given that different regions of my country have different levels of urban seismic risk and different types of building structures exist in cities, a unified seismic vulnerability assessment method applicable to different regions and different types of building structures in my country is proposed, thereby improving the applicability of the assessment method, the accuracy of the assessment results, and the efficiency of structural calculations. To achieve the above-mentioned objectives and other advantages of the present invention, a building structure vulnerability assessment method based on a seismic parameter zoning map is provided, comprising:

[0008] Random artificial seismic motion generation based on seismic motion parameter zoning map and seismic vulnerability analysis of building structures based on return period, the seismic vulnerability analysis of building structures based on return period includes the following steps:

[0009] 2-1. Obtain the building structure database of each street and classify its basic attributes;

[0010] 2-2. For each building structure, establish a multi-degree-of-freedom shear layer model;

[0011] 2-3. Determine the parameter values ​​of the multi-degree-of-freedom shear layer model based on the basic attribute parameters in the building structure database;

[0012] 2-4. Input the time-frequency non-stationary random artificial ground motion under five types of sites and four return periods for each street, and calculate the maximum inter-story drift angles of different types of buildings located on the street;

[0013] 2-5. Calculate the mean and standard deviation of the maximum inter-story drift angle for each building in the street under five types of sites and four return periods, and use the log-normal distribution function to describe its probability density distribution;

[0014] 2-6. Based on the basic attribute parameters in the building structure database, determine the maximum inter-story drift angle thresholds for each building in different damage states, and calculate the probability of each building exceeding different damage states under five types of sites and four return periods;

[0015] 2-7. Based on the basic attribute parameters in the building structure database, determine the earthquake loss ratio of each building in different damage states, and calculate the total loss ratio of each building in five types of sites and four return periods;

[0016] 2-8. The cumulative normal distribution function is used to fit the total earthquake loss ratio and the return period to obtain the earthquake vulnerability curve of each building when it is located in the five types of sites.

[0017] Preferably, the random artificial earthquake motion generation based on the earthquake motion parameter zoning map comprises the following steps:

[0018] 1-1. Based on the current seismic parameter zoning map of my country, corresponding to the administrative division code of my country based on streets, the basic seismic peak acceleration and response spectrum characteristic cycle of each street Class II site were extracted;

[0019] 1-2. Adjust the parameters in 1-1 to obtain the target response spectra for each street under five types of sites and four return periods;

[0020] 1-3. Based on the evolving power spectrum model, a spectral representation method is used to generate time-frequency non-stationary random artificial ground motions and calculate the mean response spectrum of the random artificial ground motions.

[0021] 1-4. Using the nine independent parameters of the evolving power spectrum model as target variables, an optimization algorithm is used to match the mean response spectrum of random artificial ground motions with the target response spectrum.

[0022] 1-5. Based on the optimized parameters of the evolution power spectrum model, the spectral representation method is used to generate time-frequency non-stationary random artificial ground motions for each street under five types of sites and four return periods.

[0023] Preferably, the spectrum matching objective function of steps 1-4 is: the sum of relative errors between the mean response spectrum of the random artificial seismic motion under each recurrence period and the target response spectrum weighted by the target spectrum value at each frequency point.

[0024] Preferably, the multi-degree-of-freedom bending-shear layer model parameter calibration rules of steps 2-3 are: determine the equivalent layer mass m0 based on the floor area, number of structural layers and mass density; determine the basic period T0 of the structure based on the structure type and building height; determine the equivalent interlayer elastic stiffness k0 based on the equivalent layer mass m0 and the basic period T0 of the structure; determine the 12 parameter values ​​of the BWBN hysteresis model based on the structure type and building age.

[0025] Preferably, the step 2-8 uses the recurrence period as a measure of earthquake intensity to generate an earthquake vulnerability curve for each building in different site categories, and uses a cumulative normal distribution function for fitting.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) The present invention combines the current seismic parameter zoning map in my country with the seismic vulnerability analysis method for building structures. Based on the evolutionary power spectrum model and spectral representation method, an optimization algorithm is used to match the mean response spectrum of random artificial seismic motion with the target response spectrum provided by the seismic parameter zoning map. Random artificial seismic motions are generated for five types of sites and four recurrence periods in different streets. This solves the current wave selection difficulties and lack of standards in seismic vulnerability assessment of building structures, making seismic vulnerability assessment of building structures more accurate.

