An anti-seismic risk assessment and maintenance decision method for a simply supported bridge serving near a fault

Abstract

The application discloses a kind of near fault service simply supported bridge seismic risk assessment and maintenance decision method, it is related to bridge engineering seismic performance assessment, service bridge life cycle risk assessment and bridge maintenance decision technical field.The method comprises the following steps: obtaining the service state data of target simply supported bridge;Build the service state characteristic vector of the target simply supported bridge, and identify the current degradation stage;Inversion equivalent mechanical parameters of pier, support and foundation, form structure state parameter set;The structure state parameter set is written into bridge seismic analysis model or its proxy model, and the state updating model matched with current service state is obtained;Build near-fault ground motion input sample and input into state updating model, extract displacement response index;Establish joint exceedance probability model, calculate the seismic risk probability under each damage state;Output bridge life risk grade and disposal strategy.The application can realize the whole chain technology closed loop of simply supported bridge from state perception, risk deduction to management decision.

Description

Technical Field

[0001] This invention relates to the fields of seismic performance assessment of bridge engineering, risk assessment of the entire life cycle of bridges in service, and bridge maintenance decision-making technology, specifically to a method for seismic risk assessment and maintenance decision-making of simply supported bridges in service near faults. Background Technology

[0002] Near-fault pulse-type ground motions are characterized by velocity pulses, long-period effects, and strong directionality. These motions can cause significant dynamic responses and residual deformations in critical components such as bridge piers, bearings, and foundations, thereby increasing the risk of bridge damage, failure, and even collapse.

[0003] Existing bridge seismic assessment methods typically establish finite element models or simplified analysis models based on the bridge's initial structural parameters. These models are then combined with standard response spectra, selected seismic waves, or incremental dynamic analysis methods to calculate the bridge's seismic response. Damage status is then determined based on indicators such as maximum displacement, maximum drift ratio, residual drift ratio, or component internal forces. While these methods can reflect bridge seismic performance to some extent, most assume that bridge material properties, component stiffness, and boundary conditions remain unchanged during service. This makes it difficult to reflect seismic performance degradation caused by factors such as chloride ion corrosion, steel reinforcement corrosion, concrete strength degradation, bearing aging, and changes in foundation condition.

[0004] In addition, existing seismic analyses of bridges near faults often focus on the characteristics of ground motion input, lacking a complete process for coupling analysis of ground motion near faults with the service degradation status of bridges; existing vulnerability analyses also often use a single damage index, which is difficult to simultaneously reflect the maximum deformation damage during the earthquake and the residual deformation damage after the earthquake, which may lead to inaccurate judgment of bridge damage probability and maintenance needs.

[0005] In terms of bridge maintenance and management, existing methods mostly rely on periodic inspection results, component damage levels, or empirical maintenance standards to determine maintenance measures. There is a lack of quantitative correlation between seismic risk assessment results and maintenance, reinforcement, operation management, or re-evaluation decisions, making it difficult to directly serve the full life cycle management of bridges serving near faults.

[0006] In summary, existing technologies have several drawbacks, including difficulty in dynamically reflecting the actual service status of bridges, difficulty in coupling service degradation with near-fault ground motion, insufficient accuracy of single damage index assessment, and difficulty in translating risk assessment results into maintenance decisions. Summary of the Invention

[0007] This invention provides a method for seismic risk assessment and maintenance decision-making for simply supported bridges serving near faults. It addresses the problems of existing seismic assessment methods for simply supported bridges serving near fault areas, which are subject to environmental erosion, material degradation, bearing aging, changes in foundation condition, and near-fault pulse-type ground motions during long-term service. These methods are unable to dynamically reflect the current service status of the bridge, cannot couple the effects of service degradation and near-fault ground motions, cannot simultaneously evaluate the maximum deformation damage during the earthquake and the residual deformation damage after the earthquake, and the assessment results are difficult to directly translate into maintenance decisions.

[0008] This invention is achieved through the following technical solution:

[0009] A method for seismic risk assessment and maintenance decision-making for simply supported bridges serving near faults includes the following steps:

[0010] Obtain service status data of the target simply supported bridge;

[0011] Based on the service status data, a service status feature vector of the target simply supported bridge is constructed, and the current degradation stage is identified according to the service status feature vector.

[0012] Based on the current degradation stage and the service state feature vector, the equivalent mechanical parameters of the piers, supports and foundations are inverted to form a set of structural state parameters;

[0013] The set of structural state parameters is written into the bridge seismic analysis model or its proxy model to obtain a state update model that matches the current service state.

[0014] Near-fault ground motion input samples are constructed and input into the state update model to extract the displacement response index of the structure under seismic loading.

[0015] A joint exceedance probability model is established based on the displacement response index to calculate the seismic risk probability of the target simply supported bridge under various damage states.

[0016] The bridge's full-life-cycle risk level and corresponding handling strategies are output based on the seismic risk probability.

[0017] In some optional embodiments, the service status data includes one or more of the following: environmental impact data, material degradation data, structural deformation data, support status data, foundation status data, and historical inspection or maintenance data.

[0018] In some optional embodiments, the service status feature vector is constructed using the following formula:

[0019] ;

[0020] in, For the service life of bridges, This refers to the environmental erosion level or chloride ion erosion index. For steel reinforcement corrosion rate, The remaining cross-sectional area of ​​the reinforcing steel is subject to time variation. The time-varying yield strength of the steel reinforcement. This refers to the time-varying compressive strength of concrete. This is an indicator of the deformation or cracking status of bridge piers. This refers to the equivalent stiffness of the support or the support state index. It is the basic equivalent constraint stiffness or basic state index. This refers to historical maintenance status indicators.

