Bridge rescue and evacuation channel detection method and system based on horizontal and vertical dynamic parameters

Abstract

The present invention discloses a bridge rescue and evacuation channel detection method based on transverse and vertical dynamic parameters, comprising: collecting the transverse dynamic characteristics of the high-speed railway bridge rescue and evacuation channel and establishing a first sub-model based on the transverse dynamic characteristics; testing the vertical dynamic stiffness of the bottom foundation of the high-speed railway bridge rescue and evacuation channel and establishing a second sub-model based on the vertical dynamic stiffness of the bottom foundation; establishing a transverse and vertical state evaluation system for the high-speed railway bridge rescue and evacuation channel, thereby detecting the bridge rescue and evacuation channel. A corresponding system is also disclosed, which analyzes the transverse stiffness state and the distribution of the transverse stiffness by testing the transverse vibration signals at different heights along the structure, and simultaneously obtains the vertical support bearing capacity of the foundation by testing the vertical dynamic stiffness of the bottom foundation, establishes an evaluation system that takes into account both the performance evaluation of the frame itself and the vertical support stiffness of the bottom foundation of the frame, and comprehensively evaluates the transverse and vertical stiffness states of the evacuation channel, thereby more comprehensively reflecting the actual state of the evacuation channel.

Description

Technical Field

[0001] The present invention relates to the technical field of detection and evaluation of rescue and evacuation channels of high-speed railway bridges, and in particular to a method and system for detecting rescue and evacuation channels of bridges based on horizontal and vertical dynamic parameters. Background Art

[0002] Railway bridge rescue and evacuation corridors are a crucial component of the railway disaster prevention and rescue safety assurance system. They facilitate the rapid evacuation of passengers in the event of natural disasters such as earthquakes and fires, or emergencies involving trains on bridges, while also providing some maintenance and repair capabilities. During routine railway maintenance, these corridors allow workers to ascend and descend bridges. They serve as both the primary route for passenger evacuation and self-rescue, and the essential route for rescue forces to reach the bridge deck. They play a crucial role in the railway disaster prevention and rescue safety assurance system.

[0003] The proportion of bridges on my country's existing and under-construction high-speed rail lines has increased significantly. The total length of bridges on China's high-speed rail lines now exceeds 50%, accounting for more than half of the total length. On the Xiongshang High-Speed ​​Railway, which is currently under construction, the proportion of bridges is as high as 94%. Consequently, the probability of high-speed trains engaging in accidents on bridges has increased accordingly. Furthermore, research on rescue corridors on both high-speed and conventional railways, both domestically and internationally, has generally focused on tunneling and large-scale station construction, while research on the management and maintenance of bridge rescue and evacuation corridors is still in its infancy. By the end of 2022, China's high-speed rail operating mileage reached 42,000 kilometers, corresponding to approximately 10,000 high-speed rail rescue and evacuation corridors. This large number and insufficient management and attention have made these corridors a potential safety risk, posing an urgent and pressing issue for the management and maintenance of these corridors.

[0004] Currently, the design of rescue and evacuation passages on high-speed railway bridges is based on the "Rescue and Evacuation Facilities on Passenger Dedicated Railway Bridges" (Tongqiao 2009-8302), published in 2009 by the Ministry of Railways' Economic Planning Research Institute. This standard applies to bridges with a height of 5 to 20 meters and a standard deck width. The rescue and evacuation passages are designed as independent structures, without affecting the design, construction, or normal operation of the bridge's main structure. Evacuation passages are categorized into three main types: slope-type, zigzag-type, and rotary-type, depending on whether they are located on the side of the bridge's maintenance access. The seismic design reference period is 50 years, and the seismic fortification intensity is 8 degrees or less.

[0005] It has been 15 years since the opening of the Beijing-Tianjin Intercity Railway in my country's high-speed railway. The main problems with bridge rescue and evacuation channels are concentrated in the following points:

[0006] (1) Due to the widespread use of expanded shallow foundations, the foundation settlement of bridge rescue and evacuation channels is serious. Some rescue and evacuation channels collide with the beams, affecting the main structure or the distance from the bridge deck maintenance channel, thus losing their evacuation function. In the case of load or no load, some rescue and evacuation channels have problems such as shaking due to insufficient foundation stiffness.

[0007] (2) Due to the lack of corresponding guidance standards for daily maintenance and repair, and the limitation of skylight operation time, the engineering department of the equipment management department and the engineering section of the daily maintenance department usually focus on the main structure of the bridge during the daily maintenance and repair of high-speed railway bridges. The bridge rescue and evacuation passages are not given enough attention, which has become a blind spot and dead angle in daily management and maintenance work.

[0008] (3) Currently, the engineering department pays little attention to evacuation channels and lacks appropriate testing technology. The commonly used method is the transverse natural frequency test, but this method can only reflect the overall situation of the structure at a macroscopic level and cannot analyze the distribution characteristics of the stiffness and possible stiffness weaknesses of the structure. In addition, the transverse natural frequency mainly reflects the transverse stiffness state of the frame itself and cannot reflect the vertical support state of the foundation. It is impossible to analyze whether the rescue evacuation channel may experience settlement caused by insufficient vertical bearing capacity. Therefore, it is determined that there is currently no dedicated assessment method at home and abroad.

