Earthquake vulnerability database construction method and system

Abstract

The invention discloses an earthquake vulnerability database construction method and system, and the method comprises the following steps: defining a large class of collection indexes which comprise social and economic conditions, indoor and outdoor property loss information, engineering structure damage and loss information, disaster relief input information and other information; defining subclass collection indexes corresponding to the large class collection indexes; constructing a plurality of data tables or maps based on the large-class acquisition indexes and the small-class acquisition indexes; and collecting attribute data and adding the attribute data into the data table, and collecting spatial data and adding the spatial data into the map to obtain the earthquake vulnerability database. One of the beneficial effects of the present invention is that: on the basis of earthquake hazard evaluation reports over the years, various related project question-making reports and literature data, domestic and overseas huge disaster database platforms are investigated, and a Chinese destructive earthquake database is constructed by referring to various related specifications and standards.

Description

Technical Field

[0001] The present invention belongs to the technical field of databases, and particularly relates to a method and system for constructing a seismic vulnerability database. Background Art

[0002] Internationally, the global multi-hazard databases include NatCatSERVICE of Munich Re Group, Sigma database of Swiss Re-insurance Company, EM-DAT database of Centre for Research on the Epidemiology of Disasters of WHO. Databases of other countries and regions include Network for social studies on disaster prevention in Latin America (LARED), natural disaster database of Asian Disaster Reduction Center (ADRC), Emergency Management Australia Disasters Database (EMA), Monitoring, Mapping, and Analysis of Disaster Incidents in South Africa database (MANDISA), and United States Geological Survey database of earthquake disaster information. The United Nations Food and Drug Administration (UNFDA) has established a number of disaster relief and disaster relief systems, including the USGS Database, the Dartmouth Flood Observatory Database of the University of Colorado (http: / / floodobservatory.colorado.edu / ), the Tropical Cyclone Database of the Earth Observation Research Center of the Japan Aerospace Exploration Agency (http: / / sharaku.eorc.jaxa.jp / TYP_DB / index_e.shtml), the Fire Eruption Database of the Smithsonian Institution (http: / / www.volcano.si.edu / ), the Drought Disaster Database of the United Nations World Food Program (WFP), and the Flood, Landslide, and Storm Disaster Database of the Canadian Department of Fisheries and Oceans (DFO). However, the earthquake damage matrix of different regions, different structures, and different ages in my country has not yet been made public, in order to provide basic data support for the vulnerability models of different types of buildings in different regions and time periods. Therefore, it is necessary to develop a method and system for constructing an earthquake vulnerability database. Summary of the invention

[0003] In view of the above technical problems, a first aspect of the present invention provides a method for constructing an earthquake vulnerability database, which comprises the following steps:

[0004] Define major category collection indicators, which include social and economic conditions, indoor and outdoor property loss information, engineering structure damage and loss information, disaster relief investment information, and other information;

[0005] Define minor category collection indicators corresponding to the major category collection indicators. The minor category collection indicators corresponding to the social and economic conditions are the general situation of the earthquake-stricken area and the social and economic environment of the disaster area. The minor category collection indicators corresponding to the indoor and outdoor property loss information are the estimated indoor property loss of residential and public houses, the equipment loss of enterprises and institutions, and the outdoor property loss. The minor category collection indicators corresponding to the engineering structure damage and loss information are the losses of lifeline system engineering structures, water conservancy engineering structures, and other various engineering structure losses. The minor category collection indicators corresponding to the disaster relief investment information are the earthquake disaster relief investment costs. The minor category collection indicators corresponding to the other information are the table of abnormal damage conditions outside the evaluation area, the economic losses outside the evaluation area, other losses, the general situation of the natural environment of the disaster area and the seismogenic structure environment, the distribution map of the earthquake disaster assessment area, the earthquake intensity distribution map, and the sampling point distribution map;

[0006] Construct several data tables or maps based on the major category collection indicators and the minor category collection indicators;

[0007] The earthquake vulnerability database is obtained by collecting attribute data and adding it to the data table and collecting spatial data and adding it to the map.

[0008] The second aspect of the present invention provides a method for presenting a data table, including the following steps:

[0009] Connect to the earthquake vulnerability database;

[0010] Present the earthquake damage situations of different intensities in the area on a display based on the structure type.