[0021] In some optional embodiments, the inversion equivalent mechanical parameters include:

[0022] Based on the initial diameter of the reinforcing bar, the depth of corrosion or the corrosion rate, determine the remaining effective diameter and remaining cross-sectional area of ​​the reinforcing bar, as well as the current yield strength, ultimate strength and ductile degradation parameters of the reinforcing bar;

[0023] Based on chloride ion erosion, protective layer cracking, confined zone damage, and service life, determine the current compressive strength, peak strain, ultimate strain, and confined effect parameters of the protective layer concrete and the core area concrete.

[0024] Based on the support type, shear deformation, aging cracking, slippage or voiding, the horizontal stiffness, vertical stiffness, equivalent damping, slippage friction parameters or limit state parameters of the support are inverted.

[0025] Based on foundation settlement, pile-soil interaction state, scour depth, soil stiffness degradation, or detection inversion results, determine the foundation equivalent constraint stiffness, pile-soil interaction spring parameters, or boundary constraint parameters.

[0026] In some alternative embodiments, equivalent mechanical parameters are inverted using detection data calibration or model correction methods.

[0027] In some optional embodiments, the set of structural state parameters includes one or more of the following: remaining cross-sectional area of ​​reinforcing bars, yield strength of reinforcing bars, compressive strength of concrete, constitutive parameters of concrete, pier cross-sectional stiffness, equivalent stiffness of supports, damping parameters of supports, equivalent stiffness of foundations, and pile-soil interaction parameters.

[0028] In some optional embodiments, the bridge seismic analysis model is a nonlinear finite element numerical model or a simplified dynamic calculation model, and the surrogate model is a probabilistic surrogate model or a machine learning surrogate model.

[0029] In some alternative embodiments,

[0030] When using a nonlinear finite element numerical model, the main beam, piers, supports, foundations, and pile-soil interactions are modeled separately.

[0031] When using a surrogate model, the surrogate model is trained using finite element calculation results, historical test data, or dynamic response samples to establish a mapping relationship between the service state feature vector, near-fault ground motion parameters, and bridge dynamic response, thereby quickly predicting the seismic response of bridges under different service states.

[0032] In some optional embodiments, constructing the near-fault seismic input sample includes:

[0033] Determine the fault distance, fault type, site category, design seismic group, target response spectrum, and natural vibration period of the bridge structure at the bridge site of the target bridge;

[0034] Select near-fault pulse-type ground motions that meet the requirements of fault distance, site conditions and response spectrum matching from the measured near-fault ground motion database, or generate artificial near-fault ground motions based on the target response spectrum and pulse parameters.

[0035] For the selected or generated near-fault ground motions, amplitude modulation is performed based on peak ground acceleration, peak ground velocity, velocity pulse amplitude, pulse period, or spectral acceleration to form input sample groups of near-fault ground motions at different intensity levels.

[0036] In some optional embodiments, establishing the joint exceedance probability model and calculating the seismic risk probability includes:

[0037] Set threshold values ​​for each displacement response index corresponding to each damage state;

[0038] Based on displacement response samples at various intensity levels, a multidimensional joint probability distribution model with seismic intensity as a condition variable is constructed.

[0039] The damage exceedance judgment domain is defined by the threshold values ​​of each displacement response index. When the sample response falls into this domain, the corresponding damage state is determined to have been reached.

[0040] Calculate the conditional probability of reaching or exceeding each damage state under a given seismic intensity, and use it as the seismic risk probability.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] This invention constructs feature vectors by introducing service status data and identifies degradation stages, inverting time-varying equivalent mechanical parameters of piers, bearings, and foundations to update the seismic analysis model. This transforms the evaluation model from a static initial design state to a dynamic reflection of the current actual service state. Furthermore, the updated state model is coupled with near-fault ground motion input samples, filling the technical gap in existing near-fault bridge seismic analysis that focuses on ground motion input characteristics but lacks coupling with the service degradation state system. Simultaneously, addressing the shortcomings of existing vulnerability analyses that often use single damage indicators and struggle to consider both maximum deformation damage and post-earthquake residual deformation damage, this scheme extracts the maximum drift ratio (MDR) and residual drift... By comparing displacement with RDR and establishing a joint exceedance probability model, the dual destructive effects of large displacement impact and large residual displacement unique to near-fault pulse-type ground motion are fully reflected in the damage probability assessment. Finally, by outputting the risk probability as a risk level and mapping it to maintenance, reinforcement, operation management or reassessment decisions, a quantitative correlation is established between seismic risk assessment results and life-cycle management measures. This systematically solves multiple technical problems pointed out in the background technology, such as the static nature of assessment benchmarks, the disconnect between service degradation and ground motion analysis, the insufficient accuracy of single indicators, and the disconnect between assessment and decision-making. It realizes a closed-loop technology for near-fault service simply supported bridges from state perception and risk extrapolation to management decision-making. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0044] Figure 1 A schematic diagram of the seismic risk assessment and maintenance decision-making method for simply supported bridges serving near faults provided in an embodiment of the present invention;

[0045] Figure 2 This is a reference schematic diagram of the feature vector establishment and degradation stage identification process provided in the embodiments of the present invention;

[0046] Figure 3 This is a schematic diagram illustrating the process of constructing an equivalent mechanical parameter inversion and update model provided in an embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram of the process for constructing seismic input samples and performing dynamic response analysis, provided in an embodiment of the present invention.

[0048] Figure 5This is a schematic diagram illustrating the mapping between displacement response index, exceedance probability, seismic risk level, and maintenance decision-making in an embodiment of the present invention.

[0049] Figure 6 This is a reference schematic diagram of the construction environment for a simply supported bridge provided in an embodiment of the present invention;

[0050] Figure 7 This is a schematic diagram illustrating the chloride ion corrosion process and material degradation law provided in the embodiments of the present invention;

[0051] Figure 8 This is a reference schematic diagram of the finite element model of a bridge and the constitutive relation of its materials provided in an embodiment of the present invention;

[0052] Figure 9 This is a reference schematic diagram of the response spectrum of selected seismic motions provided in an embodiment of the present invention;

[0053] Figure 10 This is a reference schematic diagram showing the vulnerability results of the displacement response index provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0055] Example 1

[0056] like Figure 1 As shown in the figure, this invention provides a method for seismic risk assessment and maintenance decision-making for simply supported bridges serving near faults, including the following steps:

[0057] S1, see reference Figure 2 To obtain service status data of the target simply supported bridge.