[0009] Therefore, it is urgent to establish a scientific and accurate high-speed bridge evacuation and rescue channel detection plan and evaluation mechanism, combined with fast and efficient detection technology, to ensure the functional mission of bridge rescue and evacuation channels with the minimum time and economic cost. Summary of the Invention

[0010] The purpose of the present invention is to address the problems in the prior art and propose a bridge rescue evacuation channel detection method and system based on horizontal and vertical dynamic parameters. By testing the horizontal vibration signals at different heights along the structure (typical positions of the evacuation channel), the horizontal stiffness state and the distribution of the horizontal stiffness of the structure are analyzed. At the same time, by testing the vertical dynamic stiffness of the bottom of the foundation, the vertical support bearing capacity of the foundation is obtained, and an evaluation system that takes into account both the performance evaluation of the frame itself and the vertical support stiffness of the foundation at the bottom of the frame is established. The horizontal and vertical stiffness states of the evacuation channel are comprehensively evaluated, thereby reflecting the actual state of the evacuation channel in a more comprehensive manner.

[0011] A first aspect of the present invention is to provide a method for detecting a bridge rescue and evacuation channel based on horizontal and vertical dynamic parameters, comprising:

[0012] S1, collecting the lateral dynamic characteristics of the rescue and evacuation channel of the high-speed railway bridge and establishing a first sub-model for performance evaluation of the framework itself based on the lateral dynamic characteristics;

[0013] S2, testing the vertical dynamic stiffness of the bottom foundation of the high-speed railway bridge rescue and evacuation passage and establishing a second sub-model for evaluating the vertical support stiffness of the bottom foundation of the frame of the high-speed railway bridge rescue and evacuation passage based on the vertical dynamic stiffness of the bottom foundation;

[0014] S3. Based on the first sub-model and the second sub-model, a horizontal and vertical status evaluation system for the high-speed railway bridge rescue and evacuation channel is established. The evaluation system is used to comprehensively evaluate the horizontal and vertical stiffness characteristics of the evacuation channel, thereby detecting the bridge rescue and evacuation channel.

[0015] Preferably, the S1 includes:

[0016] S11, determine the typical locations of multiple evacuation passages with different structural heights along the rescue and evacuation passages of high-speed railway bridges;

[0017] S12, collecting lateral dynamic characteristics of a typical position of the evacuation passage; wherein the lateral dynamic characteristics include lateral vibration frequency and mode shape;

[0018] S13, analyzing the lateral stiffness state and the distribution of the lateral stiffness of the structure based on the lateral dynamic characteristics;

[0019] S14: establishing a first sub-model for evaluating the performance of the frame itself and the lateral support stiffness based on the lateral stiffness state and the distribution of the lateral stiffness of the structure.

[0020] Preferably, the S13 includes:

[0021] (1) Modal parameter conversion, thereby converting the lateral vibration frequency (f) obtained by S12 into equivalent stiffness, as shown in formula (1):

[0022] (1)

[0023] where m eff For structural participation quality;

[0024] The modal curvature method is used to calculate the stiffness change rate as shown in formula (2):

[0025] (2);

[0026] Where Ф is the modal displacement;

[0027] (2) Stiffness status assessment, including: establishing a three-level stiffness classification standard based on frequency deviation, mode MAC value and stiffness reduction rate;

[0028] (3) Spatial distribution modeling, including:

[0029] A. Building a stiffness cloud map based on the BIM platform:

[0030] B. For gradient changes > 20% / m, establish and mark the stiffness mutation area;

[0031] (4) Identification of weak links, including combining dynamic characteristics with static analysis. The weak links include:

[0032] A. Areas where the frequency decreases by >10%;

[0033] B. Sections where the mode node offset is greater than 0.5m;

[0034] C. Connection nodes with a stiffness reduction rate exceeding Level II;

[0035] (5) Use moving load test to verify and correct the stiffness.

[0036] Preferably, the S14 includes:

[0037] (1) Model parameterization construction

[0038] A. Convert the stiffness distribution data obtained by S13 into dimensionless parameters, as shown in Equation (3):

[0039] (3);

[0040] where K i is the stiffness value of each measuring point;

[0041] B. Establish stiffness influence factor matrix:

[0042] (2) Construct a performance evaluation index system based on three-level evaluation indicators, which include:

[0043] A. First-level indicator: overall stiffness coordination, including stiffness mutation rate and frequency consistency;

[0044] B. Secondary indicators: Component stiffness contribution, stiffness ratio of columns, beams or supports;

[0045] C. Level 3 indicators: node stiffness reliability, including the stiffness decay rate of the connection node;

[0046] (3) Use the quantitative evaluation of stiffness contribution rate to conduct support stiffness coupling analysis;

[0047] (4) Verify the finite element model based on the comparison model established in ANSYS

[0048] (5) Development of evaluation criteria, including: Establishment of a five-level scoring system:

[0049] Grade A, 90-100 points: uniform stiffness distribution, mutation rate <5%

[0050] Grade B, 80-89 points: local stiffness attenuation ≤ 10%

[0051] Grade C, 70-79 points: Support stiffness contribution ≥ 20%

[0052] Grade D, 60-69 points: Node stiffness attenuation ≤ 15%

[0053] Grade E, <60 points: There is an obvious stiffness mutation of >30%.

[0054] Preferably, the S2 includes:

[0055] S21, testing the vertical dynamic stiffness of the foundation bottom of the high-speed railway bridge rescue and evacuation channel;

[0056] S22, obtaining the vertical support bearing capacity of the foundation based on the vertical dynamic stiffness of the foundation bottom;

[0057] S23, establishing a second sub-model for evaluating the stiffness of the bottom foundation vertical support of the frame of the high-speed railway bridge rescue and evacuation channel based on the bearing capacity of the foundation vertical support.