[0011] The third aspect of the present invention provides an earthquake vulnerability database construction system, which includes at least one processor; and a memory that stores instructions, and when the instructions are executed by at least one processor, the steps of the method described above are implemented.

[0012] One of the beneficial effects of the present invention is that: based on the annual earthquake damage assessment reports, various relevant project final reports, and literature materials, the domestic and foreign megadisaster database platforms are investigated, and a variety of relevant specification standards are referred to, and a destructive earthquake database in China is constructed. Description of the Drawings

[0013] Figure 1 Flowchart of the earthquake vulnerability database construction method;

[0014] Figure 2(a) Earthquake damage conditions of frame structures in destructive earthquakes in Yunnan Province from 1990 to 2000;

[0015] Figure 2(b) Earthquake damage conditions of brick-concrete structures in destructive earthquakes in Yunnan Province from 1990 to 2000;

[0016] Figure 2(c) Earthquake damage conditions of penetrated bucket wood structures in destructive earthquakes in Yunnan Province from 1990 to 2000;

[0017] Figure 2(d) Earthquake damage conditions of penetrated earth-wood structures in destructive earthquakes in Yunnan Province from 1990 to 2000;

[0018] Figure 2(e) Earthquake damage conditions of penetrated brick-wood structures in destructive earthquakes in Yunnan Province from 1990 to 2000;

[0019] Figure 3(a) Earthquake damage conditions of intensity VI areas in Yunnan region from 1990 to 2000;

[0020] Figure 3(b) Earthquake damage conditions of intensity VII areas in Yunnan region from 1990 to 2000;

[0021] Figure 3(c) Earthquake damage conditions of intensity VIII areas in Yunnan region from 1990 to 2000;

[0022] Figure 3(d) Earthquake damage conditions of intensity IX areas in Yunnan region from 1990 to 2000;

[0023] Figure 4 Earthquake vulnerability database construction system;

[0024] Figure 5 Flow chart of earthquake vulnerability database construction system; Figure 6 Example diagrams of excellent, medium and poor earthquake vulnerability. Detailed implementation manners

[0025] The following embodiments further illustrate the content of the present invention, but should not be construed as a limitation to the present invention. Without departing from the spirit and essence of the present invention, any modification or replacement made to the methods, steps or conditions of the present invention shall fall within the scope of the present invention.

[0026] The overall concept of the present invention is as follows: Based on the principles of regional characteristics, structural forms, construction years, and intensity ranges, survey and collect the disaster assessment reports of earthquake disasters and various relevant specifications and standards in China since 1975. Then, conduct a comprehensive review and meticulous collation of the losses of multiple earthquake disasters with a magnitude of 4.5 or above and an epicentral intensity of VII or above in the Chinese mainland from 1975 to 2014, construct a destructive earthquake database, and systematically classify and summarize the seismic damage characteristics of typical building structures with different structural forms and construction times in each region under intensities above VI degree, and collate the seismic damage matrices of different regions, structures, and years, etc., in order to provide basic data support for vulnerability models of different types of housing buildings in different regions and time periods. Combining with the demand for earthquake insurance, an analysis method for the average loss rate curve of building structures based on the ground motion parameter PGA is proposed, providing directly available data for the catastrophe model.

[0027] The technical route for constructing the destructive earthquake database of the present invention is not specifically limited. For example: First, according to the development history of disaster prediction methods and disaster loss assessment methods, refer to the standards and specifications of relevant work on earthquake disaster loss assessment, and select relatively typical disaster loss assessment reports in each historical stage; then, based on the information recorded in these typical reports, combined with the industry requirements of earthquake catastrophe insurance, establish an index system with relatively comprehensive seismic damage information and compatible with disaster assessment reports of each era and data quality level; finally, collect, collate, and input the information of each content in the index system to establish a destructive earthquake database.