[0058] Service status data includes one or more of the following: environmental impact data, material degradation data, structural deformation data, support status data, foundation status data, and historical inspection or maintenance data.

[0059] The environmental erosion data specifically includes chloride ion erosion conditions, temperature and humidity conditions, freeze-thaw conditions, service life, fault distance at the bridge site, and site type in the area where the simply supported bridge is located; the material performance degradation data specifically includes concrete strength, protective layer thickness, rebar diameter, rebar corrosion rate, remaining cross-sectional area of ​​rebar, and degree of degradation of rebar yield strength; the structural deformation data specifically includes pier top displacement, pier bottom curvature, pier cracks, and main beam displacement; the connection structure service data specifically includes bearing aging, shear deformation, slippage, voids, cracking, and stiffness changes; the foundation working status data specifically includes foundation settlement, pile-soil interaction status, scour or soil degradation; and the historical maintenance data specifically includes all inspection reports, damage records, repair and reinforcement records, and operation and management records.

[0060] The above data can be collected by embedding various sensors in the bridge structure, including but not limited to strain gauges, displacement gauges, accelerometers, temperature and humidity sensors, etc.; data can also be collected through regular manual inspections, such as manual inspections of crack width, concrete rebound strength, and steel corrosion; and historical inspection and maintenance records can be retrieved from the bridge management system.

[0061] S2, see further. Figure 2 Based on the service status data, a service status feature vector of the target simply supported bridge is constructed, and the current degradation stage is identified according to the service status feature vector.

[0062] Specifically, parameters such as the service life, environmental erosion level, steel corrosion rate, remaining cross-sectional area of ​​steel bars, concrete compressive strength reduction factor, pier crack development degree, pier deformation index, bearing condition index, foundation condition index, and historical maintenance status of the target simply supported bridge are normalized so that the values ​​of each component are within the range of [0,1] to eliminate the influence of dimensions and construct a bridge service status feature vector.

[0063] The bridge service status feature vector can be specifically represented as:

[0064] ;

[0065] in, For the service life of bridges, This refers to the environmental erosion level or chloride ion erosion index. For steel reinforcement corrosion rate, The remaining cross-sectional area of ​​the reinforcing steel is subject to time variation. The time-varying yield strength of the steel reinforcement. This refers to the time-varying compressive strength of concrete. This is an indicator of the deformation or cracking status of bridge piers. This refers to the equivalent stiffness of the support or the support state index. It is the basic equivalent constraint stiffness or basic state index. This refers to historical maintenance status indicators.

[0066] In practice, the environmental erosion level The assessment can be based on a comprehensive evaluation of factors such as chloride ion concentration, carbon dioxide concentration, humidity, and temperature in the environment in which the bridge is located. As an example, according to the "Standard for Durability Testing and Evaluation of Highway Bridges", the environmental erosion level can be divided into five levels: no erosion, weak erosion, moderate erosion, strong erosion, and severe erosion, with values ​​of 0, 0.25, 0.5, 0.75, and 1.0, respectively.

[0067] Steel corrosion rate It can be obtained through half-cell potential method, linear polarization method or concrete resistivity method, or through chemical analysis after on-site sampling. For cases where direct detection is not possible, estimation can be performed based on Fick's second law.

[0068] ;

[0069] in, This is the vertical depth from the exposed concrete surface (the thickness of the protective layer is used when calculating the location of the reinforcing bars). For service At that time, the distance from the concrete surface Total chloride ion concentration at depth This represents the initial chloride ion concentration inside the concrete. The external environmental chloride ion equilibrium concentration of the exposed concrete surface. This represents the effective diffusion coefficient of chloride ions in concrete. It is the error complement function.

[0070] Time-varying residual cross-sectional area of ​​reinforcing bars It can be determined based on the initial cross-sectional area and corrosion rate of the reinforcing steel:

[0071] ;

[0072] in, This represents the initial cross-sectional area of ​​the reinforcing steel.

[0073] Time-varying yield strength of steel bars and time-varying compressive strength of concrete It can be determined based on the material degradation model.

[0074] The time-varying yield strength degradation of steel bars can be modeled as follows:

[0075] ;

[0076] in, The initial yield strength of the steel reinforcement. The strength degradation coefficient related to the corrosion rate, for The rate of steel corrosion at any given time.

[0077] The degradation of concrete compressive strength can be modeled using the following approach:

[0078] ;

[0079] in, This represents the initial compressive strength of the concrete. The strength degradation coefficient related to chloride ion corrosion and carbonization. This refers to the length of service.

[0080] Bridge pier deformation or crack condition indicators The damage status of bridge piers can be comprehensively assessed based on the measured inclination of the piers, the displacement of the pier top, the width of the cracks, and the distribution of the cracks. As an example, the "Technical Condition Assessment Standard for Highway Bridges" can be used as a reference to classify the damage status of bridge piers into five levels: intact, slightly damaged, moderately damaged, severely damaged, and dangerous, with values ​​of 0, 0.25, 0.5, 0.75, and 1.0, respectively.

[0081] Support equivalent stiffness or support state index The equivalent horizontal stiffness can be determined based on the bearing type (plate rubber bearing, pot bearing, lead-core rubber bearing, etc.) and its current aging, deformation, and damage state. Taking plate rubber bearings as an example, its equivalent horizontal stiffness can be calculated using the following formula:

[0082] ;

[0083] in, This refers to the shear modulus of rubber. The bearing area of ​​the support. This represents the total thickness of the rubber layer.