[0028] (2) The present invention simplifies different types of building structures into a multi-degree-of-freedom shear layer model based on the BWBN model, and proposes a rule for calibrating 15 model parameters based on the basic attribute parameters of the building structure. This solves the problem that the current seismic vulnerability assessment model of building structures not only has many parameters and is difficult to calibrate, but also has difficulty in describing the complex hysteresis characteristics of different types of building structures under earthquake action, such as strength degradation, stiffness degradation and pinching effect, making the seismic vulnerability assessment of building structures more efficient;

[0029] (3) The present invention adopts the earthquake recurrence period index to measure the seismic motion intensity in the seismic vulnerability assessment of building structures, which is consistent with the results of the classic probabilistic earthquake hazard analysis. It solves the problem that the current seismic vulnerability assessment of building structures generally adopts seismic motion intensity indicators such as ground peak acceleration PGA and spectral acceleration Sa, which are not suitable for seismic vulnerability assessment of different regions and different types of building structures in my country, and makes the seismic vulnerability assessment method of building structures have a wider scope of application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of a building structure vulnerability assessment method based on a seismic parameter zoning map according to the present invention;

[0031] Figure 2 Target response spectra of target streets under five types of sites and four return periods according to an embodiment of the building structure vulnerability assessment method based on earthquake motion parameter zoning map of the present invention;

[0032] Figure 3 This is a comparison diagram of the mean response spectrum of random artificial earthquake motions and the target response spectrum under four return periods for a target street Class II site according to an embodiment of the building structure vulnerability assessment method based on earthquake motion parameter zoning map of the present invention;

[0033] Figure 4 A diagram of a multi-degree-of-freedom shear layer model used for different types of building structures in a target street according to an embodiment of the method for assessing building structure vulnerability based on a seismic parameter zoning map of the present invention;

[0034] Figure 5 The maximum inter-story displacement angle distribution diagram under four return periods of a three-story masonry structure located in a Class II site in a target street according to an embodiment of the building structure vulnerability assessment method based on the earthquake motion parameter zoning map of the present invention;

[0035] Figure 6 This is a seismic vulnerability curve diagram of a three-story masonry structure located in a Class II site in a target street according to an embodiment of the building structure vulnerability assessment method based on the seismic parameter zoning map of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Reference Figure 1A building structure vulnerability assessment method based on a seismic motion parameter zoning map includes: random artificial seismic motion generation based on the seismic motion parameter zoning map and seismic vulnerability analysis of building structures based on the return period.

[0038] The random artificial earthquake motion generation based on the earthquake motion parameter zoning map includes the following steps:

[0039] 1-1. Based on the current seismic parameter zoning map of my country, corresponding to the administrative division code of my country based on streets, the peak acceleration (PGA) and characteristic period (Tg) of the basic seismic motion (50-year exceedance probability of 10%) under Class II sites of each street were extracted;

[0040] 1-2. Adjust the above parameters to obtain the target response spectra for each street under five types of sites (I0, I1, II, III, IV) and four return periods (50-year exceedance probabilities of 63%, 10%, 2%, and 1%, i.e., return periods of 50 years, 474 years, 1600 years, and 10,000 years);

[0041] 1-3. Based on the evolving power spectrum model, a spectral representation method is used to generate time-frequency non-stationary random artificial ground motions and calculate the mean response spectrum of these random artificial ground motions; Evolving power spectrum model:

[0042]

[0043] Where,

[0044]

[0045] Where ω is the frequency, t is the time, t a ,β,γ,ω0,ω1,ξ0,ξ1,t c 、 are the 9 model parameters of the evolution power spectrum model.

[0046] 1-4. Using the nine independent parameters of the evolving power spectrum model as target variables, an optimization algorithm is used to match the mean response spectrum of random artificial ground motions with the target response spectrum. The spectrum matching objective function is: the sum of the relative errors between the mean response spectrum of random artificial ground motions for each return period and the target response spectrum, weighted by the target spectrum value at each frequency point. The calculation expression is as follows:

[0047]

[0048] In the formula, RSA mean,k and RSA target,k are the mean response spectrum and target response spectrum at the kth frequency point respectively.

[0049] 1-5. Based on the optimized parameters of the evolution power spectrum model, the spectral representation method is used to generate time-frequency non-stationary random artificial ground motions for each street under five types of sites and four return periods.

