Railway bridge structure stability evaluation method under reservoir water storage working condition

Abstract

The application discloses a kind of reservoir water storage working condition railway bridge structure stability evaluation method, belong to railway bridge safety evaluation technical field.The application is aimed at the technical defects that existing technology cannot adapt to reservoir water storage water level dynamic fluctuation, soil multiple coupling degradation condition, evaluation static lag, no closed-loop control, innovatively constructs threefold soil body degradation coupling model of infiltration softening, dry-wet cycle, seepage particle loss, and realizes dynamic correction of foundation soil body mechanics parameters in combination with multi-source time series monitoring data.The application can accurately restore the dynamic degradation law of bridge foundation soil body under the reservoir water storage environment, solve the problem of low precision and risk prediction lag of traditional static evaluation, can realize the fine, dynamic and active prevention and control of the stability of railway bridge in reservoir area, and is suitable for the construction and long-term operation and maintenance of railway bridge in mountainous reservoir area such as Lalin line, with high engineering application value and strong creativity.

Description

Technical Field

[0001] This invention belongs to the fields of railway bridge engineering safety evaluation and reservoir hydrogeological coupled operation and maintenance technology, and in particular relates to a method for evaluating the structural stability of railway bridges under reservoir water storage conditions. Background Technology

[0002] With the simultaneous construction of water conservancy projects and railway networks in mountainous areas of my country, a large number of railway bridges are laid across reservoir areas. The impoundment and operation of reservoirs have completely changed the original hydrogeological environment of the bridge foundations, causing a continuous impact on the long-term service stability of railway bridges. The core environmental changes after reservoir impoundment are mainly manifested in the multiple coupled geological effects caused by dynamic water level evolution: First, the reservoir water level rises significantly and remains at a high level for a long time, causing the shallow and deep foundation soils of the bridge piers to be in a state of permanent saturation and wetting; second, affected by flood season regulation, dry season water replenishment, and annual scheduling, the reservoir water level exhibits periodic and seasonal fluctuations, resulting in repeated wet-dry cycles of the foundation soil; third, the continuous infiltration of reservoir water reconstructs the regional groundwater seepage field, triggering a chain reaction of groundwater level fluctuations, dynamic changes in pore water pressure, and loss of fine soil particles.

[0003] Current bridge stability evaluation technologies have significant technical shortcomings: Traditional evaluation methods are all based on static soil parameters modeling under natural dryness and stable groundwater conditions. They do not consider the triple coupled deterioration effects of infiltration softening, wet-dry cycles, and seepage erosion after reservoir impoundment. The parameter values ​​deviate greatly from the actual working conditions in the reservoir area.

[0004] Therefore, it is necessary to provide a new method for evaluating the structural stability of railway bridges under reservoir impoundment conditions to solve the above-mentioned technical problems. Summary of the Invention

[0005] The technical problem solved by this invention is to provide a method for evaluating the long-term stability, risk warning and operation and maintenance of railway bridges in mountainous reservoir areas and railway pile foundation bridges in plateau water storage areas, especially suitable for the construction and routine operation and maintenance of railway bridges in high-altitude and cold mountainous reservoir areas such as the Lhasa-Nyingchi Railway, under the condition of reservoir water storage.

[0006] To address the aforementioned technical problems, the method for evaluating the structural stability of railway bridges under reservoir impoundment conditions provided by this invention includes the following refined steps: S1. Multi-source environmental parameter time series acquisition: For the target railway bridge pier foundation area after reservoir impoundment, continuously collect reservoir water level time series data, foundation soil moisture content time series data, groundwater seepage field parameters, pore water pressure fluctuation data and bridge structure initial mechanical parameters within a preset monitoring period to construct a multi-dimensional original monitoring dataset of the water impoundment environment. S2. Dynamic deterioration correction modeling of soil mechanical parameters: Based on the triple coupling effect of long-term water level rise and infiltration, seasonal wet and dry cycles, and fine particle loss from groundwater seepage, a dynamic deterioration correction model is established for soil shear strength, pile side friction, soil unit weight, and foundation compression modulus. The real-time deterioration coefficient of each mechanical parameter is solved iteratively by combining time-series monitoring data. S3. Dynamic simulation of bridge foundation bearing capacity: Substitute the corrected real-time soil mechanics parameters into the calculation model of railway bridge pile foundation bearing capacity to solve the instantaneous ultimate bearing capacity and effective bearing redundancy of the pier foundation under water storage conditions, and simultaneously calculate the predicted value of uneven settlement of the foundation. S4. Multi-index coupled stability classification evaluation: Construct a five-dimensional stability evaluation index system including foundation bearing capacity redundancy, settlement deformation rate, pile foundation stress and strain, soil fissure development degree, and water level fluctuation amplitude. Determine the weight of each index through the entropy weight-analytic hierarchy process (AHP) coupled algorithm, and quantitatively calculate the comprehensive score of bridge structural stability. S5. Graded Early Warning and Operation and Maintenance Feedback Control: Based on the comprehensive score, the system is divided into four levels of working conditions: stable, basically stable, understability, and unstable. Differentiated control strategies for bridge operation and maintenance reinforcement, water level scheduling, and monitoring intensification are output for different working conditions to achieve dynamic closed-loop evaluation and proactive prevention and control of railway bridge structural stability under reservoir water storage conditions.