[0028] As Figure 1 shown, in some embodiments, a method for constructing an earthquake vulnerability database is involved, which includes the following steps:

[0029] S1: Define the major collection indicators, and the major collection indicators include social and economic conditions, indoor and outdoor property loss information, engineering structure damage and loss information, disaster relief investment information, and other information;

[0030] S2: Define the subclass collection metrics corresponding to the superclass collection metrics. The subclass collection metrics corresponding to the social and economic conditions are the general situation of the earthquake-stricken area and the social and economic environment of the disaster area. The subclass collection metrics corresponding to the indoor and outdoor property loss information are the estimated indoor property loss of residential and public houses, the equipment loss of enterprises and institutions, and the outdoor property loss. The subclass collection metrics corresponding to the engineering structure damage and loss information are the losses of lifeline system engineering structures, water conservancy engineering structures, and other various engineering structures. The subclass collection metrics corresponding to the disaster relief investment information are the earthquake disaster relief investment costs. The subclass collection metrics corresponding to the other information are the table of abnormal damage conditions outside the assessment area, the economic losses outside the assessment area, other losses, the general situation of the natural environment of the disaster area and the seismogenic tectonic environment, the distribution map of the earthquake disaster assessment area, the earthquake intensity distribution map, and the sampling point distribution map;

[0031] S3: Construct a number of data tables or maps based on the superclass collection metrics and the subclass collection metrics;

[0032] S4: Obtain the earthquake vulnerability database by collecting attribute data and adding it to the data tables, and collecting spatial data and adding it to the maps.

[0033] The source of the earthquake damage data of the present invention is not limited. For example, it comes from the "Compilation of Earthquake Disaster Loss Assessments in Mainland China" over the years, various earthquake disaster assessment reports, and major earthquake monographs, etc. It can also refer to the standards and specifications related to earthquake disaster loss assessment work, the specifications and standards related to earthquake resistance and disaster reduction, and the relevant standards and specifications such as the earthquake industry and basic geographic information. The earthquake catalog of the involved database is shown in Table 1.

[0034] Table 1 Earthquake Catalog of the Database

[0035]

[0036]

[0037] The data types of the destructive earthquake database of the present invention are not limited. For example, the database can contain three types of data in total: one is attribute data, such as the summary table of continental earthquake losses, the basic information table of earthquake disaster events, and the classification table of disaster area structure types, etc., which are mainly stored in the form of two-dimensional tables; the second is spatial data, including the distribution map of the earthquake disaster assessment area, the earthquake intensity distribution map, and the sampling point distribution Figure 4 types of thematic maps, which are mainly stored in vector and raster data; the third is various documents and drawings, such as the photos of the damage of structures collected, the photos of paper documents and maps, which are mainly stored in folders.

[0038] In some embodiments, a method for constructing an earthquake vulnerability database is involved. The large - category collection indicators further include information on disaster - causing earthquakes, evaluation and loss information of buildings, and casualty information. The small - category collection indicators corresponding to the information on disaster - causing earthquakes are annual reviews of earthquake disaster losses, basic earthquake parameters, and total direct economic losses. The small - category collection indicators corresponding to the information on disaster - causing earthquakes are annual reviews of earthquake disaster losses, basic earthquake parameters, and total direct economic losses. The small - category collection indicators corresponding to the casualty information are casualty investigations.

[0039] In the implementation of the present invention, as shown in Table 2, a data collection index system divided into 8 large categories and 21 sub - categories is established.

[0040] Table 2 Index System of China's Destructive Earthquake Database

[0041]

[0042] In some embodiments, a method for constructing an earthquake vulnerability database is involved. The data table includes field aliases, field names, field descriptions, whether it is non - null / required, data types, units, value ranges, default values, and disaster categories. The field names include storage numbers, earthquake numbers, earthquake names, latitudes, longitudes, occurrence times, magnitudes, focal depths, and administrative division codes. Preferably, for the storage number, it automatically increases by 1 for each added record. For example, the data table of earthquake disaster event information is as shown in Table 3.

[0043] Table 3 Data Table of Earthquake Disaster Event Information

[0044]

[0045] In some embodiments, a method for constructing an earthquake vulnerability database is involved. The data table includes several building structure earthquake damage loss tables based on intensity. Classify the building structure earthquake damage loss tables based on regional and temporal factors, statistically analyze the earthquake damage conditions of various typical building structures under different intensities, and obtain the damage state damage probability matrix for each region.

[0046] In some embodiments, a method for constructing an earthquake vulnerability database is involved. The earthquake vulnerability database further includes structured data and unstructured data. The structured data is used to construct an agent (intelligent query for numbers) that supports fast query and display of voice or text based on NL2SQL, and the unstructured data is used to construct an agent (intelligent question answering) that supports precise positioning and text analysis based on RAG. In these embodiments, the application of AI technology is increased to improve the convenience and efficiency of database use. Specifically, the database contains data such as the above-mentioned structured two-dimensional tables and unstructured graphic reports, and the commonly used RAG (Retrieval-augmented Generation) technology and intelligent agent technologies such as intelligent question answering and intelligent query for numbers in current large models are added to improve the efficient and convenient interaction of fast query, retrieval, and statistics of the database system, and the capabilities of large models are used to give preliminary analysis and suggestions.