[0084] Basic equivalent constraint stiffness or basic state index The effective stiffness of the foundation can be determined by a combination of factors, including foundation type (spread foundation, pile foundation, etc.), soil type, foundation settlement, and scour depth. For pile foundations, the proportional coefficient m of the horizontal resistance coefficient of the soil along the pile can be determined using the m-method or the Py curve method, thereby determining the equivalent restraint stiffness of the foundation.

[0085] Historical maintenance status indicators The evaluation can be based on a comprehensive assessment of historical repair frequency, repair time, repair methods, and repair results.

[0086] Based on the bridge service status feature vector, threshold discrimination, comprehensive scoring discrimination, or model recognition methods are used to divide the current degradation stage of the bridge into the initial degradation stage, the developmental degradation stage, and the severe degradation stage.

[0087] The initial degradation stage refers to the stage where chloride ions have not yet caused significant steel corrosion or the corrosion rate is low, and the overall seismic performance of the piers, bearings, and foundations remains basically stable. The developmental degradation stage refers to the stage where steel begins to corrode, the concrete protective layer cracks or the material properties of the concrete decrease significantly, and the seismic performance of the bridge begins to decline. The severe degradation stage refers to the stage where the steel corrosion rate is high, the concrete protective layer cracks or peels significantly, the performance of the bearings or foundations deteriorates significantly, and the seismic capacity of the bridge decreases considerably.

[0088] In practical applications, the threshold values ​​for each degradation stage can be set based on the critical corrosion time of steel bars, steel bar corrosion rate, concrete strength reduction, pier crack width, bearing stiffness degradation ratio, and foundation deformation state; historical detection data and measured response data can also be used to correct the degradation stage discrimination results.

[0089] S3, such as Figure 3 As shown, based on the current degradation stage and the service state feature vector, the equivalent mechanical parameters of the piers, supports and foundations are inverted to form a set of structural state parameters.

[0090] Based on the current degradation stage of the bridge, a material degradation model is determined, and combined with the material degradation data in the service status feature vector, the current equivalent mechanical parameters of the steel reinforcement, concrete, bearings and foundations are calculated or inverted.

[0091] For the reinforcing steel in the bridge piers, based on the initial diameter of the reinforcing steel... rust depth or corrosion rate Determine the remaining effective diameter of the reinforcing bar. and remaining cross-sectional area And further determine the current yield strength of the steel reinforcement. Ultimate strength Ductility degradation parameter .

[0092] Corrosion depth It can be calculated using the following formula:

[0093] ;

[0094] in, For environmental factors; Water-cement ratio; The structural service life is expressed in years. The time it takes for the chloride ion concentration to reach the critical value, in years.

[0095] Remaining effective diameter of reinforcing bars It can be calculated using the following formula:

[0096] ;

[0097] in, This represents the initial diameter of the reinforcing bar.

[0098] Remaining cross-sectional area It can be calculated using the following formula:

[0099] .

[0100] Current yield strength of steel bars It can be calculated using the following formula:

[0101] ;

[0102] in, The initial yield strength of the steel reinforcement. The corrosion rate of the reinforcing steel. , The original mass of the reinforcing steel. The quality of the steel bars after corrosion.

[0103] Ultimate strength of steel bars Ductility degradation parameter It can be calculated using the following formula:

[0104] ;

[0105] ;

[0106] in, This represents the initial ultimate strength of the reinforcing steel. This represents the initial ultimate strain of the reinforcing steel. This is a coefficient related to the type of steel.

[0107] For bridge pier concrete, the current compressive strength, peak strain, ultimate strain, and constraint effect parameters of the protective layer concrete and the core area concrete are determined based on chloride ion erosion, protective layer cracking, confined zone damage, and service life.

[0108] Current compressive strength of protective concrete It can be calculated using the following formula:

[0109] ;

[0110] in, The value is the reduction factor for the concrete protective layer, and its specific value can be found in the "Standard for Durability Evaluation of Concrete Structures".

[0111] Current compressive strength of concrete in the core area It can be calculated using empirical formulas:

[0112] ;

[0113] in, Characteristic values ​​of the rusted stirrups , The volumetric bracing ratio after corrosion. This represents the yield strength of the stirrup after corrosion.

[0114] Peak strain of concrete and ultimate strain Determined based on current compressive strength and constraint effect parameters.

[0115] For supports, based on the support type, shear deformation, aging cracking, slippage or voiding, the horizontal stiffness, vertical stiffness, equivalent damping, slippage friction parameters or limit state parameters of the support are inverted.

[0116] Support types typically include plate rubber bearings, pot bearings, lead-core rubber bearings, or high-damping rubber bearings.

[0117] Taking plate rubber bearings as an example, their equivalent horizontal stiffness and equivalent vertical stiffness They can be calculated using the following formulas respectively:

[0118] ;

[0119] ;

[0120] in, This is the current shear modulus of the rubber. For the pressure-bearing area, The total thickness of the rubber. This refers to the rubber compression modulus.

[0121] Current shear modulus of rubber It can be calculated based on aging time and temperature history:

[0122] ;

[0123] in, The initial shear modulus, This is the aging degradation coefficient. This refers to the length of service.

[0124] For the foundation, the equivalent constraint stiffness, pile-soil interaction spring parameters, or boundary constraint parameters are determined based on foundation settlement, pile-soil interaction state, scour depth, soil stiffness degradation, or detection inversion results.

[0125] In practice, the py curve method, tz curve method, and qz curve method can be used to simulate the horizontal resistance of the pile side, the vertical frictional resistance of the pile side, and the vertical resistance of the pile end, respectively.

[0126] Furthermore, equivalent mechanical parameters can be obtained by using calibration of test data or model correction methods. Specifically, the measured frequency, mode shape, displacement response, pier top displacement, support displacement, or foundation deformation of the bridge are used as the target response, and the material strength, component stiffness, support stiffness, and foundation constraint stiffness are used as parameters to be updated. By minimizing the error between the measured response and the calculated response, equivalent mechanical parameters that match the current service status can be obtained.