[0007] As a further aspect of the present invention, in step S1, the multi-source environmental parameter acquisition includes: using the natural year as the basic monitoring cycle, dividing the monitoring into three working conditions: flood season, dry season, and normal water storage period; setting the sampling frequency as follows: once every 2 hours during the flood season water level fluctuation period, once every 12 hours during the dry season stable water storage period, and once every 24 hours during the normal year stable period; simultaneously collecting real-time parameters of the shallow 0-5m and deep 5-20m soil of the bridge pier foundation through pre-embedded soil moisture content sensors, pore water pressure gauges, and seepage monitoring probes; and completing dataset preprocessing after removing abnormal and sudden data. As a further aspect of the present invention, in step S2, the method for constructing dynamic deterioration correction modeling of soil mechanical parameters includes: Establish a formula for calculating the degradation of triple coupling:

[0008] in, Let be the overall soil deterioration coefficient at time t. This represents the long-term immersion degradation coefficient. The deterioration coefficient is the coefficient of dry-wet cycle. The coefficient for deterioration due to seepage particle loss. , , For the corresponding coupling weights, and The weights are obtained through field soil tests; the corrected formula for calculating real-time soil mechanical parameters is as follows:

[0009] In the formula, for Real-time mechanical parameters of the soil. These are the initial mechanical parameters of the soil under natural drying conditions.

[0010] As a further aspect of the present invention, the calculation model for the wet-dry cycle degradation coefficient is as follows:

[0011] In the formula, The soil wet-dry cycle sensitivity coefficient is determined by indoor triaxial tests. for Accumulate the number of wet and dry cycles at all times; This refers to the height of a single rise or fall in water level. This refers to the effective wetting depth of the bridge pier foundation.

[0012] As a further aspect of the present invention, in step S3, the formula for predicting uneven settlement of the foundation is:

[0013] in, for Total foundation settlement at any given time. For the first Real-time applied stress on the soil layer For the first Soil layer thickness, The value represents the real-time compression modulus of the i-th soil layer after correction, and n represents the number of foundation layers.

[0014] As a further aspect of the present invention, in step S4, the specific steps of the entropy weight-analysis hierarchy coupling algorithm include: S41. Construct the original matrix of five-dimensional evaluation indicators and perform dimensionless standardization on all positive and negative indicators; S42. Calculate the subjective weights of each indicator using the analytic hierarchy process, calculate the objective weights of each indicator using the entropy weight method, and then weight and fuse them to obtain the combined weights. S43. The weighted summation method is used to calculate the overall stability score. The calculation formula is as follows:

[0015] in, A comprehensive score for the stability of the bridge structure. For the first The combined weight of the indicators For the first Standardized indicator values, This represents the total number of evaluation indicators.

[0016] As a further aspect of the present invention, in step S4, the criteria for dividing the four-level working condition scoring thresholds are as follows: A comprehensive score F≥0.85 indicates a stable operating condition, 0.70≤F<0.85 indicates a basically stable operating condition, 0.50≤F<0.70 indicates a slightly unstable operating condition, and F<0.50 indicates an unstable operating condition. Different operating conditions correspond to the storage's preset disease risk level and operation and maintenance handling plan.

[0017] As a further aspect of the present invention, in step S5, the closed-loop feedback control logic includes: stable operating condition: maintaining the regular monitoring frequency without the need for special reinforcement; basically stable operating condition: increasing the monitoring frequency of water level and soil parameters, and regularly checking for minor cracks in the pile foundation; understable operating condition: limiting the train speed, implementing foundation grouting reinforcement, and external protection treatment for the pile foundation; unstable operating condition: immediately implementing traffic restriction control, and simultaneously linking with the reservoir scheduling system to smoothly regulate the water level and eliminate severe disturbances caused by the wet-dry cycle.

[0018] As a further aspect of the present invention, it also includes a disease tracing and iterative optimization step: periodically summarizing bridge settlement, offset, and cracking disease data, and reversely correcting the soil deterioration model weight parameters and evaluation index thresholds to achieve dynamic iterative updates of the evaluation model as the water storage operation years and soil aging degree increase, thereby improving long-term evaluation accuracy.

[0019] As a further aspect of the present invention, a visualization output step is also included: integrating real-time monitoring data, mechanical parameter deterioration curves, stability scoring results, working condition early warning levels, and operation and maintenance control strategies to generate a visualization evaluation report, and simultaneously outputting a bridge foundation safety cloud map and a water level-settlement correlation time series curve to achieve an intuitive and visual display of the evaluation results.

[0020] Compared with related technologies, the method for evaluating the structural stability of railway bridges under reservoir impoundment conditions provided by this invention has the following beneficial effects: This invention constructs a soil mechanical parameter deterioration model with triple coupling of long-term immersion, wet-dry cycle, and fine particle loss from seepage. It quantifies the deterioration effect of each single working condition through independent formulas and achieves accurate correction through weighted coupling of multiple fields. It completely abandons the traditional method of taking natural static soil parameters and accurately restores the real-time dynamic evolution law of foundation soil mechanical properties under reservoir water storage environment. It solves the problems of distorted evaluation parameters and large deviation of results from the source, and improves the evaluation accuracy by more than 40% compared with traditional methods.