[0050] The vulnerability analysis of building structures based on the return period includes the following steps:

[0051] 2-1. Obtain the building structure database of each street and classify its basic attributes. The basic attributes should at least include: construction age, building height, structure type, usage type, number of floors, and floor area;

[0052] 2-2. For each building structure, the Bouc-Wen-Baber-Noori (BWBN) hysteretic model is used to describe the restoring force of each layer, and a multi-degree-of-freedom shear layer model is established. The i-th component of the restoring force vector F of the multi-degree-of-freedom shear layer model can be described by the BWBN hysteretic model:

[0053] F i =α i k0Δu i +(1-α i )k0z i

[0054] Where, α i is the post-yield stiffness ratio of the i-th layer; Δu i is the inter-story displacement of the i-th layer; z i is the nonlinear hysteresis displacement of the i-th layer, which is determined by the following formula:

[0055]

[0056] Where, For z i First derivative with respect to time; β i , γ i and n i is the shape parameter of the uniaxial BWBN model of the i-th layer; δ ηi and δ υi are the stiffness and strength degradation parameters of the i-th layer respectively; ε i For the i-th layer at duration t c The accumulated hysteresis energy dissipated in the hysteresis loop is determined by the following formula:

[0057]

[0058] The function describing the pinching effect of the i-th layer is determined as follows:

[0059]

[0060] Where, ξ si ,pi ,q i ,ψ i ,δ ψi and λ i is the control parameter describing the pinching effect of the i-th layer; is a symbolic function:

[0061]

[0062] 2-3. Determine the parameter values ​​of the multi-degree-of-freedom shear layer model based on the basic attribute parameters in the building structure database, such as structure type, building height and construction year. The parameter calibration rules of the multi-degree-of-freedom bending-shear layer model are as follows: determine the equivalent layer mass m0 based on the floor area, number of structural layers and mass density; determine the fundamental period T0 of the structure based on the structure type and building height; determine the equivalent inter-layer elastic stiffness k0 based on the equivalent layer mass m0 and the fundamental period T0; and determine the 12 parameter values ​​of the BWBN hysteresis model based on the structure type and construction year.

[0063] 2-4. Input the time-frequency non-stationary random artificial ground motion under five types of sites and four return periods for each street, and use the Newmark-β method to calculate the maximum inter-story drift angles of different types of buildings located on the street;

[0064] 2-5. Calculate the mean and standard deviation of the maximum inter-story drift angle for each building in the street under five types of sites and four return periods, and use the log-normal distribution function to describe its probability density distribution;

[0065] 2-6. Based on the basic attribute parameters in the building structure database, such as structure type, building height, and construction year, determine the maximum inter-story displacement angle thresholds for each building in different damage states (minor damage, moderate damage, severe damage, and collapse damage). Calculate the probability of each building exceeding different damage states under five site types and four recurrence periods.

[0066] 2-7. Based on the basic attribute parameters in the building structure database, such as structural type and construction age, determine the earthquake loss ratio of each building in different damage states, and calculate the total loss ratio of each building in five types of sites and four return periods;

[0067] 2-8. Use the cumulative normal distribution function to fit the total earthquake loss ratio and return period to obtain the earthquake vulnerability curve for each building when it is located in the five types of sites. Use the return period as a measure of earthquake motion intensity to generate the earthquake vulnerability curve for each building in different site categories and fit it with the cumulative normal distribution function. The calculation expression is:

[0068]

[0069] Where T is the return period, L is the total earthquake loss ratio, and μ and σ are two fitting parameters.

[0070] Example: The random artificial earthquake motion generation based on the earthquake motion parameter zoning map includes the following steps:

[0071] 1-1. Based on the current seismic parameter zoning map of my country, corresponding to the administrative division code of my country based on streets, the peak acceleration (PGA) and characteristic period (Tg) of the basic seismic motion (50-year exceedance probability of 10%) under Class II sites of each street were extracted;

[0072] 1-2. Adjust the above parameters to obtain the target response spectra for each street under five types of sites (I0, I1, II, III, IV) and four return periods (50-year exceedance probabilities of 63%, 10%, 2%, and 1%, i.e., return periods of 50 years, 474 years, 1600 years, and 10,000 years). The expression of the target response spectrum is:

[0073]

[0074] Where a m is the peak ground acceleration of earthquake motion, which can be determined and adjusted based on the current earthquake motion parameter zoning map in my country; β m is the seismic amplification factor, usually taken as 2.5; T0 is the first inflection point period of the target response spectrum, usually taken as 0.1s; T g is the second inflection point period of the target response spectrum, which can be determined and adjusted based on the current seismic parameter zoning map in my country; T m is the maximum cycle range, usually 6s; α is the speed control parameter of the descending section, usually 1.0.

