A method and system for optimization of retrofitting strategy of building group considering serviceability degradation

Abstract

This invention provides a method and system for optimizing the reinforcement strategy of building complexes considering service performance degradation. The method includes: reducing the mechanical performance and deformation capacity parameters of each building; establishing a seismic damage analysis model for the building complex; obtaining the seismic damage results in the unreinforced state and determining the repair requirements; constructing a post-earthquake recovery system dynamic model of the building complex in the unreinforced state; and outputting the repair costs and repair time for each building and the building complex as a whole. The method further includes: determining the building reinforcement objectives and adjusting the mechanical performance and deformation capacity parameters of the building model; selecting different reinforcement strategies under economic constraints and obtaining the repair costs and repair time for the building complex under different reinforcement strategies; calculating the cost-to-resource ratio under different reinforcement strategies based on the repair costs and repair time in the unreinforced and reinforced states; and iterating and comparing different reinforcement strategies to determine the optimal building complex reinforcement strategy scheme under limited economic input. This invention can optimize the reinforcement strategy for building complexes.

Description

Technical Field

[0001] This invention belongs to the field of earthquake engineering, urban renewal and resilience assessment technology, and specifically refers to an optimization method and system for building complex reinforcement strategies that take into account service performance degradation. Background Technology

[0002] As buildings age, factors such as aging structural materials, deterioration of construction, and outdated design standards lead to a gradual decline in their mechanical properties and deformation capacity, making them more vulnerable to earthquake damage. For many older building complexes, this performance degradation due to service life is a significant factor influencing the extent of post-earthquake damage and repair costs.

[0003] In urban renewal and seismic reinforcement practices, challenges such as limited reinforcement funds, a large number of buildings, and significant differences in reinforcement needs are commonly encountered. Existing research often focuses on the seismic assessment of individual buildings or the estimation of repair costs based on empirical models, lacking a systematic characterization of the impact of building service life degradation.

[0004] However, existing methods have not organically combined structural performance degradation caused by the service life of buildings, performance changes before and after reinforcement, and optimization of reinforcement strategies under limited funding conditions, making it difficult to provide systematic support for repair and reinforcement decisions at the building complex scale.

[0005] Therefore, how to rationally assess and optimize different reinforcement strategies by comprehensively considering the degradation characteristics of building service performance under limited economic input is of great significance for building complex reinforcement decisions. However, corresponding methods are currently lacking. Summary of the Invention

[0006] To address the technical problems existing in the prior art, the present invention provides a method and system for optimizing the reinforcement strategy of building complexes considering service performance degradation. The technical solution is as follows: On the one hand, a method for optimizing the reinforcement strategy of building complexes considering service performance degradation is provided, the method comprising: S1. Based on the basic information of the building complex and the service life of the buildings, the mechanical performance parameters and deformation capacity parameters of each building are reduced to establish a seismic damage analysis model for the building complex and obtain the seismic damage results of the buildings in the unreinforced state. S2. Based on the earthquake damage results of the unreinforced buildings, determine the repair needs of the building complex, construct a dynamic model of the post-earthquake recovery system of the building complex in the unreinforced state, simulate the post-earthquake repair process of the building complex, and output the repair costs and repair time of each building and the building complex as a whole. S3. Determine the building reinforcement target and adjust the mechanical properties and deformation capacity parameters of the building model; S4. Under economic input constraints, select different reinforcement strategies, establish seismic damage analysis models of building complexes under different reinforcement strategies, obtain the seismic damage results of the reinforced buildings, and the corresponding repair costs and repair time for each building and the building complex as a whole. S5. Based on the repair costs and repair time in the unreinforced and reinforced states, calculate the cost-to-return ratio under different reinforcement strategies. S6. Iterate and compare different reinforcement strategies to determine the optimal reinforcement strategy for the building complex under limited economic investment.

[0007] Optionally, S1 specifically includes: S11. Based on the building's structural type and number of floors, a multi-degree-of-freedom structural model is used to perform equivalent modeling of the individual building, determining the skeleton line parameters for each floor, including: First, based on the seismic design code and seismic intensity requirements adopted by the building, the design bearing capacity of the structure of this floor is determined. Based on the building height and structural type, the natural vibration period of the structure is calculated using empirical formulas. Then, combined with the equivalent mass of this floor, the initial stiffness parameters of the structure are determined. Then, based on the initial stiffness parameters and the design bearing capacity, the design control points of the structure under the design conditions are determined; Based on the relationship between the design control points and the yield point, peak point, and ultimate point, the yield bearing capacity of the structure is calculated using the following formula. and peak load capacity The ultimate bearing capacity is 85% of the peak bearing capacity, ensuring that the seismic performance meets the requirements of this fortification level.

[0008] in, This is the ratio of the cracking bearing capacity to the design bearing capacity, i.e., the cracking overstrength coefficient; It is the ratio of peak bearing capacity to crack bearing capacity, i.e., peak overstrength coefficient; Then, based on the relationship between initial stiffness and yield bearing capacity, the displacement value corresponding to the yield point is calculated. The peak point displacement and ultimate point displacement are determined by existing experimental data or statistical regression methods, thereby determining the complete skeleton line of each layer. Based on this, considering the long-term service environment impact, the degree of material performance degradation is determined according to the construction year of the masonry dwelling. The deformation and mechanical parameters of the skeleton line characteristic points in the multi-degree-of-freedom structural model are reduced. The reduction coefficient is described using a piecewise linear function correlated with the service age. Specifically, the reduction coefficients for yield load, peak load, and ultimate load in the skeleton curve of the unfortified masonry dwelling are expressed as follows: , and Its relationship with service age t The relationship is represented as: (1) Yield load reduction factor:

[0009] (2) Peak load reduction factor:

[0010] (3) Ultimate load reduction factor:

[0011] Simultaneously, displacement reduction coefficients are also introduced for the corresponding yield displacement, peak displacement, and ultimate displacement parameters, which are expressed as follows: , and Its relationship with service age t The relationship is represented as: (1) Yield displacement reduction factor:

[0012] (2) Peak displacement reduction factor:

[0013] (3) Limit displacement reduction factor:

[0014] Then, under given seismic input conditions, the multi-degree-of-freedom structural model is subjected to seismic time history analysis to calculate the seismic response index of each floor of the building under seismic action. Based on the calculated seismic response index and in conjunction with existing seismic performance assessment standards or damage judgment criteria, the seismic damage level of the building in its unreinforced state is determined.