[0047] In some other embodiments, a method for generating earthquake vulnerability curve data for a catastrophe model of an earthquake vulnerability database is involved, including the following steps:

[0048] Obtain a damage state loss rate matrix, where the data elements of the damage state loss rate matrix include damage states and the loss rates corresponding to the damage states;

[0049] Obtain a damage state failure probability matrix, where the data elements of the damage state failure probability matrix include the damage states and the failure probabilities corresponding to the damage states;

[0050] Multiply the loss rate and the failure probability corresponding to the same damage state to obtain an average loss rate;

[0051] Save the damage state and the average loss rate corresponding to the damage state to obtain earthquake vulnerability curve data for the catastrophe model.

[0052] The term "damage state loss rate matrix" refers to a data set with at least damage states and loss rates as data elements.

[0053] The term "damage state failure probability matrix" refers to a data set with at least damage states and failure probabilities as data elements.

[0054] The term "earthquake vulnerability curve data" refers to a data set with at least damage states and average loss rates as data elements.

[0055] In the embodiments of the present invention, according to the failure probability matrix (i.e., the damage state failure probability matrix), as shown in Equation 1, the average loss rate of a certain ground motion intensity index is calculated:

[0056]

[0057] Among them, MLR is the average loss rate under a given ground motion intensity index, DS is the damage state or damage level (1, 2, 3, 4, 5 represent the five damage levels of basically intact, slightly damaged, moderately damaged, severely damaged, and destroyed respectively), and P DS is the damage probability (or damage ratio) of a given damage state under a certain ground motion intensity index, and CDF DS is the loss rate (or loss ratio) under a given damage state. The CDF DS includes a minimum value, an average value, and a maximum value. The ground motion intensity index includes, but is not limited to, seismic intensity (I) or peak ground acceleration (PGA).

[0058] The CDF DS takes values according to the seismic performance difference value DSPF of the same building type in different damage states DS DS The DSPF DS includes three levels: excellent, medium, and poor. The value-taking rule is that when DSPF DS is "excellent", CDF DS takes the minimum value; when DSPF DS is "medium", CDF DS takes the average value; when DSPF DS is "poor", CDF DS takes the maximum value. In these embodiments, the judgment of the seismic performance difference value DSPF of the same building type in different damage states DS DS is increased to select the corresponding vulnerability curve, so as to more accurately evaluate the losses under earthquake disasters of housing buildings. For examples, please refer to Figure 6 Examples 1 to 3.

[0059] Example 1: A certain housing type is "reinforced concrete". The system judges that its DSPF DS is "excellent", and CDF DS should take the minimum value, that is, query Table 4. The value-taking results of CDF DS are: basically intact 0%, slightly damaged 6%, moderately damaged 16%, severely damaged 46%, destroyed 81%. That is, the vulnerability calculated at this value-taking level is for reinforced concrete with excellent seismic performance, and the losses in actual earthquake disasters will be relatively small.

[0060] Example 2: A certain housing type is "industrial factory building". The system judges that its DSPF DS is "medium", and CDF DS should take the average value, that is, query Table 4. CDF DSThe obtained values are as follows: basically intact 2%, slightly damaged 11%, moderately damaged 31%, severely damaged 73%, and destroyed 91%. That is, for industrial buildings with moderate seismic performance calculated at this value level, the losses in actual earthquake disasters will be in the middle range.

[0061] Example 3: For a certain type of building "masonry building", the system determines that its DSPF DS is "poor", and the CDF DS should take the maximum value. That is, by querying Table 4, the obtained values of CDF DS are as follows: basically intact 5%, slightly damaged 15%, moderately damaged 45%, severely damaged 100%, and destroyed 100%. That is, for reinforced concrete with excellent seismic performance calculated at this value level, the losses in actual earthquake disasters will be relatively large.

[0062] In some embodiments, a method for generating data of a vulnerability curve for a catastrophe model related to an earthquake vulnerability database is provided. The data elements of the damage state loss rate matrix and the damage state failure probability matrix further include building type and earthquake intensity.