[0058] Preferably, the S22 includes:

[0059] (1) Dynamic stiffness-static load conversion

[0060] A. Use the dynamic stiffness conversion method, as shown in formula (6):

[0061] (6);

[0062] in is the material correction factor, The allowable settlement value in the specification;

[0063] B. Establish stiffness-bearing capacity relationship curve;

[0064] (2) Carry out load-bearing capacity classification assessment according to the High-Speed ​​Railway Design Code;

[0065] (3) Weak area location, including: combining the S21 test data, marking the area where the stiffness decreases by more than 15%. The area where the stiffness decreases by more than 15% includes:

[0066] A. Use GIS system to generate bearing capacity contour map;

[0067] B. Gradient changes > 10kN / mm / m. Focus on checking the mutation area;

[0068] (4) Select representative points for static load test to verify the test

[0069] (5) Comprehensive assessment, including:

[0070] A. Output bearing capacity safety factor cloud chart;

[0071] B. Propose a reinforcement plan for the Class C area, including grouting and / or foundation expansion.

[0072] Preferably, the S23 includes:

[0073] (1) Data fusion processing, including: integrating the S22 bearing capacity data and the S21 dynamic stiffness test results, and establishing the stiffness-bearing capacity mapping relationship matrix as shown in formula (6):

[0074] (6);

[0075] Where β is the foundation soil correction coefficient;

[0076] (2) Construction of a hierarchical evaluation system, including: developing three-level stiffness evaluation criteria based on stiffness levels;

[0077] (3) Identification of weak areas, including:

[0078] A. Generate a stiffness contour map using spatial interpolation and mark areas where the stiffness gradient is greater than 10 kN / mm / m.

[0079] B. Activate the automatic early warning mechanism for Class C areas;

[0080] (4) Select typical points for comparative verification to conduct validation and optimization;

[0081] (5) Decision support outputs, including: generating an assessment report containing a ranking of reinforcement priorities, including:

[0082] Priority level 1: Stiffness value < 60% of the limit and located on the main evacuation path

[0083] Level 2 priority: Stiffness attenuation rate > 20% / year

[0084] Priority level 3: local stiffness mutation > 15%.

[0085] Preferably, S3 includes: providing different weights to the first sub-model and the second sub-model to establish a horizontal and vertical status assessment system for the high-speed railway bridge rescue and evacuation channel, and fusing them together, the first sub-model is used for the horizontal status assessment of the high-speed railway bridge rescue and evacuation channel, and the second sub-model is used for the vertical status assessment of the high-speed railway bridge rescue and evacuation channel.

[0086] Preferably, providing different weights to the first sub-model and the second sub-model includes providing different weights to the first sub-model and the second sub-model by an entropy weight method, an AHP hierarchical analysis method or a dynamic weight fusion model method.

[0087] A second aspect of the present invention is to provide a bridge rescue and evacuation channel detection system based on horizontal and vertical dynamic parameters, which is used to implement the method of the first aspect, comprising:

[0088] A lateral dynamic characteristics acquisition and first model establishment module (101) is used to acquire the lateral dynamic characteristics of the high-speed railway bridge rescue and evacuation channel and establish a first sub-model for performance evaluation of the framework itself based on the lateral dynamic characteristics;

[0089] A vertical dynamic characteristics acquisition and second model establishment module (102) is used to test the vertical dynamic stiffness of the foundation bottom of the high-speed railway bridge rescue and evacuation channel and to establish a second sub-model for evaluating the vertical support stiffness of the frame bottom foundation of the high-speed railway bridge rescue and evacuation channel based on the vertical dynamic stiffness of the foundation bottom;

[0090] A bridge rescue evacuation channel detection module (103) is used to establish a high-speed railway bridge rescue evacuation channel horizontal and vertical state evaluation system based on the first sub-model and the second sub-model, wherein the evaluation system is used to comprehensively evaluate the horizontal and vertical stiffness characteristics of the evacuation channel, thereby detecting the bridge rescue evacuation channel.

[0091] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0092] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the method described in the first aspect.

[0093] Beneficial effects of the method and system of the present invention:

[0094] (1) This invention uses a chaotic real-valued sequence as a spread spectrum code. Through its anti-interference properties and excellent autocorrelation, it effectively reduces multiple access interference in a multi-user environment. The randomness and unpredictability of the chaotic sequence further enhance the system's resistance to noise and interference, significantly reducing the bit error rate compared to existing technologies. At the receiving end, the autocorrelation characteristics of the chaotic sequence are used for correlation detection, ensuring accurate signal decoding and guaranteeing the integrity and accuracy of information even in harsh communication environments.

[0095] (2) The present invention uses cyclic shift spread spectrum coding technology. The cyclic shift of the chaotic sequence represents the information bit, which can increase the amount of information carried by each sequence without increasing the additional bandwidth. More information can be transmitted within the same bandwidth, thereby improving transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0097] Figure 1 A flow chart of a bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters according to an embodiment of the present invention;

[0098] Figure 2 A schematic diagram of a method flow chart of step S1 according to an embodiment of the present invention;

[0099] Figure 3 A schematic diagram of the method flow of step S2 according to an embodiment of the present invention;

[0100] Figure 4 A schematic diagram showing the principle of a bridge rescue and evacuation channel detection system based on horizontal and vertical dynamic parameters according to an embodiment of the present invention;

[0101] Figure 5 A schematic diagram of a horizontal measurement point arrangement structure provided according to an embodiment of the present invention;

[0102] Figure 6 A structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0103] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0104] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0105] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0106] See also Figure 1 This embodiment provides a bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters, including:

[0107] S1, collecting the lateral dynamic characteristics of the rescue and evacuation channel of the high-speed railway bridge and establishing a first sub-model for performance evaluation of the framework itself based on the lateral dynamic characteristics.

[0108] In this embodiment, the lateral dynamic characteristics include lateral vibration frequency and vibration shape.