[0015] Optionally, S2 specifically includes: After obtaining the earthquake damage results in the unreinforced state, the building repair needs are determined based on the damage status. A system dynamics model is constructed using AnyLogic software to establish the causal relationship chain between various parameters, and the dynamic evolution of the repair process is described through positive and negative feedback mechanisms. Increased investment in repair resources can improve repair construction efficiency, thereby accelerating the repair progress and shortening the repair time. However, it can also lead to faster capital consumption and increased repair costs. This process constitutes a positive feedback relationship between repair resource investment and repair costs. As the restoration work progresses, the number of remaining buildings to be restored and the restoration needs gradually decrease, the restoration progress tends to slow down, and the intensity of restoration resource input gradually decreases, thus constraining the restoration process. This process constitutes a negative feedback relationship between the restoration progress and the remaining restoration needs. Based on the aforementioned positive and negative feedback relationships, a dynamic model of the post-earthquake recovery system of the building complex is constructed. By setting the initial level of repair resource input, construction efficiency parameters, and financial constraints, the post-earthquake repair process of the building complex in its unreinforced state is dynamically simulated. During the simulation, the system dynamics model can dynamically update the amount of repair completed, the remaining repair needs, and the amount of funds consumed, thereby obtaining the evolution of repair progress, fund consumption, and repair time during the repair of the building complex. Finally, based on the system dynamics simulation results, the post-earthquake repair cost and repair time of the building complex in the unreinforced state are output. The repair cost is the total amount of funds invested in the entire repair process, and the repair time is the total time required for the building complex to reach the preset functional restoration target. Thus, the post-earthquake recovery analysis results of the unreinforced building complex are obtained.

[0016] Optionally, S3 specifically includes: The building reinforcement targets are determined based on the building structure type, service life, and current seismic performance. The building reinforcement objectives include: improving structural bearing capacity, improving structural seismic performance, reducing earthquake damage level, shortening post-earthquake repair time, or meeting the requirements of current seismic design codes; Based on the stated building reinforcement objectives, adjust the mechanical properties and deformation capacity parameters of the building model; The adjustment of the mechanical performance parameters of the building model includes: increasing the elastic modulus, shear modulus, material strength or equivalent stiffness parameters to reflect the improvement in structural stiffness and load-bearing capacity. The adjustment of the deformation capacity parameters of the building model includes: improving yield deformation, peak deformation, ultimate deformation, ductility coefficient, or energy dissipation capacity to reflect the improvement effect of the structure's seismic deformation capacity.

[0017] Optionally, S5 specifically includes: Based on the results output by the system dynamics model, the overall repair cost and overall repair time of the building complex in the unreinforced state and under different reinforcement strategies are compared. The repair cost reduction and repair time reduction of the building complex at the overall level after implementing the reinforcement strategy are calculated. Then, the reduction in overall repair cost of the building complex, the reduction in overall repair time, or a combination of the two according to a preset weight, are used as the overall repair benefits obtained by the building complex under the corresponding reinforcement strategy. The total reinforcement cost under this reinforcement strategy will be calculated. By calculating the ratio of the overall repair benefits of the building complex to the corresponding total reinforcement cost, the output-input ratio index of different reinforcement strategies at the building complex scale is obtained.

[0018] Optionally, S6 specifically includes: Genetic algorithms are used to iteratively optimize the reinforcement strategy for the building complex: First, the building complex reinforcement strategies are encoded, where each reinforcement strategy corresponds to a group of buildings to be reinforced and their reinforcement methods, and the total reinforcement investment does not exceed the given economic constraints. Then, using the overall output-input ratio of the building complex as the fitness function of the genetic algorithm, the overall repair cost and repair time of different strategies in the post-earthquake recovery process are calculated through the system dynamics model, thereby obtaining the corresponding fitness value; During the iterative process of the genetic algorithm, selection, crossover, and mutation operations are performed sequentially to continuously generate new reinforcement strategies and eliminate strategies with low fitness. In each generation iteration, the newly generated reinforcement strategies are re-analyzed for seismic damage and system dynamics simulation to update the overall output-input ratio of the building complex. When the overall output-input ratio of the building complex no longer improves significantly in several consecutive generations of iterations, or when the preset number of iterations is reached, the iteration process is terminated, and the reinforcement strategy with the optimal overall output-input ratio of the building complex under the constraint of limited economic input is output as the final optimization result.