[0063] The method for obtaining the damage state loss rate matrix of the present invention is not limited. Any known damage state loss rate matrix is within the selection range of the present invention. For example, according to the loss ratio values given in "GB / T 18208.4 - 2011 Earthquake Field Work - Part 4: Assessment of Direct Disaster Losses" (Table 1), the data elements of the damage state loss rate matrix that can be obtained from this table include damage state (damage level), loss rate (building loss ratio), and building type.

[0064] Table 4 Reference values of building loss ratio (%)

[0065]

[0066]

[0067] In some embodiments of the present invention, a method for generating vulnerability curve data for a catastrophe model is provided. The failure probability corresponding to the damage state is generated based on the ultimate damage state of the damage state and the exceedance probability of the ultimate damage state.

[0068] In some embodiments of the present invention, the failure probability P DS under the damage state is obtained by using the method of curve fitting the earthquake damage matrix of Formulas 2, 3, and 4.

[0069] P DS0 = 1 - (EP LS1 ) (2)

[0070] P DSk =(EPLS(k+1) )-(EP LSk ) (k = 1, 2, 3) (3)

[0071] P DS4 =(EP LS4 ) (4)

[0072] Wherein, P DS0 is the occurrence probability of the damage level DS0; EP LS1 is the exceedance probability of the ultimate failure state LS1; P DSk is the occurrence probability at each damage level, where DSk is the damage level (k = 0, 1, 2, 3, 4 represent the five damage levels of basically intact, slightly damaged, moderately damaged, severely damaged, and destroyed respectively); EP LSk is the exceedance probability under a certain ultimate failure state LSk, where LSk is the ultimate failure state (k = 1, 2, 3, 4 represent the four ultimate states of slightly damaged, moderately damaged, severely damaged, and destroyed respectively); EP LS(k+1) is the exceedance probability of the ultimate failure state LS(k + 1); P DS4 is the occurrence probability of the damage level DS4; EP LS4 is the exceedance probability of the ultimate failure state LS4.

[0073] In some embodiments of the present invention, it relates to a method for generating vulnerability curve data for a catastrophe model, and the exceedance probability of the ultimate failure state is generated through cumulative calculation based on the failure probability of the ultimate failure state.

[0074] In some embodiments of the present invention, it relates to a method for generating vulnerability curve data for a catastrophe model, and the cumulative calculation method is: using the lognormal distribution function to perform least - squares fitting on the ultimate failure state.

[0075] In some embodiments of the present invention, it relates to a method for generating vulnerability curve data for a catastrophe model, and the failure states include the failure state based on PGA and the failure state based on intensity.

[0076] In some embodiments of the present invention, considering that the peak ground acceleration is generally used as the input parameter of the ground motion intensity in the earthquake catastrophe model, referring to the corresponding relationship between the macro - intensity and the acceleration PGA given in "GB / T 17742 - 2008 Chinese Seismic Intensity Scale" (Table 5), similar to the above - mentioned calculation method of the average loss rate curve based on intensity, the exceedance probability curves of each ultimate failure state under PGA can also be fitted to obtain the average loss rate curve based on PGA.

[0077] Table 5 Corresponding relationship between seismic intensity and peak acceleration PGA

[0078] Seismic intensity VI VII VIII IX X Median PGA 0.63 1.25 2.50 5.00 10.00 PGA (m / s2) (0.45,0.89) (0.90,1.77) (1.78,3.53) (3.54,7.07) (7.08,14.14)

[0079] In some embodiments of the present invention, a method for generating vulnerability curve data for a catastrophe model is involved, and further includes a step of generating an expected damage loss of an insured object caused by an earthquake disaster, and the expected damage loss is generated based on the earthquake vulnerability curve data.

[0080] In the embodiments of the present invention, for calculating the insurance loss of an earthquake disaster, a more intuitive mathematical form of loss ratio (loss rate) is adopted to calculate the loss, rather than directly using the earthquake damage matrix or the exceedance probability curve in each damage state. At a given earthquake ground motion intensity IM (Intensity Measure), such as the macro intensity I (or peak ground acceleration PGA, spectral response value Sa), the expected damage loss amount of the insured object can be calculated by the following formula (5):

[0081] Expected damage loss = Replacement value * Average loss rate (5)

[0082] In some embodiments, a method for generating earthquake vulnerability curve data for a catastrophe model of an earthquake vulnerability database is involved, and the damage probability corresponding to the damage state is generated based on the ultimate damage state of the damage state and the exceedance probability of the ultimate damage state.