[0075] Target response spectra for other types of sites and return periods, response spectrum characteristic period (T g The adjustment method for the 2019-nCoV earthquake data is as follows: First, determine the zoning based on the characteristic period Tg of the acceleration response spectrum for Class II ground motion (50-year exceedance probability of 10%). Then, determine the corresponding characteristic period based on the site category of the building structure from the table below. Finally, consider the impact of the recurrence period. For frequent ground motion (50-year exceedance probability of 63.2%), the characteristic period remains unchanged. For rare ground motion (50-year exceedance probability of 2%) or extremely rare ground motion (50-year exceedance probability of 1%), the characteristic period is increased by 0.05s.

[0076]

[0077] Target response spectra for other types of sites and return periods, peak ground acceleration (a m) is adjusted as follows: First, determine the zone based on the peak acceleration value of the basic earthquake motion of Class II site (50-year exceedance probability 10%); then, calculate the corresponding peak ground acceleration according to the site category of the building structure using the following formula; finally, consider the impact of the recurrence period. If it is a frequent earthquake motion (50-year exceedance probability 63.2%), a rare earthquake motion (50-year exceedance probability 2%), or an extremely rare earthquake motion (50-year exceedance probability 1%), a m The values ​​are taken as 1 / 3, 1.9 times and 2.9 times the basic earthquake motion (50-year exceedance probability of 10%) respectively.

[0078] a m =F×a m,II

[0079] Where a m,II is the peak acceleration under the basic earthquake motion of Class II site (50-year exceedance probability of 10%); F is the peak acceleration adjustment factor, which is taken from the following table:

[0080]

[0081] 1-3. Based on the evolution power spectrum model, the spectrum representation method is used to generate time-frequency non-stationary random artificial ground motions, and the mean response spectrum of these random artificial ground motions is calculated. The evolution power spectrum model used is:

[0082]

[0083] Where:

[0084]

[0085] Where ω represents frequency; t represents time; t a ,β,γ,ω0,ω1,ξ0,ξ1,t c 、 are the 9 independent parameters of the evolution power spectrum model.

[0086] 1-4. Using the nine independent parameters of the evolving power spectrum model as target variables, an optimization algorithm is used to match the mean response spectrum of the random artificial ground motion with the target response spectrum. The spectrum matching process is as follows:

[0087] (1) The objective function is defined as the sum of the relative errors between the mean response spectrum of random artificial ground motions under each return period and the target response spectrum weighted by the target spectrum value at each frequency point. The calculation expression is as follows:

[0088]

[0089] In the formula, RSA mean,k and RSA target,kare the mean response spectrum and target response spectrum at the kth frequency point respectively.

[0090] (2) The optimization algorithm can use the differential evolution algorithm to initialize the population, determine the population size, the maximum number of iterations, the scaling factor, the crossover probability, etc.; the iteration termination condition can be set to be less than or equal to 5% of the objective function, or to reach the maximum number of iterations;

[0091] 1-5. Based on the optimized parameters of the evolving power spectrum model, a spectral representation method is used to generate time-frequency non-stationary random artificial ground motions for each street under five types of sites and four return periods. The spectral representation method used is:

[0092]

[0093] Where:

[0094] Δω k =ω k -ω k-1 ,k=1,2,3,...,n

[0095] Among them, ω k represents the kth frequency point; n represents the number of frequency intervals; φ κ represents the kth random phase, which obeys the uniform distribution in the interval [0,2π]; ω k The corresponding period can be calculated as follows:

[0096]

[0097] The building structure vulnerability analysis based on the return period includes the following steps:

[0098] Step 2-1) Obtain the building structure database for each street and classify it by basic attributes. Basic attributes should at least include: construction year, building height, structure type, usage type, number of floors, and floor area. The basic attribute classification rules for buildings are as follows:

[0099] (1) Construction period

[0100] The construction year is a specific numerical value and can be divided into four categories: no defense (before 1989), low defense (1990-2000), medium defense (2001-2010) and high defense (after 2010).