[0019] Optionally, S6 further includes: Before initiating the iteration, the seismic damage results in the unreinforced state are used to calculate the weighting factor of each building's impact on the overall repair benefits of the building complex. This factor, used as a heuristic operator, is employed to prioritize high-value targets for reinforcement. Its calculation formula is as follows:

[0020] in, A single building The expected recovery time after the earthquake; The total recovery time of the building complex after the earthquake; A single building The expected post-earthquake repair costs; The total cost of the overall restoration of the building complex; Costs incurred for reinforcing the building before the earthquake; The numerator of the formula measures the "contribution rate" of the building to the overall system loss, while the denominator represents the input cost and weighting factors. The higher the value, the higher the return. When funds are limited, this strategy is prioritized for search. This factor is used to prioritize the reinforcement of building groups, eliminate redundant combinations with low return on investment, and thus achieve heuristic compression of the search space. Then, to avoid the high cost of physical simulation for each set of reinforcement strategies, a surrogate model is introduced as a computation accelerator. The initial reinforcement strategy population is randomly generated using a genetic algorithm. Each individual is encoded with reinforcement strength, measures and sequence. By performing a complete earthquake damage analysis and system dynamics simulation, the real cost-benefit ratio of these initial individuals is obtained as a training sample set. Next, a surrogate model is established using Gaussian process regression or radial basis function to learn the implicit mapping relationship between the reinforcement strategy encoding and the output-input ratio index. In subsequent genetic algorithm iterations, the offspring individuals are first entered into the surrogate model for rapid pre-evaluation. Only the high-quality individuals with the highest pre-evaluation values ​​are selected, and then a high-fidelity system dynamics verification is performed. During the iteration process, the real output-input ratio data obtained from the physical verification is continuously fed back to the surrogate model to dynamically adjust the model parameters, ensuring that the surrogate model has extremely high prediction accuracy in the later stages of the search. When the overall output-input ratio index growth rate of individuals in several consecutive generations is lower than the preset threshold, or when the preset number of iterations is reached, the iteration stops, and the optimal building complex reinforcement strategy scheme with the maximum overall output-input ratio under the constraint of limited economic input is output.

[0021] On the other hand, a system for optimizing the reinforcement strategy of building complexes considering service performance degradation is provided, the system comprising: The first module is used to establish a seismic damage analysis model for the building complex by reducing the mechanical performance parameters and deformation capacity parameters of each building based on the basic information of the building complex and the service life of the buildings, and obtaining the seismic damage results of the buildings in the unreinforced state. The construction module is used to determine the repair needs of the building complex based on the earthquake damage results of the unreinforced buildings, construct a dynamic model of the post-earthquake recovery system of the building complex in the unreinforced state, simulate the post-earthquake repair process of the building complex, and output the repair costs and repair time of each building and the building complex as a whole. The adjustment module is used to determine the building reinforcement target and adjust the mechanical properties and deformation capacity parameters of the building model; The second module is used to select different reinforcement strategies under economic input constraints, establish seismic damage analysis models of building complexes under different reinforcement strategies, obtain the seismic damage results of the reinforced buildings, and the corresponding repair costs and repair time for each building and the building complex as a whole. The calculation module is used to calculate the cost-to-return ratio under different reinforcement strategies based on the repair costs and repair time in the unreinforced and reinforced states. The iteration module is used to iterate and compare different reinforcement strategies to determine the optimal reinforcement strategy for the building complex under limited economic input.

[0022] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-described optimization method for building complex reinforcement strategies that take into account service performance degradation.

[0023] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement the above-described optimization method for building complex reinforcement strategies that takes into account service performance degradation.

[0024] The beneficial effects of the technical solution provided by this invention include at least the following: This invention provides a method and system for optimizing building complex reinforcement strategies that consider service performance degradation. By incorporating the reduction of building mechanical and deformation performance parameters based on the building's service life, it accurately reflects the degradation of the building complex's mechanical properties and deformation capacity in its unreinforced state. Combined with system dynamics modeling to simulate the post-earthquake recovery process of the building complex, it outputs repair costs and repair time, thereby providing optimized strategies for the repair and reinforcement of the building complex. Based on different reinforcement strategies, the mechanical performance and deformation capacity parameters of the buildings are adjusted to simulate the post-earthquake repair process after reinforcement, outputting the repair costs and repair time after reinforcement. By comparing the earthquake damage analysis results under different reinforcement strategies, the cost-benefit ratio is calculated, and an iterative optimization method is used to determine the optimal reinforcement strategy under limited funding conditions. This ensures optimal resource allocation and utilization efficiency, providing a scientific and reliable decision-making basis for urban renewal, post-disaster recovery, funding allocation, and disaster prevention and mitigation. It is a more scientific and efficient method for optimizing building complex reinforcement strategies. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of an optimization method for strengthening building complexes that takes into account service performance degradation, provided by an embodiment of the present invention. Figure 2 This is a block diagram of a building complex reinforcement strategy optimization system that takes into account service performance degradation, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0028] This invention provides an optimization method for strengthening building complexes that takes into account service performance degradation. This method can be implemented by an electronic device, which may be a terminal or a server. Figure 1 The diagram shown is a flowchart of the method. The processing flow may include the following steps: S1. Based on the basic information of the building complex and the service life of the buildings, the mechanical performance parameters and deformation capacity parameters of each building are reduced to establish a seismic damage analysis model for the building complex and obtain the seismic damage results of the buildings in the unreinforced state. Optionally, the building complex information includes the building's age, number of floors, building area, building structure type, building function and purpose, and initial mechanical performance indicators.

[0029] The mechanical performance parameters include elastic modulus, shear modulus, material strength, and equivalent cross-sectional stiffness, which are used to characterize the degradation of structural stiffness and load-bearing capacity.

[0030] The deformation capacity parameters include parameters such as yield deformation, peak deformation, ultimate deformation, ductility coefficient, and energy dissipation capacity, which are used to characterize the attenuation of the structure's seismic ductility and damage evolution capacity.

[0031] The earthquake damage results include at least the earthquake damage status of each building (intact, slightly damaged, moderately damaged, severely damaged, destroyed), earthquake response (time history information of inter-story drift angle, displacement, acceleration, and velocity), and residual deformation.