[0127] S4, see further. Figure 3 The set of structural state parameters is written into the bridge seismic analysis model or its proxy model to obtain a state update model that matches the current service state.

[0128] The preferred calculation model for bridge seismic analysis is a three-dimensional nonlinear finite element model, but it can also be a simplified dynamic calculation model, a probabilistic proxy model, or a machine learning proxy model.

[0129] When using a three-dimensional nonlinear finite element model, the main beam, piers, supports, foundations, and pile-soil interactions can be modeled separately. Specifically, the main beam can be simulated using beam-column elements or elastic beam elements; piers and pile foundations can be simulated using nonlinear beam-column elements combined with fiber sections; reinforcing steel, protective concrete, and core concrete are modeled using their respective material constitutive relations; supports can be simulated using zero-length elements, spring elements, or connection elements; and foundations can be simulated using equivalent boundary springs, Py springs, TZ springs, and QZ springs to simulate pile-soil interactions.

[0130] After inputting the set of structural state parameters obtained in step S3 into the above model, the parameters of steel reinforcement material, concrete material, pier section, bearing stiffness, foundation boundary, and pile-soil interaction are updated to obtain a state update model that matches the current service state of the bridge.

[0131] When using a proxy calculation model, the proxy model can be trained using finite element calculation results, historical test data, or dynamic response samples to establish a mapping relationship between the service state feature vector, near-fault ground motion parameters, and bridge dynamic response, thereby quickly predicting the seismic response of bridges under different service states.

[0132] S5, see reference Figure 5 Near-fault ground motion input samples are constructed and input into the state update model to extract the displacement response index of the structure under seismic loading.

[0133] Specifically, near-fault ground motion input samples are constructed based on one or more parameters among the following: bridge site fault distance, fault type, site category, target response spectrum, velocity pulse amplitude, pulse period, and structural natural period. In actual implementation, the fault distance, fault type, site category, design earthquake group, target response spectrum, and natural period of the bridge structure at the target bridge site are first determined. Then, near-fault pulse-type ground motions that meet the requirements of fault distance, site conditions, and response spectrum matching are selected from the measured near-fault ground motion database, or artificial near-fault ground motions are generated based on the target response spectrum and pulse parameters.

[0134] For the selected or generated near-fault ground motions, amplitude modulation can be performed based on peak ground acceleration (PGA), peak ground velocity (PGV), velocity pulse amplitude, pulse period, or spectral acceleration (Sa) to form input sample groups of near-fault ground motions at different intensity levels.

[0135] By inputting near-fault ground motion at different intensity levels into the state update model, nonlinear dynamic time history analysis or incremental dynamic analysis is carried out to obtain the dynamic response of key components such as piers, bearings and foundations.

[0136] The displacement response indices include the maximum drift ratio (MDR) and the residual drift ratio (RDR). The maximum drift ratio (MDR) is used to characterize the maximum deformation damage of the pier or structural member during the earthquake and can be determined based on the ratio of the maximum relative displacement of the pier to the pier height. The residual drift ratio (RDR) is used to characterize the residual deformation damage of the pier or structural member after the earthquake and can be determined based on the ratio of the residual displacement to the pier height after the earthquake.

[0137] For multi-pier simply supported bridges, the MDR and RDR of each pier can be extracted separately, and the maximum value, control pier response value or weighted response value can be used as the engineering requirement parameters of the target bridge under the ground motion input sample.

[0138] S6, see reference Figure 6 Based on the displacement response index, a joint exceedance probability model is established to calculate the seismic risk probability of the target simply supported bridge under various damage states.

[0139] Specifically, based on the bridge damage state classification rules, MDR damage thresholds and RDR damage thresholds are set respectively to classify bridge damage states into minor damage, moderate damage, severe damage, and collapse or near collapse states.

[0140] After obtaining MDR and RDR sample response data at different ground motion intensity levels, a joint probability distribution model of MDR-RDR conditionally based on ground motion intensity parameters is established. This joint probability distribution model can be established using a two-dimensional log-normal distribution, Copula function, kernel density estimation, empirical statistical model, or other probability models.

[0141] For the k-th damage state, a damage exceedance region is defined by the MDR threshold and the RDR threshold. When the MDR in the sample response exceeds the MDR threshold of the corresponding damage state, or the RDR exceeds the RDR threshold of the corresponding damage state, or both meet the preset joint discrimination condition, the target bridge is determined to have reached or exceeded the damage state.

[0142] The joint exceedance probability of the target bridge reaching or exceeding various damage states under a given seismic intensity level is calculated as the seismic risk probability for the corresponding damage state. The seismic risk probability can be expressed as:

[0143] ;

[0144] in, Indicates the seismic intensity parameter Reaching or exceeding the first The probability of seismic risk for each damage state. Indicates the first The maximum drift ratio threshold corresponding to each damage state Indicates the first The residual drift ratio threshold corresponding to each damage state.

[0145] S7, see further. Figure 6 Based on the seismic risk probability, the bridge's full life-cycle risk level and corresponding handling strategies are output.

[0146] Specifically, based on the seismic risk probability under different damage states, a correspondence between the seismic risk probability and the seismic risk level of the bridge is established. The seismic risk level of the target bridge can be divided into four levels: low risk, medium risk, high risk, and extremely high risk.

[0147] When the probability of minor and moderate damage is low, and the probability of severe damage and collapse damage is also low, the bridge is classified as low-risk, and decisions on routine inspections and periodic testing are generated.

[0148] When the probability of moderate damage increases significantly, but the probability of severe damage and collapse damage remains within a controllable range, the bridge is classified as medium-risk, and decisions are made to shorten the inspection cycle, strengthen key monitoring of piers and bearings, and carry out special inspections or partial repairs.

[0149] When the probability of severe damage exceeds a preset threshold, or the probability of moderate damage increases rapidly with service time, the bridge is determined to be at a high risk level, and decisions are made on pier reinforcement, bearing replacement, foundation treatment, load and speed limits, operation management, or seismic review.