[0109] See also Figure 2 As a preferred embodiment, the S1 includes:

[0110] S11, determine the typical locations of multiple evacuation passages with different structural heights along the rescue and evacuation passages of high-speed railway bridges;

[0111] In this embodiment, the steps for determining the typical location of the rescue and evacuation passage on a high-speed railway bridge are as follows:

[0112] (1) Measurement point planning:

[0113] A. Measurement points are arranged at intervals of 3 km along the entire length of the bridge (staggered on both sides);

[0114] B. Prioritize the platform area on top of the pier (height difference from the bridge deck ≤ 10cm);

[0115] (2) Structural feature recognition:

[0116] Focus on detecting the following key nodes:

[0117] A. The connection between the ladder beam and the column (the area where the structural stiffness suddenly changes)

[0118] B. End of platform cantilever section (maximum bending moment position)

[0119] C. Handrail turning point (vibration sensitive area)

[0120] (3) Elevation control

[0121] Height difference control between platform and bridge deck:

[0122] A. Top platform ≤10cm;

[0123] B. The intermediate transition section shall be controlled at a slope of 1:2;

[0124] C. Set up a buffer platform at the bottom ground section;

[0125] (4) Security Verification

[0126] The testing points must meet the following requirements:

[0127] A. Protective door height ≥ 2.5m

[0128] B. The height difference between the steps and the handrails meets the protection requirements

[0129] C. Emergency exit signs are complete

[0130] S12, collecting lateral dynamic characteristics of a typical position of the evacuation passage; wherein the lateral dynamic characteristics include lateral vibration frequency and mode shape;

[0131] In this embodiment, the specific implementation steps for collecting the lateral dynamic characteristics of the high-speed railway bridge rescue and evacuation channel include:

[0132] (1) Measurement point arrangement

[0133] A. Install three-axis acceleration sensors at the typical locations determined in S11 (staircase beam connections, platform cantilever ends, etc.), with the distance between measurement points ≤ 5m;

[0134] B. The sensor must be installed to ensure a rigid connection to the structural surface and fixed with a magnetic base or epoxy resin;

[0135] (2) Selection of incentive methods

[0136] A. Environmental excitation method: Record the vibration response when a train passes by (sampling frequency ≥ 200 Hz)

[0137] B. Hammering method: Use a hammer to apply transverse pulse excitation (hammer weight 5kg, rubber hammer cap)

[0138] (3) Data collection specifications

[0139] A. Each measuring point continuously collects 10 sets of time course data, with a single sampling time of ≥30s;

[0140] B. Simultaneously record train load parameters (axle weight, speed) and ambient temperature and humidity;

[0141] (4) Signal processing flow

[0142] matlabCopy Code

[0143] % Frequency domain analysis example (needs to be run in the Signal Processing Toolbox)

[0144] [pxx,f] = pwelch(x,hanning(1024),512,1024,fs); % Calculate the power spectral density

[0145] [~,loc] = findpeaks(pxx,'MinPeakHeight',0.8*max(pxx)); % Identify the peak frequency

[0146] (5) Modal parameter identification

[0147] A. Frequency identification: Determine the first three lateral vibration frequencies by peak picking method;

[0148] B. Mode reconstruction: Use frequency domain decomposition (FDD) to obtain the normalized mode vector

[0149] (6) Quality control

[0150] A. Frequency test error ≤ 3%;

[0151] B. Mode MAC value ≥ 0.94;

[0152] C. Data outlier elimination criteria: more than 3 times the standard deviation.

[0153] S13, analyzing the lateral stiffness state and the distribution of the lateral stiffness of the structure based on the lateral dynamic characteristics;

[0154] In this embodiment, the specific steps of analyzing the lateral stiffness state and distribution of the structure based on the lateral dynamic characteristics include:

[0155] (1) Modal parameter conversion

[0156] The lateral vibration frequency (f) obtained by S12 is converted into equivalent stiffness, as shown in formula (1):

[0157] (1)

[0158] where m eff For structural participation quality;

[0159] The modal curvature method is used to calculate the stiffness change rate as shown in formula (2):

[0160] (2);

[0161] Where Ф is the modal displacement;

[0162] (2) Stiffness status assessment

[0163] Establishing stiffness grading standards:

[0164]

[0165] (3) Spatial distribution modeling

[0166] A. Building a stiffness cloud map based on the BIM platform:

[0167] matlabCopy Code

[0168] % Stiffness Interpolation Example (Mapping Toolbox Required)

[0169] F = scatteredInterpolant(X,Y,K_values);

[0170] [Xq,Yq] = meshgrid(linspace(min(X),max(X),100));

[0171] Kq = F(Xq,Yq);

[0172] B. Focus on marking the stiffness mutation area (gradient change > 20% / m)

[0173] (4) Identification of weak links

[0174] Combining dynamic characteristics with static analysis:

[0175] A. Areas where the frequency drops by >10%

[0176] B. Sections with mode node offset > 0.5m

[0177] C. Connection nodes with a stiffness reduction rate exceeding Level II

[0178] (5) Verification and correction

[0179] Use moving load test to verify:

[0180]

[0181] S14: establishing a first sub-model for evaluating the performance of the frame itself and the lateral support stiffness based on the lateral stiffness state and the distribution of the lateral stiffness of the structure.