[0032] Optionally, basic information about the building complex within the study area is first obtained, including the building's construction date, structural type, number of stories, building area, functional use, and initial design mechanical performance indicators. Based on the building's construction date, the service life of each building is determined, and the building's mechanical and deformation performance parameters are reduced based on the service life to reflect the performance degradation caused by material aging, accumulated structural damage, and long-term use in the unreinforced state. Then, a three-dimensional analysis model of the building is established using finite element software, and under given seismic motion input conditions, the seismic response and damage of each building are calculated, outputting the seismic damage results in the unreinforced state.

[0033] Optionally, S1 specifically includes: S11. Based on the building's structural type and number of floors, a multi-degree-of-freedom structural model is used to perform equivalent modeling of the individual building, determining the skeleton line parameters for each floor, including: First, based on the seismic design code and seismic intensity requirements adopted by the building, the design bearing capacity of the structure of this floor is determined. Based on the building height and structural type, the natural vibration period of the structure is calculated using empirical formulas. Then, combined with the equivalent mass of this floor, the initial stiffness parameters of the structure are determined. Then, based on the initial stiffness parameters and the design bearing capacity, the design control points of the structure under the design conditions are determined; Based on the relationship between the design control points and the yield point, peak point, and ultimate point, the yield bearing capacity of the structure is calculated using the following formula. and peak load capacity The ultimate bearing capacity is 85% of the peak bearing capacity, ensuring that the seismic performance meets the requirements of this fortification level.

[0034] in, This is the ratio of the cracking bearing capacity to the design bearing capacity, i.e., the cracking overstrength coefficient; It is the ratio of peak bearing capacity to crack bearing capacity, i.e., peak overstrength coefficient; Then, based on the relationship between initial stiffness and yield bearing capacity, the displacement value corresponding to the yield point is calculated. The peak point displacement and ultimate point displacement are determined by existing experimental data or statistical regression methods, thereby determining the complete skeleton line of each layer. Based on this, considering the long-term service environment impact, the degree of material performance degradation is determined according to the construction year of the masonry dwelling. The deformation and mechanical parameters of the skeleton line characteristic points in the multi-degree-of-freedom structural model are reduced. The reduction coefficient is described using a piecewise linear function correlated with the service age. Specifically, the reduction coefficients for yield load, peak load, and ultimate load in the skeleton curve of the unfortified masonry dwelling are expressed as follows: , and Its relationship with service age t The relationship (unit: year) is expressed as follows: (1) Yield load reduction factor:

[0035] (2) Peak load reduction factor:

[0036] (3) Ultimate load reduction factor:

[0037] Simultaneously, displacement reduction coefficients are also introduced for the corresponding yield displacement, peak displacement, and ultimate displacement parameters, which are expressed as follows: , and Its relationship with service age tThe relationship (unit: year) is expressed as follows: (1) Yield displacement reduction factor:

[0038] (2) Peak displacement reduction factor:

[0039] (3) Limit displacement reduction factor:

[0040] The above piecewise linear degradation relationship and parameter values ​​are only examples. The degradation parameters under different regional environmental conditions, masonry material types and service conditions can be adjusted based on statistical analysis or test results.

[0041] Then, under given seismic input conditions, seismic time history analysis is performed on the multi-degree-of-freedom structural model to calculate the seismic response indices (displacement, velocity, acceleration, and inter-story drift angle, etc.) of each floor of the building under seismic action. Based on the calculated seismic response index and in conjunction with existing seismic performance assessment standards or damage judgment criteria, the seismic damage level of the building in its unreinforced state is determined.

[0042] S2. Based on the earthquake damage results of the unreinforced buildings, determine the repair needs of the building complex, construct a dynamic model of the post-earthquake recovery system of the building complex in the unreinforced state, simulate the post-earthquake repair process of the building complex, and output the repair costs and repair time of each building and the building complex as a whole. Optionally, the repair requirements for the building complex include the building repair area, repair resources, number of workers required for construction, number of machines required for construction, amount of capital investment, and expected repair time.

[0043] Based on the repair requirements, establish a causal relationship chain between each repair parameter and establish the system dynamics model.

[0044] The system dynamics model is used to simulate the post-earthquake repair process of the building complex, output the repair cost and repair time, and obtain the post-earthquake recovery analysis results of the unreinforced building complex.

[0045] Optionally, S2 specifically includes: After obtaining the earthquake damage results in the unreinforced state, the building repair needs are determined based on the damage status. A system dynamics model is constructed using AnyLogic software to establish the causal relationship chain between various parameters, and the dynamic evolution of the repair process is described through positive and negative feedback mechanisms. Increased investment in repair resources can improve repair construction efficiency, thereby accelerating the repair progress and shortening the repair time. However, it can also lead to faster capital consumption and increased repair costs. This process constitutes a positive feedback relationship between repair resource investment and repair costs. As the restoration work progresses, the number of remaining buildings to be restored and the restoration needs gradually decrease, the restoration progress tends to slow down, and the intensity of restoration resource input gradually decreases, thus constraining the restoration process. This process constitutes a negative feedback relationship between the restoration progress and the remaining restoration needs. Based on the aforementioned positive and negative feedback relationships, a dynamic model of the post-earthquake recovery system of the building complex is constructed. By setting the initial level of repair resource input, construction efficiency parameters, and financial constraints, the post-earthquake repair process of the building complex in its unreinforced state is dynamically simulated. During the simulation, the system dynamics model can dynamically update the amount of repair completed, the remaining repair needs, and the amount of funds consumed, thereby obtaining the evolution of repair progress, fund consumption, and repair time during the repair of the building complex. Finally, based on the system dynamics simulation results, the post-earthquake repair cost and repair time of the building complex in the unreinforced state are output. The repair cost is the total amount of funds invested in the entire repair process, and the repair time is the total time required for the building complex to reach the preset functional restoration target. Thus, the post-earthquake recovery analysis results of the unreinforced building complex are obtained.