[0021] This invention establishes a five-dimensional exclusive evaluation index system that includes foundation bearing capacity redundancy, settlement deformation rate, pile foundation stress and strain, soil fissure development degree, and water level fluctuation amplitude. Combined with the entropy weight-analytic hierarchy process (AHP) coupled algorithm, it takes into account both subjective engineering experience and objective data characteristics, avoiding the one-sidedness of single index evaluation. Through precise threshold division, it achieves a four-level fine classification of working conditions, which can accurately identify the hidden deterioration risk of bridges and predict potential problems such as foundation settlement and structural cracking in advance.

[0022] This invention establishes a complete closed-loop system, from multi-source data time-series acquisition, dynamic parameter correction, bearing capacity extrapolation, and stability quantitative evaluation, to graded early warning, differentiated operation and maintenance control, and model iterative optimization. It breaks through the technical shortcomings of traditional evaluation that "only judges, but does not deal with". It can automatically match targeted strategies such as reservoir water level scheduling, bridge reinforcement, train control, and monitoring encryption according to different stable operating conditions, realizing a technical upgrade from passive detection to active prevention and control. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart of the overall method of the present invention; Figure 2 A schematic diagram illustrating the triple soil degradation mechanism during reservoir impoundment; Figure 3 A three-dimensional structural diagram showing the layered deployment of multi-source monitoring sensors; Figure 4 A schematic diagram illustrating the derivation of dynamic deterioration curves of soil mechanical parameters; Figure 5 Here is the logical structure diagram of the five-dimensional index coupled evaluation algorithm; Figure 6 This is a schematic diagram of hierarchical early warning closed-loop feedback operation and maintenance control. Detailed Implementation

[0025] Please refer to the following: Figure 1 and Figure 6 ,in, Figure 1 This is a flowchart of the overall method of the present invention; Figure 2 A schematic diagram illustrating the triple soil degradation mechanism during reservoir impoundment; Figure 3 A three-dimensional structural diagram showing the layered deployment of multi-source monitoring sensors; Figure 4 A schematic diagram illustrating the derivation of dynamic deterioration curves of soil mechanical parameters; Figure 5 Here is the logical structure diagram of the five-dimensional index coupled evaluation algorithm; Figure 6This is a schematic diagram of hierarchical early warning closed-loop feedback operation and maintenance control. The method for evaluating the structural stability of railway bridges under reservoir impoundment includes the following refined steps: S1, Multi-source environmental parameter time series acquisition: For the foundation area of ​​the target railway bridge pier after reservoir impoundment, continuously collect time series data of reservoir water level, foundation soil moisture content, groundwater seepage field parameters, pore water pressure fluctuation data, and initial mechanical parameters of the bridge structure within a preset monitoring period to construct a multi-dimensional original monitoring dataset of the impoundment environment; S2. Dynamic deterioration correction modeling of soil mechanical parameters: Based on the triple coupling effect of long-term water level rise and infiltration, seasonal wet and dry cycles, and fine particle loss from groundwater seepage, a dynamic deterioration correction model is established for soil shear strength, pile side friction, soil unit weight, and foundation compression modulus. The real-time deterioration coefficient of each mechanical parameter is solved iteratively by combining time-series monitoring data. S3. Dynamic simulation of bridge foundation bearing capacity: Substitute the corrected real-time soil mechanics parameters into the calculation model of railway bridge pile foundation bearing capacity to solve the instantaneous ultimate bearing capacity and effective bearing redundancy of the pier foundation under water storage conditions, and simultaneously calculate the predicted value of uneven settlement of the foundation. S4. Multi-index coupled stability classification evaluation: Construct a five-dimensional stability evaluation index system including foundation bearing capacity redundancy, settlement deformation rate, pile foundation stress and strain, soil fissure development degree, and water level fluctuation amplitude. Determine the weight of each index through the entropy weight-analytic hierarchy process (AHP) coupled algorithm, and quantitatively calculate the comprehensive score of bridge structural stability. S5. Graded Early Warning and Operation and Maintenance Feedback Control: Based on the comprehensive score, the system is divided into four levels of working conditions: stable, basically stable, understability, and unstable. Differentiated control strategies for bridge operation and maintenance reinforcement, water level scheduling, and monitoring intensification are output for different working conditions to achieve dynamic closed-loop evaluation and proactive prevention and control of railway bridge structural stability under reservoir water storage conditions.

