Method for judging construction stability of extra-long highway tunnel by considering influence of temperature field

Abstract

The invention relates to the technical field of highway tunnel construction, in particular to an extra-long highway tunnel construction stability judgment method considering temperature field influence, which comprises the following steps: S1, temperature data acquisition and preprocessing: acquiring temperature data of each position in a tunnel in real time; s2, temperature gradient monitoring and local hot spot risk identification: by calculating temperature gradients among all positions in the tunnel, identifying an area with abnormal temperature gradient change, and judging a local hot spot risk area; s3, risk assessment and construction stability judgment: in combination with mechanical characteristics of the tunnel, assessing the influence of thermal stress concentration caused by temperature field change on the tunnel structure, and performing stability judgment; and S4, construction scheduling optimization: automatically adjusting tunnel construction scheduling according to results of risk assessment and construction stability judgment. According to the method, the predictability of the structure instability problem is improved, and the safety and reliability of tunnel construction are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of highway tunnel construction, and in particular to a method for determining the stability of a super-long highway tunnel construction taking into account the influence of a temperature field. Background Art

[0002] With the rapid development of transportation infrastructure construction, extra-long highway tunnels have become an important means to solve traffic problems in complex terrain. However, the construction environment of extra-long tunnels is complex. Especially during the construction process, the changes in the temperature field inside the tunnel will have a significant impact on the mechanical properties and construction stability of the structure. Changes in temperature gradients may cause local thermal stress concentration, resulting in cracks, deformation and even instability in the tunnel structure, seriously affecting construction safety and project quality. Therefore, how to accurately monitor the changes in the temperature field in the tunnel, identify local high-temperature risk areas, and scientifically manage construction scheduling based on real-time data has become an important issue that needs to be urgently solved in the field of tunnel construction.

[0003] At present, the research on the influence of temperature field in tunnel construction is mostly limited to a single monitoring or analysis method, which makes it difficult to achieve accurate monitoring and real-time response to the dynamic changes of complex temperature fields. On the one hand, the existing temperature data acquisition and processing methods lack sufficient accuracy and reliability, and are easily disturbed by environmental noise and data mutations, making it difficult to provide high-quality temperature data for subsequent analysis. On the other hand, the existing risk assessment methods are mostly based on static analysis, lacking dynamic thermal stress concentration analysis of local hot spots, resulting in inaccurate judgment of the overall stability of the tunnel structure. In addition, construction scheduling optimization usually relies on empirical judgment and fails to combine real-time risk assessment results for scientific optimization, resulting in a lack of pertinence in the construction plan, and the existence of construction hazards and resource waste in high-risk areas.

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for determining the construction stability of extra-long highway tunnels taking into account the influence of temperature field, thereby realizing accurate identification and dynamic risk assessment of local hot spot risk areas, and ensuring the scientificity and efficiency of construction scheduling and resource allocation. Summary of the invention

[0005] The invention provides a method for determining the construction stability of an extra-long highway tunnel taking the influence of a temperature field into consideration.

[0006] The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field includes the following steps:

[0007] S1, temperature data collection and preprocessing: by setting up multiple temperature sensors in the tunnel, the temperature data of various locations inside the tunnel are collected in real time, and the collected temperature data are preprocessed, including denoising and smoothing;

[0008] S2, temperature gradient monitoring and local hot spot risk identification: Based on the pre-processed temperature data, by calculating the temperature gradient between various locations in the tunnel, the area with abnormal temperature gradient changes is identified, and the local hot spot risk area is determined, including:

[0009] S21, temperature gradient calculation: based on the preprocessed temperature data, the temperature gradient between the monitoring points in the tunnel is calculated using a gradient calculation algorithm to obtain local temperature gradient information;

[0010] S22, abnormal area identification: based on the obtained local temperature gradient information, identifying the area where the temperature gradient changes abnormally;

[0011] S23, local hot spot risk identification: risk level assessment is performed on the identified abnormal temperature gradient change area, and the local hot spot risk area is determined based on the risk level assessment result;

[0012] S3, risk assessment and construction stability judgment: Based on the identified local hot spot risk areas and combined with the mechanical properties of the tunnel, the impact of thermal stress concentration caused by temperature field changes on the tunnel structure is evaluated, and stability judgment is performed;

[0013] S4, construction scheduling optimization: Automatically adjust tunnel construction scheduling based on the results of risk assessment and construction stability judgment.