[0046] S3. Determine the building reinforcement target and adjust the mechanical properties and deformation capacity parameters of the building model; Optionally, S3 specifically includes: The building reinforcement targets are determined based on the building structure type, service life, and current seismic performance. The building reinforcement objectives include: improving structural bearing capacity, improving structural seismic performance, reducing earthquake damage level, shortening post-earthquake repair time, or meeting the requirements of current seismic design codes; Based on the stated building reinforcement objectives, adjust the mechanical properties and deformation capacity parameters of the building model; The adjustment of the mechanical performance parameters of the building model includes: increasing the elastic modulus, shear modulus, material strength or equivalent stiffness parameters to reflect the improvement in structural stiffness and load-bearing capacity; The adjustment of the deformation capacity parameters of the building model includes: improving yield deformation, peak deformation, ultimate deformation, ductility coefficient, or energy dissipation capacity to reflect the improvement effect of the structure's seismic deformation capacity.

[0047] In one feasible implementation, the building's design parameters will be adjusted according to the corresponding seismic fortification standards, depending on the specific reinforcement target. If the target seismic fortification is 8 degrees, the building structure's design parameters will be calculated according to the 8-degree seismic fortification requirements in the relevant design codes. Based on the code requirements for an 8-degree seismic fortification target, important parameters such as the building's yield point and peak value will be calculated according to the 8-degree design requirements. If the reinforcement target is 7.5 degrees, calculations will be performed based on the corresponding design parameters to meet the seismic resistance requirements of a 7.5-degree seismic fortification, and parameters such as the yield point and peak value will be adjusted accordingly to ensure the structural safety of the repaired and reinforced building under the predetermined fortification strength.

[0048] S4. Under economic input constraints, select different reinforcement strategies, establish seismic damage analysis models of building complexes under different reinforcement strategies, obtain the seismic damage results of the reinforced buildings, and the corresponding repair costs and repair time for each building and the building complex as a whole. Given a total reinforcement budget or a single building reinforcement budget constraint, multiple reinforcement strategies are set.

[0049] The reinforcement strategy includes combinations of different reinforcement measures, different reinforcement intensities, or different reinforcement priorities for different buildings.

[0050] Based on the different reinforcement strategies, corresponding seismic damage analysis models for building clusters are constructed. Under the same seismic input conditions, seismic damage analysis is performed on building clusters under different reinforcement strategies to obtain seismic damage results that reflect the behavior of buildings after reinforcement.

[0051] Based on the earthquake damage results after reinforcement, a post-earthquake recovery system dynamic model of the building complex under the reinforced state is constructed. The structural form of the model is consistent with the system dynamic model under the unreinforced state. The model simulates the post-earthquake repair process and outputs the repair cost and repair time corresponding to each reinforcement strategy.

[0052] In one feasible implementation, given a total reinforcement funding constraint or a single building reinforcement budget constraint, multiple reinforcement strategies for building complexes are set. For each reinforcement strategy, based on the reinforcement objectives and parameter adjustment methods determined in S3, a corresponding seismic damage analysis model for the building complex is constructed, and seismic damage analysis is performed under the same seismic motion input conditions to obtain the seismic response and damage state of the building complex under each reinforcement strategy. After obtaining the seismic damage results after reinforcement, the repair requirements of the reinforced building complex are further determined based on the building damage state. Then, a post-earthquake recovery system dynamic model of the building complex in the reinforced state is constructed using AnyLogic software. The structural form of the model is consistent with the system dynamic model in the unreinforced state to ensure the comparability of the simulation results in the two states. By simulating the system dynamic model, the post-earthquake repair process of the building complex under different reinforcement strategies is simulated, and the repair costs and repair times corresponding to each reinforcement strategy, as well as the overall repair costs and repair times of the building complex, are output.

[0053] S5. Based on the repair costs and repair time in the unreinforced and reinforced states, calculate the cost-to-return ratio under different reinforcement strategies. Optionally, S5 specifically includes: Based on the results output by the system dynamics model, the overall repair cost and overall repair time of the building complex in the unreinforced state and under different reinforcement strategies are compared. The repair cost reduction and repair time reduction of the building complex at the overall level after implementing the reinforcement strategy are calculated. Then, the reduction in overall repair cost of the building complex, the reduction in overall repair time, or a combination of the two according to a preset weight, are used as the overall repair benefits obtained by the building complex under the corresponding reinforcement strategy. The total reinforcement cost under this reinforcement strategy will be calculated. By calculating the ratio of the overall repair benefits of the building complex to the corresponding total reinforcement cost, the output-input ratio index of different reinforcement strategies at the building complex scale is obtained.

[0054] S6. Iterate and compare different reinforcement strategies to determine the optimal reinforcement strategy for the building complex under limited economic investment.

[0055] Optionally, an iterative optimization method is adopted to screen and update different reinforcement strategies in rounds, with the goal of maximizing the overall output-input ratio of the building complex under constraints, and to dynamically adjust the reinforcement strategies.

[0056] When the reinforcement strategy fails to further improve the overall output-input ratio of the building complex under given economic input conditions, the iteration process terminates, and the optimal building complex reinforcement strategy under limited economic input conditions is output.

[0057] In one feasible implementation, a genetic algorithm is used to iteratively optimize the reinforcement strategy for the building complex.