[0150] When the probability of collapse or near collapse exceeds a preset threshold, or when the bridge is in a severely degraded stage and the probability of damage under near-fault earthquake action increases significantly, the bridge is determined to be at an extremely high risk level, and decisions are made to immediately conduct a special assessment, emergency reinforcement, restrict traffic, divert traffic, suspend use for reassessment, or conduct post-earthquake emergency response.

[0151] In practical applications, a comprehensive risk index can also be constructed. :

[0152] ;

[0153] in, , , , These represent the exceedance probabilities for minor damage, moderate damage, severe damage, and collapse or near-collapse, respectively. , , , These represent the weights corresponding to each damage state, with higher weights for more severe damage states. This is based on the comprehensive risk index. The size of the bridge is used to classify it into different seismic risk levels and output corresponding maintenance decisions.

[0154] The correspondence between seismic risk level and maintenance decisions can be set as follows:

[0155] Low risk level: The seismic performance of the bridge basically meets the requirements, and routine inspections, periodic testing, and data archiving are carried out.

[0156] Medium risk level: Some bridge components show a downward trend in seismic performance. Measures include shortening the inspection cycle, setting up monitoring points, inspecting bearings, tracking cracks, and carrying out local repairs.

[0157] High-risk level: The seismic performance of key bridge components has significantly declined, and special inspections, pier reinforcement, bearing replacement, foundation reinforcement, load and speed limits, and seismic retesting are carried out.

[0158] Extremely high risk level: The bridge is at high risk of severe damage or collapse under near-fault earthquake action. Emergency assessment, traffic control, traffic restrictions, temporary support, emergency reinforcement or decommissioning for reassessment should be implemented.

[0159] Through the above steps, a complete technical process is formed, from service status data acquisition, degradation stage identification, equivalent mechanical parameter inversion, status update model establishment, near-fault ground motion input, dynamic response analysis, MDR-RDR joint risk probability calculation to maintenance decision output.

[0160] Example 2

[0161] This embodiment provides a specific application example of a method for seismic risk assessment and maintenance decision-making for simply supported bridges serving near faults.

[0162] This embodiment uses a reinforced concrete simply supported beam bridge in a near-fault area as the target bridge for illustration. The bridge is a four-span simply supported T-beam bridge with a span arrangement of 4×40m and a width of 11m. Both the piers and pile foundations have circular cross-sections; the pier diameter is 2.0m, and the pile foundation diameter is 2.2m. The maximum pier height is 39.46m. Both the piers and pile foundations are constructed of C30 concrete, and the bearings are ordinary plate rubber bearings. A schematic diagram of the target bridge structure can be found in [reference needed]. Figure 6 .

[0163] 1) Determine the basic information of the target simply supported bridge, including the bridge's location, bridge type, span arrangement, bridge width, pier height, pier cross-sectional dimensions, pile foundation dimensions, concrete strength grade, reinforcement configuration, bearing type, foundation type, service life, and fault conditions at the bridge site, as the basis for subsequent dynamic seismic risk assessment.

[0164] 2) Obtain service status data of the target simply supported bridge. Service status data includes environmental impact data, material degradation data, structural deformation data, bearing status data, foundation status data, and historical inspection or maintenance data.

[0165] The environmental impact data includes chloride ion erosion conditions, temperature and humidity conditions, freeze-thaw conditions, bridge site fault distance, fault type, and site category; the material degradation data includes steel reinforcement corrosion rate, remaining cross-sectional area of ​​steel reinforcement, steel reinforcement yield strength, concrete compressive strength, and concrete cover cracking; the structural deformation data includes pier top displacement, pier bottom curvature, pier body cracks, and residual deformation; the bearing condition data includes bearing aging, cracking, slippage, shear deformation, and stiffness degradation; and the foundation condition data includes foundation settlement, scour, and pile-soil interaction.

[0166] 3) For reference Figure 2 The illustrated feature vector establishment and degradation stage identification process involves organizing, filtering, and normalizing the aforementioned service status data to construct a bridge service status feature vector X. This feature vector X includes at least the bridge's service life, environmental erosion level, steel reinforcement corrosion rate, remaining cross-sectional area of ​​the steel reinforcement, steel reinforcement yield strength, time-varying compressive strength of concrete, pier deformation index, bearing equivalent stiffness, foundation equivalent constraint stiffness, and historical maintenance status index.

[0167] 4) Identify the current degradation stage of the target bridge based on the bridge's service status feature vector X. Specifically, threshold discrimination, comprehensive scoring, or model recognition methods can be used to divide the target bridge into the initial degradation stage, the developing degradation stage, and the severe degradation stage. The initial degradation stage is characterized by minimal steel reinforcement corrosion or minor material performance degradation; the developing degradation stage is marked by steel reinforcement corrosion, cracking of the concrete cover, and a decline in the bridge's seismic performance; and the severe degradation stage is characterized by high steel reinforcement corrosion rates, significant cracking or spalling of the concrete cover, and substantial degradation of the bearing or foundation performance.

[0168] 5) Calculate the critical corrosion time, corrosion rate, remaining effective diameter, remaining cross-sectional area, yield strength reduction, and concrete compressive strength reduction of the target bridge during its service life based on the chloride ion corrosion model. The chloride ion corrosion process and material degradation law can be determined according to... Figure 7 The diagram shows the chloride ion corrosion process and material degradation pattern.

[0169] 6) For reference Figure 3 The diagram shown illustrates the process of constructing an equivalent mechanical parameter inversion and update model. The current degradation stage, steel corrosion rate, concrete strength reduction, bearing aging state, foundation state parameters, and historical test response data are input into the equivalent mechanical parameter inversion module to invert the equivalent mechanical parameters of the piers, bearings, and foundations.