[0026] In step S1, the multi-source environmental parameter acquisition includes: Based on the calendar year as the monitoring cycle, the system is divided into three working conditions: flood season, dry season, and normal water storage. The sampling frequency is set as follows: once every 2 hours during the flood season when water levels fluctuate, once every 12 hours during the dry season when water levels are stable, and once every 24 hours during the normal water storage period. Simultaneously, real-time parameters of the shallow soil (0-5m) and deep soil (5-20m) of the bridge pier foundation are collected using pre-embedded soil moisture sensors, pore water pressure gauges, and seepage monitoring probes. After removing abnormal and abrupt data, the dataset is preprocessed. In step S2, the method for constructing a dynamic deterioration correction model for soil mechanical parameters includes: Establish a formula for calculating the degradation of triple coupling:

[0027] in, Let be the overall soil deterioration coefficient at time t. This represents the long-term immersion degradation coefficient. The deterioration coefficient is the coefficient of dry-wet cycle. The coefficient for deterioration due to seepage particle loss. , , For the corresponding coupling weights, and The weights are obtained through field soil tests; the corrected formula for calculating real-time soil mechanical parameters is as follows:

[0028] In the formula, for Real-time mechanical parameters of the soil. These are the initial mechanical parameters of the soil under natural drying conditions.

[0029] The calculation model for the wet-dry cycle degradation coefficient is as follows:

[0030] In the formula, The soil wet-dry cycle sensitivity coefficient is determined by indoor triaxial tests. for Accumulate the number of wet and dry cycles at all times; This refers to the height of a single rise or fall in water level. This refers to the effective wetting depth of the bridge pier foundation.

[0031] In step S3, the formula for predicting uneven settlement of the foundation is:

[0032] in, for Total foundation settlement at any given time. For the first Real-time applied stress on the soil layer For the first Soil layer thickness, The value represents the real-time compression modulus of the i-th soil layer after correction, and n represents the number of foundation layers.

[0033] In step S4, the specific steps of the entropy weight-analysis hierarchy coupling algorithm include: S41. Construct the original matrix of five-dimensional evaluation indicators and perform dimensionless standardization on all positive and negative indicators; S42. Calculate the subjective weights of each indicator using the analytic hierarchy process, calculate the objective weights of each indicator using the entropy weight method, and then weight and fuse them to obtain the combined weights. S43. The weighted summation method is used to calculate the overall stability score. The calculation formula is as follows:

[0034] in, A comprehensive score for the stability of the bridge structure. For the first The combined weight of the indicators For the first Standardized indicator values, This represents the total number of evaluation indicators.

[0035] In step S4, the criteria for classifying the scoring thresholds for the four-level working conditions are as follows: A comprehensive score F≥0.85 indicates a stable operating condition, 0.70≤F<0.85 indicates a basically stable operating condition, 0.50≤F<0.70 indicates a slightly unstable operating condition, and F<0.50 indicates an unstable operating condition. Different operating conditions correspond to the storage's preset disease risk level and operation and maintenance handling plan.

[0036] In step S5, the closed-loop feedback control logic includes: stable operating condition: maintaining the regular monitoring frequency without special reinforcement; basically stable operating condition: increasing the monitoring frequency of water level and soil parameters, and regularly checking for minor cracks in the pile foundation; unstable operating condition: limiting the train speed, implementing foundation grouting reinforcement, and external protection treatment for the pile foundation; unstable operating condition: immediately implementing traffic restriction control, and simultaneously linking with the reservoir scheduling system to smoothly regulate the water level and eliminate severe disturbances from the dry-wet cycle.

[0037] It also includes a disease tracing and iterative optimization step: regularly summarizing bridge settlement, offset, and cracking disease data, reversely correcting the soil deterioration model weight parameters and evaluation index thresholds, realizing dynamic iterative updates of the evaluation model as the water storage operation years and soil aging degree increase, and improving long-term evaluation accuracy.

[0038] It also includes a visualization output step: integrating real-time monitoring data, mechanical parameter deterioration curves, stability scoring results, operating condition early warning levels, and operation and maintenance control strategies to generate a visualized evaluation report, simultaneously outputting a bridge foundation safety cloud map and a water level-settlement correlation time series curve, achieving an intuitive and visual display of the evaluation results. Example 1

[0039] A railway bridge with pile foundation in a reservoir area of ​​the Lhasa-Nyingchi Railway was selected as the evaluation object. The foundation of the bridge pier is a composite stratum of silty clay and sand. Affected by the water storage and operation of the upstream reservoir, it is under complex working conditions of periodic rise and fall of water level, long-term saturation of soil and continuous seepage of groundwater. During its service, there is a slight risk of uneven settlement. The method of this invention is used to carry out dynamic stability evaluation throughout the year. Specific implementation steps

[0040] Step 1: Monitoring System Deployment and Data Acquisition: Moisture content sensors, pore water pressure gauges, and seepage monitoring probes are deployed in the shallow 0-5m and deep 5-20m strata of the bridge pier foundation, respectively. Automatic water level monitoring instruments are deployed in the reservoir area. Sampling intervals are set at 2 hours during the flood season (June-September), 12 hours during the dry season (December-March), and 24 hours during the normal stable period. Time-series data for 12 consecutive months are collected, and outlier data is removed after preprocessing to construct a complete monitoring dataset.

[0041] Step 2: Soil Deterioration Model Parameter Calibration and Dynamic Correction: Soil wetting sensitivity coefficient, wet-dry cycle sensitivity coefficient, and seepage erosion coefficient were calibrated using indoor triaxial tests, and coupling weights α=0.4, β=0.35, and γ=0.25 were determined. The comprehensive soil deterioration coefficient η(t) was calculated time-by-time, and the real-time soil shear strength, compression modulus, and pile side friction parameters were corrected. By comparing these parameters with those under natural conditions, the amount of mechanical property degradation after water impoundment was accurately obtained.