[0058] Optionally, S6 specifically includes: Genetic algorithms are used to iteratively optimize the reinforcement strategy for the building complex: First, the building complex reinforcement strategies are encoded, where each reinforcement strategy corresponds to a group of buildings to be reinforced and their reinforcement methods, and the total reinforcement investment does not exceed the given economic constraints. Then, using the overall output-input ratio of the building complex as the fitness function of the genetic algorithm, the overall repair cost and repair time of different strategies in the post-earthquake recovery process are calculated through the system dynamics model, thereby obtaining the corresponding fitness value; During the iterative process of the genetic algorithm, selection, crossover, and mutation operations are performed sequentially to continuously generate new reinforcement strategies and eliminate strategies with low fitness. In each generation iteration, the newly generated reinforcement strategies are re-analyzed for seismic damage and system dynamics simulation to update the overall output-input ratio of the building complex. When the overall output-input ratio of the building complex no longer improves significantly in several consecutive generations of iterations, or when the preset number of iterations is reached, the iteration process is terminated, and the reinforcement strategy with the optimal overall output-input ratio of the building complex under the constraint of limited economic input is output as the final optimization result.

[0059] Optionally, to address the "computational explosion" problem caused by the geometrical increase in strategy combinations in large-scale building complexes with limited economic input, the following approach can be used to quickly lock in the optimal solution from a large number of possible reinforcement combinations.

[0060] Optionally, S6 further includes: Before initiating the iteration, the seismic damage results in the unreinforced state are used to calculate the weighting factor of each building's impact on the overall repair benefits of the building complex. This factor, used as a heuristic operator, is employed to prioritize high-value targets for reinforcement. Its calculation formula is as follows:

[0061] in, A single building The expected recovery time after the earthquake; The total recovery time of the building complex after the earthquake; A single building The expected post-earthquake repair costs; The total cost of the overall restoration of the building complex; Costs incurred for reinforcing the building before the earthquake; The numerator of the formula measures the "contribution rate" of the building to the overall system loss, while the denominator represents the input cost and weighting factors. The higher the value, the higher the return. When funds are limited, this strategy is prioritized for search. This factor is used to prioritize the reinforcement of building groups, eliminate redundant combinations with low return on investment, and thus achieve heuristic compression of the search space. Then, to avoid the high cost of physical simulation for each set of reinforcement strategies, a surrogate model is introduced as a computation accelerator. The initial reinforcement strategy population is randomly generated using a genetic algorithm. Each individual is encoded with reinforcement strength, measures and sequence. By performing a complete earthquake damage analysis and system dynamics simulation, the real cost-benefit ratio of these initial individuals is obtained as a training sample set. Next, a surrogate model is established using Gaussian process regression or radial basis function to learn the implicit mapping relationship between the reinforcement strategy encoding and the output-input ratio index. In subsequent genetic algorithm iterations, the offspring individuals are first entered into the surrogate model for rapid pre-evaluation. Only the high-quality individuals with the highest pre-evaluation values ​​are selected, and then a high-fidelity system dynamics verification is performed. During the iteration process, the real output-input ratio data obtained from the physical verification is continuously fed back to the surrogate model to dynamically adjust the model parameters, ensuring that the surrogate model has extremely high prediction accuracy in the later stages of the search. When the overall output-input ratio index growth rate of individuals in several consecutive generations is lower than the preset threshold, or when the preset number of iterations is reached, the iteration stops, and the optimal building complex reinforcement strategy scheme with the maximum overall output-input ratio under the constraint of limited economic input is output.

[0062] like Figure 2 As shown, this embodiment of the invention also provides a system for optimizing the reinforcement strategy of building complexes that considers service performance degradation. The system includes: The first module 210 is used to reduce the mechanical performance parameters and deformation capacity parameters of each building based on the basic information of the building complex and the service life of the buildings, establish a seismic damage analysis model of the building complex, and obtain the seismic damage results of the buildings in the unreinforced state. The construction module 220 is used to determine the repair needs of the building complex based on the earthquake damage results of the unreinforced buildings, construct a dynamic model of the post-earthquake recovery system of the building complex in the unreinforced state, simulate the post-earthquake repair process of the building complex, and output the repair costs and repair time of each building and the building complex as a whole. Adjustment module 230 is used to determine the building reinforcement target and adjust the mechanical properties and deformation capacity parameters of the building model; The second module 240 is used to select different reinforcement strategies under economic input constraints, establish seismic damage analysis models of building groups under different reinforcement strategies, obtain the seismic damage results of the reinforced buildings, and the corresponding repair costs and repair time for each building and the building group as a whole. The calculation module 250 is used to calculate the cost-to-return ratio under different reinforcement strategies based on the repair costs and repair time in the unreinforced and reinforced states. Iteration module 260 is used to iterate and compare different reinforcement strategies to determine the optimal reinforcement strategy for the building complex under limited economic input.

[0063] The building complex reinforcement strategy optimization system considering service performance degradation provided in this embodiment of the invention has a functional structure that corresponds to the building complex reinforcement strategy optimization method considering service performance degradation provided in this embodiment of the invention, and will not be described again here.

[0064] Figure 3 This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present invention. The electronic device 300 may vary considerably due to different configurations or performance. It may include one or more central processing units (CPUs) 301 and one or more memories 302. The memory 302 stores at least one instruction, which is loaded and executed by the processor 301 to implement the steps of the above-mentioned optimization method for building complex reinforcement strategy considering service performance degradation.

[0065] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions that can be executed by a processor in a terminal to complete the aforementioned method for optimizing the reinforcement strategy of building complexes taking into account service performance degradation. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0066] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the reinforcement strategy of building complexes considering service performance degradation, characterized in that, The method includes: S1. Based on the basic information of the building complex and the service life of the buildings, the mechanical performance parameters and deformation capacity parameters of each building are reduced to establish a seismic damage analysis model for the building complex and obtain the seismic damage results of the buildings in the unreinforced state. S2. Based on the earthquake damage results of the unreinforced buildings, determine the repair needs of the building complex, construct a dynamic model of the post-earthquake recovery system of the building complex in the unreinforced state, simulate the post-earthquake repair process of the building complex, and output the repair costs and repair time of each building and the building complex as a whole. S3. Determine the building reinforcement target and adjust the mechanical properties and deformation capacity parameters of the building model; S4. Under economic input constraints, select different reinforcement strategies, establish seismic damage analysis models of building complexes under different reinforcement strategies, obtain the seismic damage results of the reinforced buildings, and the corresponding repair costs and repair time for each building and the building complex as a whole. S5. Based on the repair costs and repair time in the unreinforced and reinforced states, calculate the cost-to-return ratio under different reinforcement strategies. S6. Iterate and compare different reinforcement strategies to determine the optimal reinforcement strategy for the building complex under limited economic investment.