[0083] In some embodiments, a method for presenting a data table is involved, including the following steps:

[0084] Connect to the earthquake vulnerability database;

[0085] Present the earthquake damage conditions of different intensities in the region on a display based on the structure type.

[0086] Taking 25 destructive earthquakes in Yunnan Province from 1991 to 2000, where there are many earthquake disasters and various housing structure forms, as an example, the earthquake damage conditions of various typical housing structures under different intensities are divided into 5 damage states: basically intact, slightly damaged, moderately damaged, severely damaged, and destroyed, and the earthquake damage conditions and seismic performance of various typical housing structures are statistically analyzed to establish an earthquake damage matrix based on intensity.

[0087] 1. Earthquake damage conditions of typical housing structures

[0088] Figure 3 shows the damage of various typical house structures under different intensities in the destructive earthquakes in Yunnan Province from 1990 to 2000. Table 3 shows the damage of frame structures under different intensities. It can be seen from Table 6 and Figure 3 (a) that frame structures have good seismic performance. When the earthquake intensity is low, such as VI and VII, the frame structures are basically in a basically intact state or only slightly damaged, and do not affect the normal use of residents; when the earthquake intensity is high, such as VIII and IX, only a small part of the frame structures are likely to be seriously damaged, and most of them can be used normally or after repair. According to the requirements of the "Code for Seismic Design of Buildings" GBJ11-89 implemented at the time, the frame structures designed and constructed according to the code achieved the seismic fortification goal of "no damage in small earthquakes, repairable in medium earthquakes, and not collapsing in large earthquakes".

[0089] Table 6 Damage of frame structures under different earthquake intensities (%)

[0090]

[0091]

[0092] Table 7 shows the damage of brick-concrete structures under different earthquake intensities. It can be seen from Table 4 and Figure 3(b) that when the earthquake intensity is low, such as VI and VII, the damage of most brick-concrete structures is basically intact or slightly damaged, which does not affect normal use. When the earthquake intensity is high, such as in the VIII area, 46.5% of the brick-concrete structures are moderately damaged or severely damaged; in the IX area, the proportion of brick-concrete structures that are severely damaged or destroyed is as high as 49.1%, and nearly half of the houses cannot be used normally.

[0093] Table 7 Damage of brick-concrete structures under different earthquake intensities (%)

[0094] Intensity Substantially intact Slightly damaged Moderately damaged Heavily damaged Destroyed Ⅵ 75.26 20.97 3.76 0.00 0.00 Ⅶ 54.58 28.94 13.93 2.56 0.00 Ⅷ 13.32 39.67 34.65 11.82 0.55 Ⅸ 18.30 18.05 14.50 38.10 11.00

[0095] Table 5 shows the damage of the through-beam timber structure under different earthquake intensities. It can be seen from Table 8 and Figure 3(c) that when the earthquake intensity is low, the through-beam timber roof truss has good integrity due to its unique structural form and light roof weight, and has good earthquake resistance. However, when the earthquake intensity is high, such as in the Ⅷ degree zone, the through-beam timber roof truss structure has obviously insufficient earthquake resistance. The reason for this problem may be that the through-beam timber structure has a force system of one beam continuously penetrating multiple columns, and has strong integrity. When subjected to a large earthquake load, if individual weak links are damaged, other related parts will be unevenly stressed or unbalanced, resulting in serious damage or destruction of the entire structure.

[0096] Table 8 Damage of through-beam timber structures under different earthquake intensities (%)

[0097] Intensity Substantially intact Slightly damaged Moderately damaged Heavily damaged Destroyed Ⅵ 82.305 14.56 3.14 0.00 0.00 Ⅶ 79.78 13.88 6.35 0.00 0.00 Ⅷ 0 14.10 28.00 44.20 13.73

[0098] Table 6 shows the earthquake damage conditions of civil structures under different intensities. As can be seen from Table 9 and Figure 3(d), the seismic performance of civil structures is relatively poor. The cumulative probabilities of being basically intact or slightly damaged in the VI-degree area and VII-degree area are 91.6% and 77.6% respectively, and most houses can still be used normally after the earthquake. In the IX-degree area, more than 50% of the houses are severely damaged or destroyed and cannot be used after the earthquake.