[0170] For the reinforcing bars in bridge piers, calculate the remaining cross-sectional area A of the reinforcing bars based on their initial diameter, corrosion depth, or corrosion rate. s (t), and further determine the time-varying yield strength of the steel reinforcement. And ductility degradation parameters; for bridge pier concrete, the time-varying compressive strength of concrete is determined based on the degree of chloride ion erosion, the cracking of the protective layer, and the service life. Peak strain, ultimate strain, and constraint effect parameters; for supports, determine the equivalent stiffness of the support based on its aging, cracking, slippage, or detachment. and Damping parameters and friction parameters; for the foundation, the equivalent stiffness of the foundation is determined based on the foundation settlement, scour depth, and pile-soil interaction state. And pile-soil interaction parameters.

[0171] 7) The structural state parameter set obtained from the inversion is written into the bridge seismic analysis calculation model to establish a state update model that matches the current service state. The structural state parameter set includes the remaining cross-sectional area of ​​the reinforcing steel. Yield strength of steel bars Concrete compressive strength Support equivalent stiffness and Basic equivalent stiffness And one or more of the pile-soil interaction parameters.

[0172] 8) Establish a three-dimensional nonlinear finite element model or proxy model of the target bridge. In this embodiment, OpenSEES can be used to establish a three-dimensional nonlinear finite element model of the target bridge. The main beam is simulated using beam-column elements or elastic beam elements, the piers and pile foundations are simulated using nonlinear beam-column elements combined with fiber cross-sections, the supports are simulated using zero-length elements or spring elements, and the pile-soil interaction is simulated using py, tz, and qz springs. For details, please refer to... Figure 8 The bridge finite element model and material constitutive relation are shown in the schematic diagram.

[0173] 9) Reference Figure 4 The diagram shown illustrates the process of constructing ground motion input samples and performing dynamic response analysis. It determines parameters such as fault distance, fault type, site category, target response spectrum, velocity pulse amplitude, pulse period, and structural natural vibration period at the bridge site of the target bridge, and constructs near-fault ground motion input samples accordingly.

[0174] Based on the bridge site conditions, near-fault pulse-type ground motions that meet the requirements are selected from the near-fault ground motion database, or artificial near-fault ground motions are generated based on the target response spectrum and velocity pulse parameters. In this embodiment, near-fault pulse-type ground motions with fault distances within the range of 0-20 km and site conditions similar to the target bridge can be selected. Amplitude modulation is then performed based on peak ground acceleration (PGA), spectral acceleration (Sa), peak ground velocity (PGV), or velocity pulse parameters to form multiple input sample groups of near-fault ground motions at different intensity levels. The selection of near-fault ground motions and the matching of response spectra can be found in [reference needed]. Figure 9 The diagram shows a reference schematic of the response spectrum of a selected seismic motion.

[0175] 10) Input the near-fault ground motion input sample group into the state update model, perform nonlinear dynamic time history analysis or incremental dynamic analysis, and obtain the dynamic response results of the target bridge under different seismic intensity levels, different service years, and different degradation stages. The dynamic response results include pier top displacement, pier bottom curvature, pier bottom bending moment, support displacement, foundation deformation, residual displacement, and internal force response of structural components.

[0176] The maximum drift ratio (MDR) and residual drift ratio (RDR) are extracted based on the dynamic response results. The MDR characterizes the maximum deformation damage to piers or structural members during seismic loading and can be determined by the ratio of the maximum relative displacement of the pier to its height. The RDR characterizes the residual deformation damage to piers or structural members after the seismic loading ends and can be determined by the ratio of the residual displacement to its height after the seismic loading ends. For multi-pier simply supported bridges, the MDR and RDR for each pier can be extracted separately, and the maximum response value, control pier response value, or weighted response value can be selected as the engineering requirement parameters for the target bridge.

[0177] refer to Figure 5 The diagram shown illustrates the mapping between displacement response indices, joint exceedance probability, seismic risk level, and maintenance decision. MDR and RDR sample response data obtained from various ground motion input samples are input into the joint exceedance probability model to calculate the seismic risk probability of the target bridge under different damage states.

[0178] Based on the bridge damage state classification rules, MDR (Minor Damage Detection) and RDR (Reduced Damage Detection) thresholds are set to classify bridge damage states into minor damage, moderate damage, severe damage, and collapse or near collapse. In one embodiment, the MDR threshold can be set to 0.7%, 1.5%, 2.5%, and 5.0%, respectively, and the RDR threshold can be set to 0.2%, 0.5%, 1.0%, and 1.75%, respectively. When the MDR exceeds the corresponding damage threshold, or the RDR exceeds the corresponding damage threshold, or both meet the preset joint discrimination conditions, the target bridge is determined to have reached or exceeded the corresponding damage state.

[0179] Based on the MDR-RDR joint transcendence probability model, the probability of minor injury is calculated separately. Probability of moderate injury Probability of severe injury and the probability of collapse or near collapse Furthermore, the seismic risk probability of the target bridge under different service stages, different seismic intensity levels, and different damage states was obtained. The vulnerability calculation results of the MDR-RDR dual control index are shown below. Figure 10 The displacement response index vulnerability results are shown in the schematic diagram.

[0180] Based on the probability of minor injury Probability of moderate injury Probability of severe injury and the probability of collapse or near collapse This function outputs the seismic risk level of the target bridge throughout its entire lifespan. The seismic risk level can be categorized as low risk, medium risk, high risk, and extremely high risk. Low risk indicates that the bridge's seismic performance basically meets the requirements; medium risk indicates that the seismic performance of some bridge components shows a downward trend; high risk indicates that the seismic performance of key bridge components has significantly decreased; and extremely high risk indicates that the bridge faces a high risk of severe damage or collapse under near-fault earthquake action.