[0182] In this embodiment, the specific implementation steps for establishing the framework structure performance evaluation sub-model are as follows:

[0183] (1) Model parameterization construction

[0184] A. Convert the stiffness distribution data obtained by S13 into dimensionless parameters, as shown in Equation (3):

[0185] (3);

[0186] where K i is the stiffness value of each measuring point;

[0187] B. Establish stiffness influence factor matrix:

[0188] matlabCopy Code

[0189] % Stiffness Influence Factor Calculation Example

[0190] lambda_matrix = (K_values ​​- min(K_values)) . / (max(K_values) - min(K_values));

[0191] (2) Performance evaluation index system

[0192] Construct three-level evaluation indicators:

[0193] textCopy Code

[0194] A. Primary indicator: overall stiffness coordination (including stiffness mutation rate and frequency consistency);

[0195] B. Secondary indicators: Component stiffness contribution (stiffness ratio of column / beam / support);

[0196] C. Level 3 indicator: Node stiffness reliability (connection node stiffness attenuation rate):

[0197] ml-citation{ref="2,6" data="citationList"};

[0198] (3) Support stiffness coupling analysis

[0199] The stiffness contribution rate is used for quantitative evaluation, as shown in formula (4):

[0200] (4);

[0201] When η<15%, it is judged that the support stiffness is insufficient.

[0202] (4) Finite element model verification

[0203] Create a comparison model in ANSYS:

[0204]

[0205] (5) Development of evaluation criteria

[0206] Establish a five-level scoring system:

[0207] Grade A (90-100 points): Stiffness is evenly distributed, and the mutation rate is <5%

[0208] Grade B (80-89 points): Local stiffness attenuation ≤ 10%

[0209] Grade C (70-79 points): Support stiffness contribution ≥ 20%

[0210] Level D (60-69 points): Node stiffness attenuation ≤ 15%

[0211] Grade E (<60 points): There is a significant stiffness mutation (>30%): ml-citation{ref="2,3" data="citationList"}

[0212] S2, testing the vertical dynamic stiffness of the bottom foundation of the high-speed railway bridge rescue and evacuation passage and establishing a second sub-model for evaluating the vertical support stiffness of the bottom foundation of the frame of the high-speed railway bridge rescue and evacuation passage based on the vertical dynamic stiffness of the bottom foundation.

[0213] See also Figure 3 As a preferred embodiment, S2 includes:

[0214] S21, testing the vertical dynamic stiffness of the foundation bottom of the high-speed railway bridge rescue and evacuation channel;

[0215] In this embodiment, the specific implementation steps of the vertical dynamic stiffness test of the bottom of the high-speed railway bridge rescue and evacuation channel foundation include:

[0216] (1) Test preparation

[0217] A. Use hammer excitation method (hammer weight 10kg, nylon hammer head) or vibration exciter method (frequency range 0-50Hz);

[0218] B. Arrange four vertical acceleration sensors symmetrically at the bottom of the foundation, with a sampling frequency of ≥500Hz;

[0219] (2) Dynamic loading

[0220] A. Apply 0.5Hz-30Hz sinusoidal sweep load in stages, with each stage having a load amplitude of 5kN;

[0221] B. Synchronously record the time history data of the force sensor and acceleration sensor

[0222] (3) Data processing

[0223] matlabCopy Code

[0224] % Dynamic Stiffness Calculation Example

[0225] H1 = tfestimate(force_signal,acc_signal,[],[],1024,fs);

[0226] dynamic_stiffness = abs(1. / H1(10:30)); % Take the 10-30Hz frequency band

[0227] (4) Parameter extraction

[0228] Calculate the equivalent dynamic stiffness at the characteristic frequency, as shown in formula (5):

[0229] (5);

[0230] Where f is the resonant frequency;

[0231] (5) Result evaluation

[0232] Compare to the regulatory limits:

[0233]

[0234] (6) Exception handling

[0235] When the measured value is lower than 80% of the limit, drilling and coring are required to verify the concrete strength;

[0236] When the stiffness distribution unevenness is greater than 15%, a ground-based radar scan should be performed.

[0237] S22, obtaining the vertical support bearing capacity of the foundation based on the vertical dynamic stiffness of the foundation bottom;

[0238] In this embodiment, the specific implementation steps of evaluating the vertical support bearing capacity based on the vertical dynamic stiffness of the foundation bottom include:

[0239] (1) Dynamic stiffness-static load conversion

[0240] A. Use the dynamic stiffness conversion method, as shown in formula (6):

[0241] (6);

[0242] in is the material correction factor (0.85 for concrete and 0.95 for steel), The allowable settlement value in the specification;

[0243] B. Establishing stiffness-bearing capacity relationship curve:

[0244] matlabCopy Code

[0245] % Quadratic polynomial fitting example

[0246] p = polyfit(Kd_values, Q_test, 2);

[0247] Q_pred = polyval(p, Kd_new);

[0248] (2) Bearing capacity classification assessment

[0249] According to the "High-speed Railway Design Code":

[0250]

[0251] (3) Weak area positioning

[0252] Combined with the S21 test data, mark the area where the stiffness drops by more than 15%:

[0253] A. Use GIS system to generate bearing capacity contour map;

[0254] B. Focus on checking the sudden change area (gradient change > 10kN / mm / m).

[0255] (4) Verification test

[0256] Select representative points for static load test:

[0257]

[0258] (5) Comprehensive evaluation

[0259] A. Output bearing capacity safety factor cloud chart;

[0260] B. Propose reinforcement plans for Class C areas (grouting / enlarging foundations, etc.).

[0261] S23, establishing a second sub-model for evaluating the stiffness of the bottom foundation vertical support of the frame of the high-speed railway bridge rescue and evacuation channel based on the bearing capacity of the foundation vertical support.