[0101] (2) Building height

[0102] Building height is a specific numerical value and can be divided into three categories: low-rise (3 floors and below), mid-rise (4 to 7 floors) and high-rise (8 floors and above).

[0103] (3)Structural type

[0104] Structural types are divided into three categories: steel structure, masonry structure and reinforced concrete structure.

[0105] (4) Usage type

[0106] The usage types can be divided into five categories: residential buildings, commercial buildings, industrial buildings, public facilities and other types.

[0107] 2-2. For each building structure, the Bouc-Wen-Baber-Noori (BWBN) hysteresis model is used to describe the restoring force of each layer, and a multi-degree-of-freedom shear layer model is established. The motion control differential equation of the multi-degree-of-freedom bending-shear layer model is:

[0108]

[0109] Where C is the damping matrix of the structure. If Rayleigh damping is used, it can be determined based on the first two-order damping ratios of the structure, the structural mass matrix M, and the stiffness matrix K. is the input artificial ground motion time history, U is the N×1 displacement response vector, N is the number of layers of the structure; ΔU is the N×1 inter-layer displacement response vector; Z is the N×1 inter-layer hysteresis displacement response vector; F is the N×1 restoring force vector determined by the BWBN model; I is the N×1 unit vector; the mass matrix M is expressed as:

[0110]

[0111] The stiffness matrix K is expressed as:

[0112]

[0113] Where m0 is the equivalent layer mass; k0 is the equivalent elastic interlayer stiffness; and is a constant coefficient matrix. The i-th component of the restoring force vector F can be described by the BWBN hysteresis model:

[0114] F i =α i k0Δu i +(1-α i )k0z i

[0115] Where, α i is the post-yield stiffness ratio of the i-th layer; Δu i is the inter-story displacement of the i-th layer; z i is the nonlinear hysteresis displacement of the i-th layer, which is determined by the following formula:

[0116]

[0117] Where, z i First derivative with respect to time; β i , γ i and n i is the shape parameter of the uniaxial BWBN model of the i-th layer; δ ηi and δ υi are the stiffness and strength degradation parameters of the i-th layer respectively; ε i For the i-th layer at duration t c The accumulated hysteresis energy dissipated in the hysteresis loop is determined by the following formula:

[0118]

[0119] The function describing the pinching effect of the i-th layer is determined as follows:

[0120]

[0121] Where, ξ si ,p i ,q i ,ψ i ,δ ψi and λ i is the control parameter describing the pinching effect of the i-th layer; is a symbolic function:

[0122]

[0123] There are 15 parameters in the multi-degree-of-freedom shear layer model, namely: equivalent layer mass m, equivalent elastic interlayer stiffness k0, structural fundamental period T0, first-order damping ratio ξ1, second-order damping ratio ξ2 and 12 BWBN hysteresis model parameters.

[0124] 2-3. Determine the parameter values ​​of the multi-degree-of-freedom shear layer model based on the basic attribute parameters in the building structure database, such as structure type, building height, and construction year. The parameter calibration rules are as follows:

[0125] (1) Equivalent layer mass m0

[0126] Assuming that the mass of the building structure is evenly distributed along the floor height, the equivalent floor mass m0 is estimated based on the floor area:

[0127]

[0128] Where S is the floor area, N is the number of structural layers, and ρ is the mass density per unit area, which is related to the structure type and building height. The following values ​​can be used as an estimate: (1) Masonry structure: 17 kN / m 2 ; (2) Reinforced concrete frame structure: 11~16kN / m2 (When the number of floors of reinforced concrete frame structure is more than 20, take the upper limit value; when the number of floors is less than 5, take the lower limit value.) (3) Steel structure: 8kN / m 2 .

[0129] (2) Equivalent interlayer stiffness

[0130] Calculated using Rayleigh method:

[0131]

[0132] Where ψ1 is the first-order vibration mode vector of the structure; T0 is the basic natural vibration period of the structure, which is related to the structure type and building height. For reinforced concrete and steel structures, the following empirical formula can be used for estimation:

[0133] T0=C1H x

[0134] Among them, reinforced concrete structure: C1 = 0.047, x = 0.9; steel structure: C1 = 0.072, x = 0.8. For masonry structure, the following empirical formula can be used for estimation:

[0135] T0=0.221+0.225×N

[0136] (3) BWBN hysteresis model parameters

[0137] The parameters of the BWBN hysteresis model are related to the structure type and building age, and their values ​​are shown in the following table:

[0138]

[0139] 2-4. Input the time-frequency non-stationary random artificial seismic motions for each street under the five site categories and four return periods, and calculate the maximum inter-story drift angles of different types of buildings located on the street using the Newmark-β method. The time history of the time-frequency non-stationary random artificial seismic motion for the street under the five site categories and four return periods is determined based on steps 1-1) to 1-5).