[0042] Step 3: Foundation bearing capacity and settlement calculation: The foundation is divided into 6 layers using the layered summation method. The real-time corrected soil parameters are substituted into the solution. The total settlement and uneven settlement difference of the foundation are calculated at each time step using the settlement calculation formula. The real-time ultimate bearing capacity and bearing redundancy of the pile foundation are then solved to predict the settlement development trend.

[0043] Step 4: Quantitative evaluation of stability: Five-dimensional evaluation indicators are selected. Subjective weights are determined using the analytic hierarchy process (AHP), and objective weights are calculated using the entropy weight method. These are then combined to obtain a combined weight, and a weighted comprehensive stability score is calculated. During the flood season, when water levels fluctuate drastically, the comprehensive score is 0.62, which is considered an unstable condition. During the dry season, when water levels are stable and the water is stored at a steady level, the comprehensive score is 0.78, which is considered a basically stable condition.

[0044] Step 5, Closed-loop feedback operation and maintenance control: For unstable conditions during the flood season, take measures such as limiting train speed, increasing monitoring frequency to 1 hour / time, and grouting reinforcement of the shallow foundation layer; for basically stable conditions during the dry season, maintain routine monitoring and conduct monthly inspections of pile foundation appearance defects.

[0045] Step 6: Model Iteration and Optimization: At the end of the year, summarize the settlement and stress monitoring data for the whole year, reverse the dry-wet cycle degradation coefficient and evaluation threshold, optimize the model accuracy, and provide more accurate algorithm support for the stability evaluation in the following year.

[0046] In this embodiment, the method of the present invention accurately identifies the risk of bridge instability caused by water level fluctuations during the flood season, and curbs the trend of foundation settlement in advance through operation and maintenance control. Compared with the traditional static evaluation method, it successfully predicts three hidden defects, reduces the evaluation error by 42%, and is fully adapted to the long-term safe operation and maintenance needs of bridges under the water storage conditions of the Lalin Line Reservoir. The practicality and creativity of the technology are significant. Example 2:

[0047] Stability evaluation of bridges under repeated rise and fall conditions in high-altitude, dry years Another simply supported beam railway bridge in the high-altitude reservoir area of ​​the Lhasa-Nyingchi Railway was selected as the evaluation object. The bridge foundation is composed of silty clay mixed with gravel. The regional climate is characterized by long-term low water levels in winter, concentrated high water levels during summer, large annual water level differences, and the superposition of freeze-thaw and wet-dry cycles, which is a typical extreme condition for reservoirs in high-altitude mountainous areas. The bridge has been in service for many years and has been consistently rated as "stable" by traditional static evaluation. However, there are hidden defects such as slight uneven vertical settlement of the piers and minor displacement of the abutments. Traditional methods cannot identify the risk of long-term cumulative deterioration. Therefore, the dynamic evaluation method of this invention is used to conduct a year-round, full-cycle stability assessment. Specific implementation steps

[0048] Step 1: Differentiated monitoring deployment: In response to the characteristics of large annual water level drops in high-altitude and cold regions, data on reservoir water level, soil moisture content (0-20m layer), pore water pressure, and groundwater seepage velocity are collected continuously throughout the year. The sampling interval is 6 hours during the dry season in winter, 2 hours during the fluctuating water level in summer, and 12 hours during the stable season in spring and autumn. A total of 8,760 sets of valid time-series data are collected throughout the year. After outliers are removed using the Grubbs criterion, a dedicated monitoring dataset for high-altitude and cold-weather conditions is constructed.

[0049] Step 2: Adaptive Correction of the Deterioration Model: Combining the characteristics of high-altitude freeze-thaw cycles and wet-dry coupling, the model sensitivity coefficients are recalibrated, and the coupling weights are determined as follows: α=0.42 (long-term immersion), β=0.40 (superposition of freeze-thaw cycles), and γ=0.18 (seepage particle loss). The annual cumulative number of wet-dry cycles and the amplitude of water level fluctuations are calculated time-series, and substituted into the deterioration formula to solve for the comprehensive soil deterioration coefficient. The soil shear strength, compression modulus, and pile side friction parameters are corrected in real time to accurately capture the cyclical attenuation of soil strength caused by dry winter dehydration and summer water saturation.

[0050] Step 3: Dynamic Deduction of Settlement and Bearing Capacity: Substitute the annual time-series dynamic soil parameters into the layered summation method settlement model, calculate the differentiated settlement of the freeze-thaw layer, saturated infiltrated layer, and stable foundation layer, and solve for the pile foundation bearing capacity redundancy under different working conditions throughout the year. The calculation results show that the annual dry-wet cycle superimposed freeze-thaw effect leads to an average annual decrease of 3.2% in the comprehensive mechanical parameters of the soil, and the uneven settlement of the foundation shows a year-by-year cumulative trend. The traditional static parameter calculation results seriously underestimate the risk of disease.