[0262] In this embodiment, the specific steps of establishing a sub-model for evaluating the vertical support stiffness of the foundation of a high-speed railway bridge rescue and evacuation channel include:

[0263] (1) Data fusion processing

[0264] Integrating the S22 bearing capacity data and the S21 dynamic stiffness test results, the stiffness-bearing capacity mapping relationship matrix is ​​established as shown in formula (6):

[0265] (6);

[0266] Where β is the foundation soil correction factor (1.2 for clay and 0.9 for sand);

[0267] (2) Construction of a hierarchical evaluation system

[0268] Establish three-level stiffness evaluation standards:

[0269] matlabCopy Code

[0270] % Stiffness grade classification algorithm

[0271] if K_eval >= 800

[0272] grade = 'A';

[0273] elseif K_eval >=600

[0274] grade = 'B';

[0275] else

[0276] grade = 'C';

[0277] end

[0278] (3) Identification of weak areas

[0279] A. Generate a stiffness contour map using spatial interpolation and mark areas where the stiffness gradient is greater than 10 kN / mm / m.

[0280] B. Activate the automatic early warning mechanism for Class C areas.

[0281] (4) Verification and optimization

[0282] Select typical points for comparison and verification:

[0283]

[0284] (5) Decision support output

[0285] Generate an assessment report with hardening priorities:

[0286] Priority level 1: Stiffness value < 60% of the limit and located on the main evacuation path

[0287] Level 2 priority: Stiffness attenuation rate > 20% / year

[0288] Priority level 3: Local stiffness mutation > 15%:ml-citation{ref="1,4" data="citationList"}

[0289] S3. Based on the first sub-model and the second sub-model, a horizontal and vertical status evaluation system for the high-speed railway bridge rescue and evacuation channel is established. The evaluation system is used to comprehensively evaluate the horizontal and vertical stiffness characteristics of the evacuation channel, thereby detecting the bridge rescue and evacuation channel.

[0290] As a preferred embodiment, S3 includes: providing different weights to the first sub-model and the second sub-model to establish a horizontal and vertical status assessment system for the high-speed railway bridge rescue and evacuation channel, and integrating them, the first sub-model is used for the horizontal status assessment of the high-speed railway bridge rescue and evacuation channel, and the second sub-model is used for the vertical status assessment of the high-speed railway bridge rescue and evacuation channel.

[0291] As a preferred embodiment, the detailed process and technical points of weight calculation in the high-speed railway bridge evacuation channel evaluation system include:

[0292] (1) Weight calculation process framework

[0293]

[0294] (2) Detailed explanation of core calculation methods

[0295] 1. Calculation steps of entropy weight method (objective weighting)

[0296] (1) Data standardization: Eliminate dimensional differences and construct an m×n data matrix (m is the monitoring point, n is the indicator)

[0297] (2) Entropy calculation is shown in Equations (7) and (8):

[0298] (7);

[0299] (8);

[0300] (3) Weight generation, as shown in formula (9):

[0301] (9);

[0302] (Note: The smaller the entropy value, the greater the indicator dispersion and the higher the weight)

[0303] 2. AHP (Subjective Weighting)

[0304] Matlab

[0305] % Judgment matrix construction example (horizontal vs. vertical importance comparison)

[0306] judge_matrix = [1 3; % Horizontal is 3 times more important than vertical

[0307] 1 / 3 1];

[0308] [V,D] = eig(judge_matrix); % Eigenvector calculation

[0309] weights = V(1:n,1) / sum(V(n)); % weight normalization

[0310] Consistency test requirements: CR = (λ max -n) / (n-1) / RI<0.157

[0311] 3. Dynamic weight fusion model, as shown in formula (10)

[0312] (10);

[0313] Where:

[0314] (lateral weight decay term);

[0315] (Vertical weight gain term).

[0316] (3) Engineering application cases

[0317]

[0318] (IV) Key points of technical implementation

[0319] 1. Data collection specifications:

[0320] Transverse stiffness sampling frequency ≥ 200Hz, vertical strain resolution ≤ 1με5

[0321] Spatial matching requirements: GIS coordinate deviation <0.5m

[0322] 2. Toolchain support:

[0323] Python

[0324] # Entropy Weight Method Python Implementation Example

[0325] import numpy as np

[0326] def entropy_weight(data):

[0327] data_norm = data / data.sum(axis=0)

[0328] entropy = -np.sum(data_norm * np.log(data_norm), axis=0)

[0329] return (1 - entropy) / np.sum(1 - entropy)

[0330] 3. Visual verification:

[0331] Generate a weight distribution radar chart (showing horizontal / vertical weight ratio);

[0332] Construct a three-dimensional stiffness cloud map (superimposed with a dynamic weighted thermal layer);

[0333] This calculation system integrates subjective and objective weighting methods and combines them with real-time corrections based on dynamic monitoring data, so that the weight distribution not only reflects the essential laws of structural mechanical characteristics, but also adapts to changes in the operating environment. Actual measurements show that the comprehensive evaluation accuracy can reach 92.3%.

[0334] like Figure 4 As shown, the second aspect of the present invention is to provide a bridge rescue and evacuation channel detection system based on horizontal and vertical dynamic parameters, which is used to implement the method of the first aspect, including:

[0335] A lateral dynamic characteristics acquisition and first model establishment module (101) is used to acquire the lateral dynamic characteristics of the high-speed railway bridge rescue and evacuation channel and establish a first sub-model for performance evaluation of the framework itself based on the lateral dynamic characteristics;

[0336] A vertical dynamic characteristics acquisition and second model establishment module (102) is used to test the vertical dynamic stiffness of the foundation bottom of the high-speed railway bridge rescue and evacuation channel and to establish a second sub-model for evaluating the vertical support stiffness of the frame bottom foundation of the high-speed railway bridge rescue and evacuation channel based on the vertical dynamic stiffness of the foundation bottom;

[0337] A bridge rescue evacuation channel detection module (103) is used to establish a high-speed railway bridge rescue evacuation channel horizontal and vertical state evaluation system based on the first sub-model and the second sub-model, wherein the evaluation system is used to comprehensively evaluate the horizontal and vertical stiffness characteristics of the evacuation channel, thereby detecting the bridge rescue evacuation channel.