[0014] Optionally, the temperature data collection and preprocessing in S1 includes:

[0015] S11, temperature data collection: by setting temperature sensors at multiple monitoring points in the tunnel, the temperature data of each position in the tunnel is collected in real time;

[0016] S12, data preprocessing: preprocessing the collected temperature data, specifically including:

[0017] Data denoising: For the temperature data T(t) at each time point t, the mean filter algorithm is used to denoise the data;

[0018] Data smoothing: Use the exponentially weighted moving average method to smooth the denoised temperature data.

[0019] Optionally, the temperature gradient calculation in S21 includes:

[0020] S211, temperature gradient calculation: based on the pre-processed temperature data, the temperature gradient between the monitoring points in the tunnel is calculated using a gradient calculation algorithm;

[0021] S212, obtaining local temperature gradient information: obtaining local temperature gradient information of each area in the tunnel by calculating the temperature gradient between each monitoring point.

[0022] Optionally, the abnormal area identification in S22 includes:

[0023] S221, temperature gradient difference calculation: performing difference analysis on the temperature gradient data by calculating the difference D(x, y, z) of the gradient change;

[0024] S222, Identification of abnormal temperature gradient change area: Identify the area with abnormal temperature gradient change based on the calculated difference D(x, y, z). When the difference D(x, y, z) is greater than the set temperature threshold T threshold When , the area is marked as an abnormal temperature gradient change area, indicating that there is uneven temperature distribution inside the tunnel or the influence of external heat sources.

[0025] Optionally, the local hotspot risk identification in S23 includes:

[0026] S231, thermal stress calculation: Based on the identification results of the abnormal temperature gradient change area in the tunnel, combined with the thermal expansion coefficient and mechanical properties of the tunnel material, calculate the thermal stress σ caused by the temperature gradient change in this area thermal ;

[0027] S232, Risk level calculation: Based on the calculated thermal stress σ thermal , combined with the temperature resistance and safety threshold of the tunnel structure, calculate the risk level R of the area level ;

[0028] S233, Risk area determination: Based on the calculated risk level R level , the risk level is higher than the risk setting value k high The area is identified as a local hot spot risk area.

[0029] Optionally, the risk assessment and construction stability determination in S3 include:

[0030] S31, thermal stress concentration assessment: Based on the identified local hot spot risk areas, assess the thermal stress concentration areas;

[0031] S32, Structural stability analysis: Based on the results of thermal stress concentration assessment and combined with the mechanical properties of the tunnel, the influence of thermal stress concentration in local hot spots on the stability of the overall tunnel structure is analyzed;

[0032] S33, construction risk identification: Based on the results of structural stability analysis, the construction area is divided into risk levels, including low risk, medium risk and high risk.

[0033] Optionally, the thermal stress concentration assessment in S31 includes:

[0034] S311, obtaining thermal stress distribution: performing thermal stress calculation on the identified local hot spot area to obtain thermal stress distribution;

[0035] S312, Thermal stress concentration assessment: Calculate the thermal stress concentration based on the thermal stress distribution and identify areas with high thermal stress concentration.

[0036] Optionally, the structural stability analysis in S32 includes:

[0037] S321, Structural stability index calculation: Based on the results of thermal stress concentration assessment, calculate the structural stability index S stability ;

[0038] S322, overall structural stability assessment: Combined with the structural stability index and thermal stress concentration, the stability of the overall tunnel structure is comprehensively assessed, and the stability risk coefficient R of the overall structure is calculated. risk .

[0039] Optionally, the construction risk identification in S33 includes:

[0040] S331, low risk: when the risk factor R risk ≤ risk threshold lower limit R low When the construction area is determined to be a low-risk area;

[0041] S332, medium risk: when the lower limit of the risk threshold R low <Risk Factor R risk ≤ Risk threshold upper limit R high When the construction area is determined to be a medium-risk area;

[0042] S333, high risk: when the risk factor R risk >Risk threshold upper limit R high The construction area is judged as a high-risk area.

[0043] Optionally, the construction scheduling optimization in S4 includes:

[0044] S41, risk level matching construction strategy: According to the risk level of the construction area (low risk, medium risk, high risk), the corresponding construction strategy is matched, including:

[0045] Low-risk areas: Construction will proceed as planned, without adjusting the construction pace and process;

[0046] Medium-risk areas: slow down construction progress, reduce mechanical loads, strengthen monitoring, and collect temperature and stress data in real time;

[0047] High-risk areas: suspend construction and formulate supplementary reinforcement plans (such as cooling treatment or adding support structures);

[0048] S42, dynamic construction plan adjustment: Based on real-time temperature data and risk level assessment results, the construction plan is dynamically adjusted, and the scheduling optimization algorithm is used to optimize the construction sequence and resource allocation.