2. The method according to claim 1, characterized in that, S1 specifically includes: S11. Based on the building's structural type and number of floors, a multi-degree-of-freedom structural model is used to perform equivalent modeling of the individual building, determining the skeleton line parameters for each floor, including: First, based on the seismic design code and seismic intensity requirements adopted by the building, the design bearing capacity of the structure of this floor is determined. Based on the building height and structural type, the natural vibration period of the structure is calculated using empirical formulas. Then, combined with the equivalent mass of this floor, the initial stiffness parameters of the structure are determined. Then, based on the initial stiffness parameters and the design bearing capacity, the design control points of the structure under the design conditions are determined; Based on the relationship between the design control points and the yield point, peak point, and ultimate point, the yield bearing capacity of the structure is calculated using the following formula. and peak load capacity The ultimate bearing capacity is 85% of the peak bearing capacity, ensuring that the seismic performance meets the requirements of this fortification level. in, This is the ratio of the cracking bearing capacity to the design bearing capacity, i.e., the cracking overstrength coefficient; It is the ratio of peak bearing capacity to crack bearing capacity, i.e., peak overstrength coefficient; Then, based on the relationship between initial stiffness and yield bearing capacity, the displacement value corresponding to the yield point is calculated. The peak point displacement and ultimate point displacement are determined by existing experimental data or statistical regression methods, thereby determining the complete skeleton line of each layer. Based on this, considering the long-term service environment impact, the degree of material performance degradation is determined according to the construction year of the masonry dwelling. The deformation and mechanical parameters of the skeleton line characteristic points in the multi-degree-of-freedom structural model are reduced. The reduction coefficient is described using a piecewise linear function correlated with the service age. Specifically, the reduction coefficients for yield load, peak load, and ultimate load in the skeleton curve of the unfortified masonry dwelling are expressed as follows: , and Its relationship with service age t The relationship is represented as: (1) Yield load reduction factor: (2) Peak load reduction factor: (3) Ultimate load reduction factor: Simultaneously, displacement reduction coefficients are also introduced for the corresponding yield displacement, peak displacement, and ultimate displacement parameters, which are expressed as follows: , and Its relationship with service age t The relationship is represented as: (1) Yield displacement reduction factor: (2) Peak displacement reduction factor: (3) Limit displacement reduction factor: Then, under given seismic input conditions, the multi-degree-of-freedom structural model is subjected to seismic time history analysis to calculate the seismic response index of each floor of the building under seismic action. Based on the calculated seismic response index and in conjunction with existing seismic performance assessment standards or damage judgment criteria, the seismic damage level of the building in its unreinforced state is determined.

3. The method according to claim 1, characterized in that, S2 specifically includes: After obtaining the earthquake damage results in the unreinforced state, the building repair needs are determined based on the damage status. A system dynamics model is constructed using AnyLogic software to establish the causal relationship chain between various parameters, and the dynamic evolution of the repair process is described through positive and negative feedback mechanisms. Increased investment in repair resources can improve repair construction efficiency, thereby accelerating the repair progress and shortening the repair time. However, it can also lead to faster capital consumption and increased repair costs. This process constitutes a positive feedback relationship between repair resource investment and repair costs. As the restoration work progresses, the number of remaining buildings to be restored and the restoration needs gradually decrease, the restoration progress tends to slow down, and the intensity of restoration resource input gradually decreases, thus constraining the restoration process. This process constitutes a negative feedback relationship between the restoration progress and the remaining restoration needs. Based on the aforementioned positive and negative feedback relationships, a dynamic model of the post-earthquake recovery system of the building complex is constructed. By setting the initial level of repair resource input, construction efficiency parameters, and financial constraints, the post-earthquake repair process of the building complex in its unreinforced state is dynamically simulated. During the simulation, the system dynamics model can dynamically update the amount of repair completed, the remaining repair needs, and the amount of funds consumed, thereby obtaining the evolution of repair progress, fund consumption, and repair time during the repair of the building complex. Finally, based on the system dynamics simulation results, the post-earthquake repair cost and repair time of the building complex in the unreinforced state are output. The repair cost is the total amount of funds invested in the entire repair process, and the repair time is the total time required for the building complex to reach the preset functional restoration target. Thus, the post-earthquake recovery analysis results of the unreinforced building complex are obtained.

4. The method according to claim 1, characterized in that, S3 specifically includes: The building reinforcement targets are determined based on the building structure type, service life, and current seismic performance. The building reinforcement objectives include: improving structural bearing capacity, improving structural seismic performance, reducing earthquake damage level, shortening post-earthquake repair time, or meeting the requirements of current seismic design codes; Based on the stated building reinforcement objectives, adjust the mechanical properties and deformation capacity parameters of the building model; The adjustment of the mechanical performance parameters of the building model includes: increasing the elastic modulus, shear modulus, material strength or equivalent stiffness parameters to reflect the improvement in structural stiffness and load-bearing capacity; The adjustment of the deformation capacity parameters of the building model includes: improving yield deformation, peak deformation, ultimate deformation, ductility coefficient, or energy dissipation capacity to reflect the improvement effect of the structure's seismic deformation capacity.