[0181] 11) Generate corresponding maintenance, reinforcement, operation management, or reassessment decisions based on the seismic risk level of the target bridge. When the target bridge is low-risk, generate decisions for routine inspections, periodic testing, and data archiving; when the target bridge is medium-risk, generate decisions for shortening the inspection cycle, key monitoring, bearing inspection, crack tracking, and local repairs; when the target bridge is high-risk, generate decisions for specialized testing, pier reinforcement, bearing replacement, foundation treatment, load and speed limits, and seismic reassessment; when the target bridge is extremely high-risk, generate decisions for emergency assessment, traffic control, traffic restrictions, temporary support, emergency reinforcement, or decommissioning for reassessment.

[0182] Through the above steps, this embodiment realizes a complete technical process from target bridge service status data acquisition, degradation stage identification, equivalent mechanical parameter inversion, state update model construction, near-fault ground motion input sample construction, dynamic response analysis, MDR-RDR joint seismic risk probability calculation to maintenance decision output. This implementation method can reflect the seismic risk changes of simply supported beam bridges in service near faults under the combined action of chloride ion erosion and near-fault pulse-type ground motion, and can further transform the risk assessment results into maintenance, reinforcement, operation management, or reassessment decisions.

[0183] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for seismic risk assessment and maintenance decision-making for simply supported bridges serving near faults, characterized in that, Includes the following steps: Obtain service status data of the target simply supported bridge; Based on the service status data, a service status feature vector of the target simply supported bridge is constructed, and the current degradation stage is identified according to the service status feature vector. Based on the current degradation stage and the service state feature vector, the equivalent mechanical parameters of the piers, supports and foundations are inverted to form a set of structural state parameters; The set of structural state parameters is written into the bridge seismic analysis model or its proxy model to obtain a state update model that matches the current service state. Near-fault ground motion input samples are constructed and input into the state update model to extract the displacement response index of the structure under seismic loading. A joint exceedance probability model is established based on the displacement response index to calculate the seismic risk probability of the target simply supported bridge under various damage states. The bridge's full-life-cycle risk level and corresponding handling strategies are output based on the seismic risk probability.

2. The method according to claim 1, characterized in that, The service status data includes one or more of the following: environmental impact data, material degradation data, structural deformation data, support status data, foundation status data, and historical inspection or maintenance data.

3. The method according to claim 1, characterized in that, The service status feature vector is constructed using the following formula: ； in, For the service life of bridges, This refers to the environmental erosion level or chloride ion erosion index. For steel reinforcement corrosion rate, The remaining cross-sectional area of ​​the reinforcing steel is subject to time variation. The time-varying yield strength of the steel reinforcement. This refers to the time-varying compressive strength of concrete. This is an indicator of the deformation or cracking status of bridge piers. This refers to the equivalent stiffness of the support or the support state index. It is the basic equivalent constraint stiffness or basic state index. This refers to historical maintenance status indicators.

4. The method according to claim 1, characterized in that, Inversion equivalence parameters include: Based on the initial diameter of the reinforcing bar, the depth of corrosion or the corrosion rate, determine the remaining effective diameter and remaining cross-sectional area of ​​the reinforcing bar, as well as the current yield strength, ultimate strength and ductile degradation parameters of the reinforcing bar; Based on chloride ion erosion, protective layer cracking, confined zone damage, and service life, determine the current compressive strength, peak strain, ultimate strain, and confined effect parameters of the protective layer concrete and the core area concrete. Based on the support type, shear deformation, aging cracking, slippage or voiding, the horizontal stiffness, vertical stiffness, equivalent damping, slippage friction parameters or limit state parameters of the support are inverted. Based on foundation settlement, pile-soil interaction state, scour depth, soil stiffness degradation, or detection inversion results, determine the foundation equivalent constraint stiffness, pile-soil interaction spring parameters, or boundary constraint parameters.

5. The method according to claim 4, characterized in that, Equivalent mechanical parameters are retrieved using calibration based on test data or model correction methods.

6. The method according to claim 1, characterized in that, The set of structural state parameters includes one or more of the following: residual cross-sectional area of ​​reinforcing bars, yield strength of reinforcing bars, compressive strength of concrete, constitutive parameters of concrete, pier cross-sectional stiffness, equivalent stiffness of supports, damping parameters of supports, equivalent stiffness of foundations, and pile-soil interaction parameters.

7. The method according to claim 1, characterized in that, The bridge seismic analysis model is a nonlinear finite element numerical model or a simplified dynamic calculation model, and the surrogate model is a probabilistic surrogate model or a machine learning surrogate model.

8. The method according to claim 7, characterized in that, When using a nonlinear finite element numerical model, the main beam, piers, supports, foundations, and pile-soil interactions are modeled separately. When using a surrogate model, the surrogate model is trained using finite element calculation results, historical test data, or dynamic response samples to establish a mapping relationship between the service state feature vector, near-fault ground motion parameters, and bridge dynamic response, thereby quickly predicting the seismic response of bridges under different service states.

9. The method according to claim 1, characterized in that, Constructing the near-fault ground motion input samples includes: Determine the fault distance, fault type, site category, design seismic group, target response spectrum, and natural vibration period of the bridge structure at the bridge site of the target bridge; Select near-fault pulse-type ground motions that meet the requirements of fault distance, site conditions and response spectrum matching from the measured near-fault ground motion database, or generate artificial near-fault ground motions based on the target response spectrum and pulse parameters. For the selected or generated near-fault ground motions, amplitude modulation is performed based on peak ground acceleration, peak ground velocity, velocity pulse amplitude, pulse period, or spectral acceleration to form input sample groups of near-fault ground motions at different intensity levels.

10. The method according to claim 1, characterized in that, Establishing the joint exceedance probability model and calculating the seismic risk probability includes: Set threshold values ​​for each displacement response index corresponding to each damage state; Based on displacement response samples at various intensity levels, a multidimensional joint probability distribution model with seismic intensity as a condition variable is constructed. The damage exceedance judgment domain is defined by the threshold values ​​of each displacement response index. When the sample response falls into this domain, the corresponding damage state is determined to have been reached. Calculate the conditional probability of reaching or exceeding each damage state under a given seismic intensity, and use it as the seismic risk probability.

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