[0099] Table 9 Earthquake damage conditions of civil structures under different intensities (%)

[0100] Intensity Substantially intact Slightly damaged Moderately damaged Heavily damaged Destroyed Ⅵ 66.98 24.56 7.58 0.85 0.01 Ⅶ 42.07 35.59 14.59 6.60 1.11 Ⅷ 9.84 36.47 37.23 14.62 1.80 Ⅸ 0.00 24.90 24.60 35.70 14.80

[0101] Table 7 shows the earthquake damage conditions of masonry-timber structures under different intensities. As can be seen from Table 10 and Figure 2(e), the seismic performance of masonry-timber structures is relatively poor. In the VI-degree area, the cumulative probability of being basically intact and slightly damaged of masonry-timber structures is 90.8%, and the vast majority of houses can still be used normally. In the VII-degree area, about 1 / 3 of the masonry-timber structures are moderately damaged or severely damaged.

[0102] Table 10 Earthquake damage conditions of masonry-timber structures under different intensities (%)

[0103] Intensity Substantially intact Slightly damaged Moderately damaged Heavily damaged Destroyed Ⅵ 66.07 24.69 7.66 1.59 0.00 Ⅶ 37.30 30.00 23.60 9.10 0.00

[0104] 2. Earthquake damage conditions under different intensities

[0105] Figure 4 The earthquake damage conditions in Yunnan region from 1990 to 2000 under different intensities are as follows. In the VI-degree area, the seismic performances of various structures are not very different. The frame structure and the through-tenon wooden truss structure are slightly better than other structure types, while the masonry-timber structure and the civil structure are relatively poor. In the VII-degree area, more than 80% of the frame structures, brick-concrete structures and through-tenon wooden truss structures are basically intact or slightly damaged, and the through-tenon wooden truss structure has the most prominent seismic performance. In the VIII-degree area, the seismic performance of the frame structure is significantly higher than that of other structures. The brick-concrete structure is slightly better than the civil structure, and the through-tenon wooden truss structure has the worst seismic performance. In the IX-degree area, the frame structure and the brick-concrete structure have good seismic performances, while the civil structure is severely damaged. It can be seen from this that the frame structure has the best seismic performance and is higher than other structures except in the VII-degree area. The civil structure and the masonry-timber structure have relatively poor seismic performances. The seismic performance of the brick-concrete structure is between that of the frame structure and the civil (masonry-timber) structure. The through-tenon wooden structure has good seismic performance in low-intensity areas but poor seismic performance in high-intensity areas.

[0106] As Figure 4 shown, in some embodiments, it relates to a seismic vulnerability database construction system. The system includes at least one processor, a display, and a memory that stores instructions. When the instructions are executed by at least one processor, the steps of the foregoing method are implemented.

[0107] Embodiments and functional operations of the subject matter described in this specification can be implemented in digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware, including the structures disclosed in this specification and structural equivalents thereof, or in a combination of one or more of the foregoing. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers for execution by, or to control the operation of, a data processing apparatus. The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including, by way of example, programmable processors, computers, or multiprocessors or multi-computers. The apparatus may include special purpose logic circuitry, e.g., an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The apparatus may also include code that creates an execution environment for the relevant computer programs, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A computer program (which may also be referred to or described as a program, software, a software application, a module, a software module, a script, or code) can be written in any form of programming language, including a compiled or interpreted language, or a declarative or procedural language, and the computer program can be deployed in any form, including as a stand-alone program or as a module, a component, a subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. The program may be stored in a portion of a file that holds other programs or data, e.g., in one or more scripts stored in a markup language document; in a single file dedicated to the relevant program; or in multiple co-related files, e.g., files that store one or more modules, subroutines, or portions of code. The computer program may be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. For purposes of sending an interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having: a display device, e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor, for displaying information to the user; and a keyboard and a pointing device such as a mouse or a trackball by which the user can send input to the computer. Other kinds of devices may also be used to send an interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic input, speech input, or tactile input.Additionally, a computer can interact with a user by sending a document to a device used by the user and receiving the document from that device; for example, by sending a web page to a web browser on the user's client device in response to a received request from the web browser. Although the present invention has been described in detail above with general descriptions, specific embodiments, and experiments, based on the present invention, some modifications or improvements can be made to it, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. A method for constructing an earthquake vulnerability database, characterized in that It includes the following steps: Define major collection indicators, which include socio-economic conditions, indoor and outdoor property loss information, engineering structure damage and loss information, disaster relief investment information, and other information; Define minor collection indicators corresponding to the major collection indicators. The minor collection indicators corresponding to the socio-economic conditions are the general situation of the earthquake-stricken area and the socio-economic environment of the disaster area. The minor collection indicators corresponding to the indoor and outdoor property loss information are the estimation of indoor property loss in residential and public houses, the loss of equipment of enterprises and institutions, and the outdoor property loss. The minor collection indicators corresponding to the engineering structure damage and loss information are the loss of lifeline system engineering structures, the loss of water conservancy engineering structures, and the loss of other various engineering structures. The minor collection indicators corresponding to the disaster relief investment information are the earthquake disaster relief investment costs. The minor collection indicators corresponding to the other information are the table of abnormal damage conditions outside the evaluation area, the economic losses outside the evaluation area, other losses, the general situation of the natural environment in the disaster area and the seismogenic tectonic environment, the distribution map of the earthquake disaster assessment area, the earthquake intensity distribution map, and the sampling point distribution map; Construct several data tables or maps based on the major collection indicators and the minor collection indicators; Collect attribute data and add it to the data table, and collect spatial data and add it to the map to obtain the earthquake vulnerability database.