5. The method according to claim 1, characterized in that, S5 specifically includes: Based on the results output by the system dynamics model, the overall repair cost and overall repair time of the building complex in the unreinforced state and under different reinforcement strategies are compared. The repair cost reduction and repair time reduction of the building complex at the overall level after implementing the reinforcement strategy are calculated. Then, the reduction in overall repair cost of the building complex, the reduction in overall repair time, or a combination of the two according to a preset weight, are used as the overall repair benefits obtained by the building complex under the corresponding reinforcement strategy. The total reinforcement cost under this reinforcement strategy will be calculated. By calculating the ratio of the overall repair benefits of the building complex to the corresponding total reinforcement cost, the output-input ratio index of different reinforcement strategies at the building complex scale is obtained.

6. The method according to claim 1, characterized in that, S6 specifically includes: Genetic algorithms are used to iteratively optimize the reinforcement strategy for the building complex: First, the building complex reinforcement strategies are encoded, where each reinforcement strategy corresponds to a group of buildings to be reinforced and their reinforcement methods, and the total reinforcement investment does not exceed the given economic constraints. Then, using the overall output-input ratio of the building complex as the fitness function of the genetic algorithm, the overall repair cost and repair time of different strategies in the post-earthquake recovery process are calculated through the system dynamics model, thereby obtaining the corresponding fitness value; During the iterative process of the genetic algorithm, selection, crossover, and mutation operations are performed sequentially to continuously generate new reinforcement strategies and eliminate strategies with low fitness. In each generation iteration, the newly generated reinforcement strategies are re-analyzed for seismic damage and system dynamics simulation to update the overall output-input ratio of the building complex. When the overall output-input ratio of the building complex no longer improves significantly in several consecutive generations of iterations, or when the preset number of iterations is reached, the iteration process is terminated, and the reinforcement strategy with the optimal overall output-input ratio of the building complex under the constraint of limited economic input is output as the final optimization result.

7. The method according to claim 6, characterized in that, The S6 further includes: Before initiating the iteration, the seismic damage results in the unreinforced state are used to calculate the weighting factor of each building's impact on the overall repair benefits of the building complex. This factor, used as a heuristic operator, is employed to prioritize high-value targets for reinforcement. Its calculation formula is as follows: in, A single building The expected recovery time after the earthquake; The total recovery time of the building complex after the earthquake; A single building The expected post-earthquake repair costs; The total cost of the overall restoration of the building complex; Costs incurred for reinforcing the building before the earthquake; The numerator of the formula measures the "contribution rate" of the building to the overall system loss, while the denominator represents the input cost and weighting factors. The higher the value, the higher the return. When funds are limited, this strategy is prioritized for search. This factor is used to prioritize the reinforcement of building groups, eliminate redundant combinations with low return on investment, and thus achieve heuristic compression of the search space. Then, to avoid the high cost of physical simulation for each set of reinforcement strategies, a surrogate model is introduced as a computation accelerator. The initial reinforcement strategy population is randomly generated using a genetic algorithm. Each individual is encoded with reinforcement strength, measures and sequence. By performing a complete earthquake damage analysis and system dynamics simulation, the real cost-benefit ratio of these initial individuals is obtained as a training sample set. Next, a surrogate model is established using Gaussian process regression or radial basis function to learn the implicit mapping relationship between the reinforcement strategy encoding and the output-input ratio index. In subsequent genetic algorithm iterations, the offspring individuals are first entered into the surrogate model for rapid pre-evaluation. Only the high-quality individuals with the highest pre-evaluation values ​​are selected, and then a high-fidelity system dynamics verification is performed. During the iteration process, the real output-input ratio data obtained from the physical verification is continuously fed back to the surrogate model to dynamically adjust the model parameters, ensuring that the surrogate model has extremely high prediction accuracy in the later stages of the search. When the overall output-input ratio index growth rate of individuals in several consecutive generations is lower than the preset threshold, or when the preset number of iterations is reached, the iteration stops, and the optimal building complex reinforcement strategy scheme with the maximum overall output-input ratio under the constraint of limited economic input is output.

8. A system for optimizing the reinforcement strategy of building complexes considering service performance degradation, characterized in that, The system includes: The first module is used to establish a seismic damage analysis model for the building complex by reducing the mechanical performance parameters and deformation capacity parameters of each building based on the basic information of the building complex and the service life of the buildings, and obtaining the seismic damage results of the buildings in the unreinforced state. The construction module is used to determine the repair needs of the building complex based on the earthquake damage results of the unreinforced buildings, construct a dynamic model of the post-earthquake recovery system of the building complex in the unreinforced state, simulate the post-earthquake repair process of the building complex, and output the repair costs and repair time of each building and the building complex as a whole. The adjustment module is used to determine the building reinforcement target and adjust the mechanical properties and deformation capacity parameters of the building model; The second module is used to select different reinforcement strategies under economic input constraints, establish seismic damage analysis models of building complexes under different reinforcement strategies, obtain the seismic damage results of the reinforced buildings, and the corresponding repair costs and repair time for each building and the building complex as a whole. The calculation module is used to calculate the cost-to-return ratio under different reinforcement strategies based on the repair costs and repair time in the unreinforced and reinforced states. The iteration module is used to iterate and compare different reinforcement strategies to determine the optimal reinforcement strategy for the building complex under limited economic input.

9. An electronic device comprising a processor and a memory, wherein the memory stores at least one instruction, characterized in that, The processor loads and executes at least one instruction to implement the cluster reinforcement strategy optimization method considering service performance degradation as described in any one of claims 1-7.

10. A computer-readable storage medium storing at least one instruction, characterized in that, The at least one instruction is loaded and executed by the processor to implement the building complex reinforcement strategy optimization method considering service performance degradation as described in any one of claims 1-7.

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