[0051] Step 4: Comprehensive Evaluation of Five-Dimensional Indicators: This step integrates five indicators: settlement rate, bearing capacity redundancy, annual water level variation, soil fissure development, and pile foundation stress fluctuation. A comprehensive score is calculated using the AHP-entropy weight coupling algorithm. The score for the winter dry season is 0.81 (basically stable), and the score for the summer high-water-level infiltration fluctuation period is 0.66 (less stable), accurately identifying structural risks under summer high-water-level conditions.

[0052] Step 5: Closed-loop operation and maintenance control and iteration: In response to the unstable working conditions in summer, control strategies are adopted, such as early and gradual water storage in the reservoir area, avoiding sudden rises and falls in water level, increasing the frequency of settlement monitoring, and implementing seepage prevention and reinforcement for bridge abutment foundations; routine monitoring is maintained in winter. At the end of the year, the threshold for the deterioration coefficient of high-altitude and cold-weather working conditions is reversed based on the cumulative settlement data of the whole year, thereby improving the accuracy of the evaluation in high-altitude and cold-weather regions.

[0053] This embodiment is adapted to the special working conditions of freeze-thaw and wet-dry coupling in reservoirs in high-altitude and cold mountainous areas. It successfully identifies long-term cumulative hidden settlement risks that cannot be detected by traditional evaluation methods, effectively avoids the gradual damage problem of railway bridges in high-altitude and cold reservoir areas after long-term service, and verifies that the model of this invention can adapt to different climates and different water level fluctuation cycles. Its versatility is significantly better than that of traditional static evaluation technology. Example 3

[0054] Refined evaluation of bridges in reservoir areas with high permeability sandy soil strata The railway bridge in the valley section of the Lhasa-Nyingchi Railway reservoir area was selected as the evaluation object. The bridge foundation consists of interlayered silty fine sand and silty clay, with large soil pores and poor cementation. After the reservoir impoundment, the strong groundwater seepage makes it extremely prone to fine particle erosion and loss. This is a typical weak stratum in the reservoir area most susceptible to foundation loosening, bearing capacity reduction, and localized voids. Traditional evaluation only considers soil infiltration and softening, ignoring the deterioration caused by fine particle seepage and loss, resulting in seriously distorted evaluation results. Therefore, the triple-coupled deterioration model of this invention was used to conduct a special evaluation. Specific implementation steps

[0055] Step 1: Targeted monitoring and data collection: In view of the high seepage characteristics of sandy soil strata, deep seepage monitoring equipment is deployed to collect dynamic fluctuation data of seepage velocity and pore water pressure in the 5-20m sand layer of the foundation. The data is matched synchronously with the temporal changes of water level in the reservoir area. The sampling frequency is 1 hour / time during the flood season and 8 hours / time during the non-flood season. The focus is on capturing data during periods of sudden changes in seepage and high incidence of particle loss after the water level rises.

[0056] Step 2: Adaptive Adjustment of Deterioration Model Weights: Considering the characteristic of particle loss dominating the deterioration of sandy soil layers, the coupling weights are recalibrated, setting α=0.30, β=0.25, and γ=0.45 to strengthen the seepage erosion deterioration weights and accurately adapt to the core disease mechanism of sandy soil layers. By monitoring seepage velocity changes in real time and iteratively calculating the seepage deterioration coefficient, the soil porosity, density, and pile side friction parameters are corrected to reconstruct the foundation homogeneity deterioration process caused by long-term particle loss from the sand layer.

[0057] Step 3, Differential Settlement and Bearing Capacity Calculation: For the sand-clay interlayer structure, the number of foundation layers is refined to 8 layers. The settlement due to infiltration and softening of the clay layer and the settlement due to particle loss and loosening of the sand layer are calculated separately. The uneven settlement difference between layers is accurately calculated to predict the risk of local voids and local reduction in bearing capacity.

[0058] Step 4, Stability Classification Evaluation: Based on the five-dimensional evaluation system, the weight of seepage fluctuation and settlement rate indicators is amplified. The comprehensive score range under high seepage conditions is calculated to be 0.58-0.72. During the period of severe seepage at high water levels in the flood season, the stability score is lower than 0.6, which is judged as an unstable condition, with the potential safety hazard of continuous loss of fine particles leading to loosening of the foundation.

[0059] Step 5, Specialized Closed-Loop Prevention and Control: In response to the characteristics of seepage and erosion in sandy soil strata, a specialized operation and maintenance strategy was developed: anti-seepage curtain treatment was implemented around the pile foundation to block seepage channels; the rate of rise and fall of reservoir water level was reduced to weaken the erosion effect of dynamic water; deep settlement and seepage monitoring was intensified, and a risk warning threshold for particle loss was established to achieve source control.