2. The method for constructing a seismic vulnerability database according to claim 1, wherein The major collection indicators also include earthquake disaster information, building assessment and loss information, and casualty information; the minor collection indicators corresponding to the earthquake disaster information are the annual review of earthquake disaster losses, the basic earthquake parameters, and the total direct economic loss. The minor collection indicators corresponding to the earthquake disaster information are the annual review of earthquake disaster losses, the basic earthquake parameters, and the total direct economic loss. The minor collection indicators corresponding to the casualty information are the casualty investigation.

3. The method for constructing a seismic vulnerability database according to claim 1, characterized in that, The data table includes field aliases, field names, field descriptions, whether it is non-null / required, data types, units, value ranges, default values, and disaster categories; the field names include storage numbers, earthquake numbers, earthquake names, latitudes, longitudes, occurrence times, magnitudes, focal depths, and administrative division codes.

4. The method for constructing a seismic vulnerability database according to claim 1, wherein Preferably, for the storage number, it automatically increases by 1 each time a record is added by default.

5. The method for constructing an earthquake vulnerability database according to claim 1, wherein The data table includes several earthquake damage loss tables of building structures based on intensity; classify the earthquake damage loss tables of building structures based on intensity according to regional and temporal factors, statistically analyze the earthquake damage conditions of various typical building structures under different intensities, and obtain the damage state damage probability matrix for each region.

6. The method for constructing an earthquake vulnerability database according to claim 1, wherein It also includes that the earthquake vulnerability database includes structured data and unstructured data; the structured data is used to construct a voice or text query display intelligent agent (intelligent query of numbers) based on NL2SQL, and the unstructured data is used to construct a precise question and answer intelligent agent (intelligent question and answer) based on RAG, forming a flexible interactive database system that supports multi-form fast query visualization.

7. The method for generating seismic vulnerability curve data for the catastrophe model of the seismic vulnerability database according to any one of claims 1 to 6, characterized in that, It includes the following steps: Obtain a damage state loss rate matrix, and the data elements of the damage state loss rate matrix include damage states and the loss rates corresponding to the damage states; Obtain a damage state damage probability matrix, where the data elements of the damage state damage probability matrix include the damage state and the damage probability corresponding to the damage state; Multiply the loss rate corresponding to the same damage state by the damage probability to obtain an average loss rate; Save the damage state and the average loss rate corresponding to the damage state to obtain the seismic vulnerability curve data for the catastrophe model.

8. The method for generating seismic vulnerability curve data for the catastrophe model of the seismic vulnerability database according to claim 7, characterized in that, The data elements of the damage state loss rate matrix and the damage state damage probability matrix further include building type and earthquake intensity.

9. The method for presenting a data table according to any one of claims 1 to 8, characterized in that, It includes the following steps: Connect to the seismic vulnerability database; Based on the structural type, present the earthquake damage conditions of different intensities in the area on the display.

10. A seismic vulnerability database construction system, characterized in that The system includes at least one processor; and a memory that stores instructions, which, when executed by the at least one processor, implement the steps of the method according to any one of claims 1-9.

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