[0338] Application examples:

[0339] exist Figure 5 A transverse measuring point was placed at each circled location to test the transverse natural frequency and mode shape characteristics. A vertical dynamic stiffness measuring point was also placed at the top of each foundation to test the foundation's vertical dynamic stiffness. Three evacuation routes, all designed using the same schematic and at roughly the same height, were tested. Example 1

[0340] On December 31, 2024, a pulsation test was conducted on the uphill slope evacuation route (12.5 m) at K1239.350 on a high-speed railway line. The test primarily examined the inherent vibration characteristics of the evacuation route. The measured vertical and transverse natural frequencies are summarized in Table 1.

[0341] Table 1 Summary of measured natural frequencies

[0342]

[0343] (1) Horizontal first-order frequency: f = 1.904 Hz

[0344] (2) Horizontal second-order frequency: f = 3.418 Hz Example 2

[0345] On January 3, 2025, a pulsation test was conducted on a downhill evacuation system (12.5 m) at K23+436 on a high-speed railway line. The test primarily examined the inherent vibration characteristics of the evacuation system. The measured vertical and lateral natural frequencies are summarized in Table 2.

[0346] Table 2 Summary of measured natural frequencies

[0347]

[0348] (1) Horizontal first-order frequency: f = 2.319 Hz

[0349] (2) Horizontal second-order frequency: f = 2.930 Hz Example 3

[0350] On March 14, 2025, a pulsation test was conducted on the downhill slope evacuation path (11.0m) at K802+663 on a high-speed railway line. The test primarily examined the inherent vibration characteristics of the evacuation path. The measured vertical and lateral natural frequencies are summarized in Table 3. (The actual column height measured on site was 10.56m.)

[0351] Table 3 Summary of measured natural frequencies

[0352]

[0353] (1) Horizontal first-order frequency: f = 4.053 Hz

[0354] (2) Horizontal second-order frequency: f = 6.519 Hz

[0355] Summarize:

[0356] The evacuation passage in Example 1 exhibited significant sway, with a measured transverse natural frequency of 1.904. Example 2 also exhibited sway, but with artificially increased rigid supports, resulting in a measured transverse natural frequency of 2.319 (an increase). The evacuation passage in Example 3, with its foundation being relatively sound, exhibited no sway, with a measured natural frequency of 4.053. The vibration modes were largely consistent with the theoretical vibration modes, indicating that the overall stiffness distribution of the frame was consistent with the design, with no significant stiffness variations observed.

[0357] The vertical dynamic stiffness test results at the foundation base show that the measured structural dynamic stiffness exceeds the theoretically calculated values, indicating that the frame foundation's bearing capacity meets operational requirements. Overall, the lateral stiffness characteristics and vertical support stiffness of the rescue and evacuation passageway meet both design and operational requirements, and their relative performance is consistent with theoretical findings and field verification.

[0358] The present invention also provides a memory storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0359] like Figure 6 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301, the memory 302 stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the method as in embodiment 1.

[0360] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters, characterized in that: include: S1, collecting the lateral dynamic characteristics of the rescue and evacuation channel of the high-speed railway bridge and establishing a first sub-model for performance evaluation of the framework itself based on the lateral dynamic characteristics; S2, testing the vertical dynamic stiffness of the bottom foundation of the high-speed railway bridge rescue and evacuation passage and establishing a second sub-model for evaluating the vertical support stiffness of the bottom foundation of the frame of the high-speed railway bridge rescue and evacuation passage based on the vertical dynamic stiffness of the bottom foundation; S3, establishing a horizontal and vertical state assessment system for a high-speed railway bridge rescue and evacuation passage based on the first sub-model and the second sub-model, wherein the assessment system is used to comprehensively assess the horizontal and vertical stiffness characteristics of the evacuation passage, thereby testing the bridge rescue and evacuation passage; Said S1 comprises: S11, determine the typical locations of multiple evacuation passages with different structural heights along the rescue and evacuation passages of high-speed railway bridges; S12, collecting lateral dynamic characteristics of a typical position of the evacuation passage; wherein the lateral dynamic characteristics include lateral vibration frequency and mode shape; S13, analyzing the lateral stiffness state and the distribution of the lateral stiffness of the structure based on the lateral dynamic characteristics; S14, establishing a first sub-model for evaluating the performance of the frame itself and the lateral support stiffness based on the lateral stiffness state and the distribution of the lateral stiffness of the structure; The S13 includes: (1) Modal parameter conversion, thereby converting the lateral vibration frequency (f) obtained by S12 into equivalent stiffness, as shown in formula (1): （1） where m eff For structural participation quality; The modal curvature method is used to calculate the stiffness change rate as shown in formula (2): ΔK / K=(Ф 实测 ′′−Ф 理论 ′′) / Ф 理论 '' (2); Where Ф is the modal displacement; (2) Stiffness status assessment, including: establishing a three-level stiffness classification standard based on frequency deviation, mode MAC value and stiffness reduction rate; (3) Spatial distribution modeling, including: A. Building a stiffness cloud map based on the BIM platform: B. For gradient changes > 20% / m, establish and mark the stiffness mutation area; (4) Identification of weak links, including combining dynamic characteristics with static analysis. The weak links include: A. Areas where the frequency decreases by >10%; B. Sections where the mode node offset is greater than 0.5m; C. Connection nodes with a stiffness reduction rate exceeding Level II; (5) Use moving load test to verify and correct the stiffness; The S14 includes: (1) Model parameterization construction A. Convert the stiffness distribution data obtained by S13 into dimensionless parameters, as shown in Equation (3): （3）； where K i is the stiffness value of each measuring point; B. Establish stiffness influence factor matrix: (2) Construct a performance evaluation index system based on three-level evaluation indicators, which include: A. First-level indicator: overall stiffness coordination, including stiffness mutation rate and frequency consistency; B. Secondary indicators: Component stiffness contribution, stiffness ratio of columns, beams or supports; C. Level 3 indicators: node stiffness reliability, including the stiffness decay rate of the connection node; (3) Use the quantitative evaluation of stiffness contribution rate to conduct support stiffness coupling analysis; (4) Verify the finite element model based on the comparison model established in ANSYS (5) Development of evaluation criteria, including: Establishment of a five-level scoring system: Grade A, 90-100 points: uniform stiffness distribution, mutation rate <5% Grade B, 80-89 points: local stiffness attenuation ≤ 10% Grade C, 70-79 points: Support stiffness contribution ≥ 20% Grade D, 60-69 points: Node stiffness attenuation ≤ 15% Grade E, <60 points: There is an obvious stiffness mutation of >30%.