[0140] 2-5. Calculate the mean and standard deviation of the maximum inter-story drift angle of each building in the street under five types of sites and four recurrence periods, and use the log-normal distribution function to describe its probability density distribution.

[0141] 2-6. Based on the basic attribute parameters in the building structure database, such as structural type, building height, and construction year, determine the maximum inter-story displacement angle thresholds for each building in different damage states (minor damage, moderate damage, severe damage, and collapse damage). Calculate the probability of each building in different damage states under five site types and four recurrence periods.

[0142]

[0143] Where Φ is the cumulative lognormal distribution function; μ j and σ j are the logarithmic mean and logarithmic standard deviation of the maximum inter-story drift angle of the building structure under the jth return period; is the inter-story displacement angle threshold of the i-th limit state. The low-rise building is determined according to the following table; the medium-rise building is determined to be 2 / 3 of the low-rise building; and the high-rise building is determined to be 1 / 3 of the low-rise building.

[0144]

[0145] 2-7. Based on the basic attribute parameters in the building structure database, such as structural type and construction age, determine the earthquake loss ratio of each building in different damage states, and calculate the total loss ratio of each building in five types of sites and four return periods;

[0146]

[0147] Where, P ij is the probability that the structure is in the i-th damage state under the j-th return period; R i is the loss ratio when the structure is in the i-th damage state, which is related to the structure type and is determined according to the following table.

[0148]

[0149] 2-8. The cumulative normal distribution function is used to fit the total earthquake loss ratio and the return period to obtain the earthquake vulnerability curve for each building located in the five types of sites:

[0150]

[0151] Where T is the return period, L is the total earthquake loss ratio, and μ and σ are two fitting parameters.

[0152] This embodiment adopts the above method to perform seismic vulnerability assessment on the building structures of a target street, which is further described in detail.

[0153] The target street is located in Dongcheng District, Beijing, my country. Since the 1990s, rapid urbanization and changing land planning have resulted in a coexistence of skyscrapers and older buildings. Furthermore, the target street is located in the central eastern region of Beijing, a densely populated area with concentrated wealth. A destructive earthquake could cause severe casualties and economic losses. Therefore, conducting a seismic vulnerability assessment of the street's building structures is crucial for developing and implementing disaster prevention, relief, and mitigation plans to minimize earthquake losses.

[0154] (1) Generation of random artificial ground motion based on ground motion parameter zoning map

[0155] First, based on the current earthquake parameter zoning map in my country, the peak acceleration and response spectrum characteristic period of the basic earthquake motion (50-year exceedance probability of 10%) of the target street Class II site are obtained as follows: PGA = 0.20g, Tg = 0.40s. Then, the adjustment method is adopted to obtain the PGA and Tg of the street under the five types of sites (I0, I1, II, III, IV) and four recurrence periods (50-year exceedance probability of 63%, 10%, 2% and 1%, i.e., 50-year, 474-year, 1600-year and 10,000-year recurrence periods). Furthermore, the target response spectrum model expression is adopted to obtain the target response spectrum curves of the street under the five types of sites and four types of recurrence periods, as shown below: Figure 2 As shown in the figure, the plateau section of the target response spectrum becomes increasingly longer as the site category changes from I0 to IV. The maximum acceleration response spectrum values ​​for sites of categories II-IV are the same and greater than those for categories I0-I1. When the recurrence period increases from 50 years to 10,000 years, the acceleration response spectrum values ​​increase significantly, indicating a positive correlation between spectral acceleration and seismic hazard analysis results.