[0060] This embodiment fully verifies the adaptive adaptability of the triple-coupled degradation model of the present invention. It can adjust the degradation weight differently for clay, sand, and composite strata, solve the technical shortcomings of traditional methods that cannot adapt to seepage erosion degradation of sandy strata, accurately predict special diseases such as fine particle loss, loose foundation, and local uneven settlement, and greatly improve the safety evaluation accuracy and operation and maintenance targeting of railway bridges in weak strata in reservoir areas. The working principle of the railway bridge structural stability evaluation method under reservoir impoundment conditions provided by this invention is as follows: To address the differentiated hydrological characteristics of the reservoir during the flood season, dry season, and year-round water storage, a time-segmented differentiated sampling strategy was adopted to solve the problem that a single sampling frequency cannot adapt to drastic water level fluctuations and stable operating conditions. By deploying sensors in deep and shallow layers, data on the water content, pore water pressure, and seepage velocity of the soil at different depths of the bridge pier foundation were accurately acquired. Simultaneously, time-series data on reservoir water levels and initial mechanical parameters of the bridge structure were collected. The Grubbs criterion was used to remove anomalous abrupt changes in the monitoring data, and data normalization preprocessing was completed to construct a time-continuous and dimensionally complete water storage environment monitoring dataset, providing accurate data support for subsequent modeling.

[0061] Based on the three core degradation mechanisms of reservoir impoundment, independent degradation models are decomposed and coupled together: long-term wetting continuously softens the soil and fills pores, causing a decrease in the soil foundation strength; periodic wet-dry cycles induce repeated expansion and contraction of the soil, promoting the development of microcracks and cementation fracture; groundwater seepage continuously carries away fine particles, increasing soil porosity and reducing foundation uniformity. The three degradation coefficients are calculated separately using independent formulas, and combined with the coupling weights calibrated by indoor soil tests, a comprehensive degradation coefficient is synthesized. This allows for real-time correction of core parameters such as soil shear strength, compression modulus, and pile side friction, achieving dynamic and accurate updates of soil mechanical properties based on impoundment time, water level, and seepage characteristics.

[0062] By substituting the corrected real-time soil mechanics parameters into the layered summation method settlement calculation model and the pile foundation bearing capacity formula, the traditional fixed parameter calculation mode is abandoned. The ultimate bearing capacity and effective bearing redundancy of the foundation are deduced on a time-by-time basis, and the dynamic settlement deformation is accurately calculated. Through layered calculation, the performance differences between shallow infiltrated soil and deep stable soil are distinguished, the mechanism of uneven foundation settlement is accurately restored, and the dynamic and accurate prediction of bridge foundation deformation and bearing capacity is achieved.

[0063] Based on the constructed five-dimensional stability evaluation index system, an entropy-weighted analytic hierarchy process (AHP) coupled algorithm is used to integrate subjective and objective weights: the AHP determines subjective weights based on bridge engineering specifications and operation and maintenance experience, ensuring that the evaluation is consistent with the actual project; the entropy-weighted algorithm determines objective weights based on the discrete characteristics of monitoring data, ensuring data objectivity. A comprehensive stability score is calculated through weighted summation, and precise grading is achieved by combining four-level working condition thresholds, comprehensively covering core risk dimensions such as foundation bearing capacity, structural deformation, hydrological disturbance, and soil deterioration, thus avoiding the limitations of single-index evaluation.

[0064] Establish a one-to-one mapping relationship between operating conditions and maintenance strategies to form a precise feedback logic: maintain routine maintenance under stable operating conditions to save maintenance costs; increase monitoring frequency in advance under basically stable operating conditions to detect potential problems early; proactively implement speed limiting and reinforcement measures under unstable operating conditions to curb the development of defects; and coordinate with reservoir scheduling and railway control under unstable operating conditions for emergency evacuation. Simultaneously, through long-term defect data tracing and iterative model parameter iteration, the evaluation system achieves adaptive optimization as the project's service life increases, forming a permanent closed-loop maintenance system.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments, or they can be used directly or indirectly, without departing from the principles and spirit of the invention. In other related technical fields, the scope of the invention is defined by the appended claims and their equivalents, and they are similarly included within the scope of patent protection of the invention.

Claims

1. A method for evaluating the structural stability of railway bridges under reservoir impoundment conditions, characterized in that, This includes the following refined steps: S1. Multi-source environmental parameter time-series acquisition; S2. Modeling for dynamic deterioration correction of soil mechanical parameters; S3. Dynamic simulation of bridge foundation bearing capacity; S4. Multi-index coupled stability classification evaluation; S5, graded early warning and operation and maintenance feedback control.

2. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, In step S1, the multi-source environmental parameter acquisition is used to continuously collect time series data of reservoir water level, time series data of foundation soil moisture content, groundwater seepage field parameters, pore water pressure fluctuation data and initial mechanical parameters of bridge structure within a preset monitoring period for the target railway bridge pier foundation area after reservoir impoundment, and to construct a multi-dimensional original monitoring dataset of the impoundment environment. Specifically, it includes: Based on the natural year as the monitoring cycle, the system is divided into three working conditions: flood season, dry season, and normal water storage. The sampling frequency is set as follows: once every 2 hours during the flood season when the water level fluctuates, once every 12 hours during the dry season when the water storage is stable, and once every 24 hours during the normal year. Simultaneously, real-time parameters of the shallow soil (0-5m) and deep soil (5-20m) of the bridge pier foundation are collected through pre-embedded soil moisture sensors, pore water pressure gauges, and seepage monitoring probes. After removing abnormal and sudden data, the dataset is preprocessed.

3. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, In step S2, the dynamic deterioration correction model of soil mechanical parameters is based on the triple coupling effect of long-term water level rise and wetting, seasonal dry and wet cycle, and fine particle loss from groundwater seepage. The dynamic deterioration correction model of soil shear strength, pile side friction, soil unit weight, and foundation compression modulus is established, and the real-time deterioration coefficient of each mechanical parameter is solved iteratively by combining time-series monitoring data. Specific construction methods include: Establish a formula for calculating the degradation of triple coupling: ； in, Let be the overall soil deterioration coefficient at time t. This represents the long-term immersion degradation coefficient. The deterioration coefficient is the coefficient of dry-wet cycle. The coefficient for deterioration due to seepage particle loss. , , For the corresponding coupling weights, and The weights are obtained through field soil tests; the corrected formula for calculating real-time soil mechanical parameters is as follows: ； In the formula, for Real-time mechanical parameters of the soil. These are the initial mechanical parameters of the soil under natural drying conditions.

4. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 3, characterized in that, The calculation model for the wet-dry cycle degradation coefficient is as follows: ； In the formula, The soil wet-dry cycle sensitivity coefficient is determined by indoor triaxial tests. for Accumulate the number of dry and wet cycles at all times; This refers to the height of a single rise or fall in water level. This refers to the effective wetting depth of the bridge pier foundation.

5. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, In step S3, the corrected real-time soil mechanics parameters are substituted into the calculation model of the bearing capacity of the railway bridge pile foundation to solve the instantaneous ultimate bearing capacity and effective bearing redundancy of the pier foundation under the water storage condition, and the predicted value of uneven settlement of the foundation is calculated simultaneously. The formula for predicting uneven settlement of the foundation is as follows: ； in, for Total foundation settlement at any given time. For the first Real-time applied stress on the soil layer For the first Soil layer thickness, The value represents the real-time compression modulus of the i-th soil layer after correction, and n represents the number of foundation layers.

6. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, In step S4, a five-dimensional stability evaluation index system is constructed, which includes foundation bearing capacity redundancy, settlement deformation rate, pile foundation stress and strain, soil fissure development degree, and water level fluctuation amplitude. The weight of each index is determined by the entropy weight-analytic hierarchy process coupling algorithm, and the comprehensive score of bridge structural stability is quantitatively calculated. The specific steps of the entropy weight-analytic hierarchy process coupling algorithm include: S41. Construct the original matrix of five-dimensional evaluation indicators and perform dimensionless standardization on all positive and negative indicators; S42. Calculate the subjective weights of each indicator using the analytic hierarchy process, calculate the objective weights of each indicator using the entropy weight method, and then weight and fuse them to obtain the combined weights. S43. The weighted summation method is used to calculate the overall stability score. The calculation formula is as follows: ； in, A comprehensive score for the stability of the bridge structure. For the first The combined weight of the indicators For the first Standardized indicator values, This represents the total number of evaluation indicators.

7. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, In step S4, the criteria for classifying the scoring thresholds for the four-level working conditions are as follows: A comprehensive score F≥0.85 indicates a stable operating condition, 0.70≤F<0.85 indicates a basically stable operating condition, 0.50≤F<0.70 indicates a slightly unstable operating condition, and F<0.50 indicates an unstable operating condition. Different operating conditions correspond to the storage's preset disease risk level and operation and maintenance handling plan.

8. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, In step S5, based on the comprehensive score, the working conditions are divided into four levels: stable, basically stable, understability, and unstable. Differentiated control strategies for bridge operation and maintenance reinforcement, water level scheduling, and monitoring intensification are output for different working conditions to realize dynamic closed-loop evaluation and active prevention and control of railway bridge structural stability under reservoir water storage conditions. The closed-loop feedback control logic includes: Stable operating conditions: Maintain regular monitoring frequency; no special reinforcement required. Under basically stable working conditions: increase monitoring of water level and soil parameters, and regularly check for minor cracks in the pile foundation; Unstable operating conditions: Limit train speed, implement foundation grouting reinforcement, and pile foundation external protection treatment; Instability conditions: Immediately implement traffic restrictions and control measures, and simultaneously coordinate with the reservoir dispatching system to gently regulate water levels and eliminate severe disturbances in the dry-wet cycle.

9. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, It also includes a disease tracing and iterative optimization step: regularly summarizing bridge settlement, offset, and cracking disease data, reversely correcting the soil deterioration model weight parameters and evaluation index thresholds, realizing dynamic iterative updates of the evaluation model as the water storage operation years and soil aging degree increase, and improving long-term evaluation accuracy.

10. The method for evaluating the structural stability of railway bridges under reservoir impoundment conditions according to claim 1, characterized in that, It also includes a visualization output step: integrating real-time monitoring data, mechanical parameter deterioration curves, stability scoring results, working condition early warning levels, and operation and maintenance control strategies to generate a visualized evaluation report, and simultaneously outputting bridge foundation safety cloud maps and water level-settlement correlation time series curves to achieve an intuitive and visual display of the evaluation results.

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