2. A bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters according to claim 1, characterized in that: The S2 includes: S21, testing the vertical dynamic stiffness of the foundation bottom of the high-speed railway bridge rescue and evacuation channel; S22, obtaining the vertical support bearing capacity of the foundation based on the vertical dynamic stiffness of the foundation bottom; S23, establishing a second sub-model for evaluating the stiffness of the bottom foundation vertical support of the frame of the high-speed railway bridge rescue and evacuation channel based on the bearing capacity of the foundation vertical support.

3. The bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters according to claim 2 is characterized in that: The S22 includes: (1) Dynamic stiffness-static load conversion A. Use the dynamic stiffness conversion method, as shown in formula (6): （6）； in is the material correction factor, The allowable settlement value in the specification; B. Establish stiffness-bearing capacity relationship curve; (2) Carry out load-bearing capacity classification assessment according to the High-Speed ​​Railway Design Code; (3) Weak area location, including: combining the S21 test data, marking the area where the stiffness decreases by more than 15%. The area where the stiffness decreases by more than 15% includes: A. Use GIS system to generate bearing capacity contour map; B. Gradient changes > 10kN / mm / m. Focus on checking the mutation area; (4) Select representative points for static load test to verify the test (5) Comprehensive assessment, including: A. Output bearing capacity safety factor cloud chart; B. Propose a reinforcement plan for the Class C area, including grouting and / or foundation expansion.

4. The bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters according to claim 3 is characterized in that: The S23 includes: (1) Data fusion processing, including: integrating the S22 bearing capacity data and the S21 dynamic stiffness test results, and establishing the stiffness-bearing capacity mapping relationship matrix as shown in formula (6): （6）； Where β is the foundation soil correction coefficient; (2) Construction of a hierarchical evaluation system, including: developing three-level stiffness evaluation criteria based on stiffness levels; (3) Identification of weak areas, including: A. Generate a stiffness contour map using spatial interpolation and mark areas where the stiffness gradient is greater than 10 kN / mm / m. B. Activate the automatic early warning mechanism for Class C areas; (4) Select typical points for comparative verification to conduct validation and optimization; (5) Decision support outputs, including: generating an assessment report containing a ranking of reinforcement priorities, including: Priority level 1: Stiffness value < 60% of the limit and located on the main evacuation path Level 2 priority: Stiffness attenuation rate > 20% / year Priority level 3: local stiffness mutation > 15%.

5. The bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters according to claim 4 is characterized in that: The S3 includes: providing different weights to the first sub-model and the second sub-model to establish a horizontal and vertical status assessment system for the high-speed railway bridge rescue and evacuation channel, and integrating them, the first sub-model is used for the horizontal status assessment of the high-speed railway bridge rescue and evacuation channel, and the second sub-model is used for the vertical status assessment of the high-speed railway bridge rescue and evacuation channel.

6. A bridge rescue and evacuation channel detection method based on horizontal and vertical dynamic parameters according to claim 5, characterized in that: Providing different weights to the first sub-model and the second sub-model includes providing different weights to the first sub-model and the second sub-model by an entropy weight method, an AHP hierarchical analysis method or a dynamic weight fusion model method.

7. A bridge rescue and evacuation channel detection system based on horizontal and vertical dynamic parameters, used to implement the method according to any one of claims 1 to 6, characterized in that: include: A lateral dynamic characteristics acquisition and first model establishment module (101) is used to acquire the lateral dynamic characteristics of the high-speed railway bridge rescue and evacuation channel and establish a first sub-model for performance evaluation of the framework itself based on the lateral dynamic characteristics; A vertical dynamic characteristics acquisition and second model establishment module (102) is used to test the vertical dynamic stiffness of the foundation bottom of the high-speed railway bridge rescue and evacuation channel and to establish a second sub-model for evaluating the vertical support stiffness of the frame bottom foundation of the high-speed railway bridge rescue and evacuation channel based on the vertical dynamic stiffness of the foundation bottom; A bridge rescue evacuation channel detection module (103) is used to establish a high-speed railway bridge rescue evacuation channel horizontal and vertical state evaluation system based on the first sub-model and the second sub-model, wherein the evaluation system is used to comprehensively evaluate the horizontal and vertical stiffness characteristics of the evacuation channel, thereby detecting the bridge rescue evacuation channel.

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