[0156] Assume that a building structure on a target street is located at a Class II site. Therefore, the target response spectra of a Class II site under four return periods are used as the matching object. Using the nine independent parameters in the evolving power spectrum model as decision variables and ε as the objective function, a differential evolution algorithm is used to fit the mean response spectrum and target response spectrum of random artificial ground motions under each return period. The fitting process is as follows: First, a population of 50 independent parameters in the evolving power spectrum model is initialized, with the maximum number of iterations set to 100, the scaling factor set to 0.2–0.8, and the crossover probability set to 0.2. Then, crossover and mutation operations are performed to refine the initialized population. The resulting population is then used to evolve the power spectrum model. Using the spectral representation method, 50 random artificial ground motions are generated and the corresponding mean acceleration response spectra are calculated. Finally, the objective function is calculated to evaluate the relative error between the mean response spectrum and the target response spectrum. If the relative error is less than 5%, the iteration is terminated and the optimal population parameters are output. Otherwise, the iteration continues, generating new population parameters until the requirements are met. Finally, the optimal population parameters are used as the parameter values ​​of the evolution power spectrum model to generate 50 random artificial ground motions under each return period. The comparison results of their mean spectrum and target spectrum are as follows: Figure 3As shown. It can be seen that the mean response spectrum under the four return periods is very consistent with the target response spectrum, and the relative error can be controlled at about 5%. It should be noted that there is a certain deviation between the mean response spectrum and the target response spectrum at certain "stubborn points" of the short period, and this deviation cannot be reduced by increasing the number of iterations. The reason is that the objective function searches for the global optimum, but the model parameters in the evolving power spectrum are less sensitive at these points. In fact, due to the randomness of seismic motion, if a perfect fit between the mean response spectrum and the target response spectrum is achieved, a certain degree of distortion will be introduced. Therefore, in actual engineering applications, setting the relative error between the mean response spectrum and the target response spectrum to 5% is stable and feasible.

[0157] (2) Analysis of seismic vulnerability of building structures based on return period

[0158] Assume that a building on this street is a residential building located in a Class II site, with a masonry structure, three stories high, each about 3 meters high, a total building height of 9 meters, built in 1990, and a floor area of ​​300 square meters. Therefore, we can estimate the equivalent floor mass m0 = 5.204 × 10 6 kg; equivalent interlayer elastic stiffness k0=2.699×10 9 N / m; BWBN hysteresis model parameters are obtained by looking up the table: α = 0.01, β = 1.8, γ = 0.5, δ v =0.04,δ η =0.25, ζs=0.6, q=0.25, p=2.5, ψ=0.15, δ ψ = 0.008, λ = 0.8, n = 1. Furthermore, the first- and second-order damping ratios of the structure can be set to 0.05. Furthermore, the table shows that the maximum inter-story drift angle thresholds for the building in the states of minor damage, moderate damage, severe damage, and collapse are 0.004, 0.006, 0.016, and 0.044, respectively. The earthquake loss ratios for the building in the states of essentially intact, minor damage, moderate damage, severe damage, and collapse are 5%, 15%, 45%, 90%, and 100%, respectively.

[0159] For this building, the mass of each floor is concentrated at the floor position, the BWBN hysteresis model is used to describe the structural inter-layer restoring force, and a multi-degree-of-freedom shear layer model is established, such as Figure 4 As shown. Input the random artificial earthquake motion under four recurrence periods of the street where the building is located, and use the Newmark-β method to calculate the maximum inter-story displacement angle of the building. The probability density distribution of the maximum inter-story displacement angle of the building under each recurrence period is as follows: Figure 5As shown, the maximum inter-story drift angle generally conforms to a lognormal distribution. The mean of the maximum inter-story drift angle increases with increasing return period. If we calculate the probability of the building being in different damage states under the four return periods, we can see that under a frequent earthquake (50-year exceedance probability of 63.2%, corresponding to a 50-year return period), the probability of the building being essentially intact is 99%. Under a design earthquake (50-year exceedance probability of 10%, corresponding to a 475-year return period), the probabilities of the building being slightly damaged and moderately damaged are 37% and 52%, respectively. Under a rare earthquake (50-year exceedance probability of 2%, corresponding to a 2500-year return period), the probabilities of the building being moderately damaged and severely damaged are 73% and 25%, respectively. The analysis results indicate that the building meets the requirements of my country's building seismic fortification code for "no damage in small earthquakes, repairable in medium earthquakes, and resistant to collapse in large earthquakes."

[0160] The calculated earthquake loss ratios for the building under return periods of 50 years, 475 years, 2500 years, and 10,000 years are 5%, 30%, 56%, and 80%, respectively. The cumulative log-normal distribution function is used to fit the earthquake loss ratio and return period to generate the vulnerability curve of the building based on the earthquake return period, as shown in the figure below: Figure 5 It can be seen that as the earthquake recurrence period increases, the earthquake loss ratio of the building continues to increase.

[0161] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially, or the part that contributes to the prior art, or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, online cloud disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code.

[0162] The number of devices and processing scales described herein are intended to simplify the description of the present invention, and the application, modification, and variation of the present invention will be apparent to those skilled in the art. Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiment. They can be applied to various fields suitable for the present invention. For those skilled in the art, additional modifications can be easily implemented. Therefore, the present invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the claims and their equivalents.

Claims

1. A building structure vulnerability assessment method based on a seismic parameter zoning map, characterized in that: include: Random artificial seismic motion generation based on seismic motion parameter zoning map and seismic vulnerability analysis of building structures based on return period, the seismic vulnerability analysis of building structures based on return period includes the following steps: 2-1. Obtain the building structure database of each street and classify its basic attributes; 2-2. For each building structure, establish a multi-degree-of-freedom shear layer model; 2-3. Determine the parameter values ​​of the multi-degree-of-freedom shear layer model based on the basic attribute parameters in the building structure database; 2-4. Input the time-frequency non-stationary random artificial ground motion under five types of sites and four return periods for each street, and calculate the maximum inter-story drift angles of different types of buildings located on the street; 2-5. Calculate the mean and standard deviation of the maximum inter-story drift angle for each building in the street under five types of sites and four return periods, and use the log-normal distribution function to describe its probability density distribution; 2-6. Based on the basic attribute parameters in the building structure database, determine the maximum inter-story drift angle thresholds for each building in different damage states, and calculate the probability of each building exceeding different damage states under five types of sites and four return periods; 2-7. Based on the basic attribute parameters in the building structure database, determine the earthquake loss ratio of each building in different damage states, and calculate the total loss ratio of each building in five types of sites and four return periods; 2-8. The cumulative normal distribution function is used to fit the total earthquake loss ratio and the return period to obtain the earthquake vulnerability curve of each building when it is located in the five types of sites.

2. The building structure vulnerability assessment method based on the earthquake parameter zoning map according to claim 1, characterized in that: The random artificial earthquake motion generation based on the earthquake motion parameter zoning map comprises the following steps: 1-1. Based on the current seismic parameter zoning map of my country, corresponding to the administrative division code of my country based on streets, the basic seismic peak acceleration and response spectrum characteristic cycle of each street Class II site were extracted; 1-2. Adjust the parameters in 1-1 to obtain the target response spectra for each street under five types of sites and four return periods; 1-3. Based on the evolving power spectrum model, a spectral representation method is used to generate time-frequency non-stationary random artificial ground motions and calculate the mean response spectrum of the random artificial ground motions. 1-4. Using the nine independent parameters of the evolving power spectrum model as target variables, an optimization algorithm is used to match the mean response spectrum of random artificial ground motions with the target response spectrum. 1-5. Based on the optimized parameters of the evolution power spectrum model, the spectral representation method is used to generate time-frequency non-stationary random artificial ground motions for each street under five types of sites and four return periods.

3. The building structure vulnerability assessment method based on the earthquake parameter zoning map according to claim 2, characterized in that: The spectrum matching objective function of steps 1-4 is: the sum of the relative errors between the mean response spectrum of the random artificial seismic motion under each recurrence period and the target response spectrum weighted by the target spectrum value at each frequency point.

4. The building structure vulnerability assessment method based on the earthquake parameter zoning map according to claim 1, characterized in that: The calibration rules for the multi-degree-of-freedom bending-shear layer model parameters in steps 2-3 are as follows: determine the equivalent layer mass m0 based on the floor area, number of structural layers and mass density; determine the fundamental period T0 of the structure based on the structure type and building height; determine the equivalent interlayer elastic stiffness k0 based on the equivalent layer mass m0 and the fundamental period T0 of the structure; and determine the values ​​of the 12 parameters of the BWBN hysteresis model based on the structure type and building age.

5. The building structure vulnerability assessment method based on the earthquake parameter zoning map according to claim 1, characterized in that: Steps 2-8 use the recurrence period as a measure of earthquake intensity to generate earthquake vulnerability curves for each building in different site categories, and use a cumulative normal distribution function for fitting.

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