[0049] Beneficial effects of the present invention:

[0050] The present invention significantly improves the accuracy and stability of monitoring temperature changes inside tunnels through real-time collection and preprocessing of temperature data, as well as temperature gradient monitoring and local hot spot risk identification. It effectively eliminates noise and mutation phenomena in temperature data through mean filtering denoising and exponentially weighted moving average smoothing, and provides high-quality input data for subsequent temperature gradient calculations. On this basis, it utilizes temperature gradient difference calculation and abnormal area identification algorithms to achieve accurate identification of abnormal areas of temperature gradient changes, and can effectively filter normal temperature fluctuations to avoid misidentification. At the same time, based on a flexible threshold setting method, it adapts to temperature changes in different construction environments, and provides a scientific basis for local hot spot risk identification.

[0051] The present invention comprehensively evaluates the impact of thermal stress concentration caused by temperature field changes on tunnel structures through thermal stress calculation, thermal stress concentration evaluation and structural stability analysis in local hot spot risk areas. By calculating thermal stress concentration and structural stability indicators, the threat degree of thermal stress to structural stability can be intuitively quantified. At the same time, the stability of local high-risk areas and the overall structure of the tunnel is taken into account. A comprehensive risk level classification method is adopted to quickly identify high-risk areas, which provides a scientific judgment basis for thermal stress control and safety precautions during construction, significantly improves the predictability of structural instability problems, and ensures the safety and reliability of tunnel construction.

[0052] The present invention designs a construction scheduling optimization method based on risk level by combining risk assessment results, realizes accurate matching of construction area risks and resources, and the dynamic construction plan adjustment method optimizes the construction sequence and resource allocation by using the scheduling optimization algorithm, gives priority to low-risk areas, and reasonably postpones or suspends construction in high-risk areas. At the same time, through real-time feedback and dynamic updating of scheduling plans, it ensures that the construction process can flexibly respond to environmental changes, significantly reduces the probability of construction accidents and quality problems, improves construction safety, and also improves construction efficiency and economy, providing strong technical support for the intelligent management of tunnel construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a schematic diagram of a flow chart of a discrimination method according to an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of temperature gradient monitoring and local hot spot risk identification according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0057] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0058] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0059] like Figure 1-Figure 2 As shown in FIG. 1 , the method for determining the construction stability of a super-long highway tunnel considering the influence of the temperature field includes the following steps:

[0060] S1, temperature data collection and preprocessing: by setting up multiple temperature sensors in the tunnel, real-time temperature data of various locations inside the tunnel are collected, and the collected temperature data are preprocessed, including denoising and smoothing, to ensure the accuracy and reliability of the temperature data;

[0061] S2, temperature gradient monitoring and local hot spot risk identification: Based on the pre-processed temperature data, by calculating the temperature gradient between various locations in the tunnel, the area with abnormal temperature gradient changes is identified, and the local hot spot risk area is determined, including:

[0062] S21, temperature gradient calculation: based on the preprocessed temperature data, the temperature gradient between the monitoring points in the tunnel is calculated using a gradient calculation algorithm to obtain local temperature gradient information;

[0063] S22, abnormal area identification: based on the obtained local temperature gradient information, identifying the area where the temperature gradient changes abnormally;

[0064] S23, local hot spot risk identification: risk level assessment is performed on the identified abnormal temperature gradient change area, and the local hot spot risk area is determined based on the risk level assessment result;

[0065] S3, risk assessment and construction stability judgment: Based on the identified local hot spot risk areas and combined with the mechanical properties of the tunnel, the impact of thermal stress concentration caused by temperature field changes on the tunnel structure is evaluated, and stability judgment is performed;

[0066] S4, construction scheduling optimization: automatically adjust tunnel construction scheduling based on the results of risk assessment and construction stability judgment to avoid high-intensity construction in high-risk temperature areas;

[0067] Through the above content, the problem of temperature field influence during the construction of extra-long highway tunnels has been effectively solved. By real-time monitoring of temperature changes inside the tunnel and accurate identification of local hot spots, potential construction risks can be warned in advance, construction scheduling can be optimized, and high-intensity operations in high-temperature risk areas can be avoided, thereby improving construction safety and stability, reducing the risk of structural damage caused by thermal stress, and ensuring the smooth progress of tunnel construction. It has strong intelligent and automated characteristics, can respond to tunnel temperature changes in real time, and provide accurate construction scheduling decisions.

[0068] Temperature data acquisition and preprocessing in S1 include:

[0069] S11, temperature data collection: by setting temperature sensors at multiple monitoring points in the tunnel, the temperature data of each position in the tunnel is collected in real time;

[0070] S12, data preprocessing: preprocessing the collected temperature data, specifically including:

[0071] Data denoising: For the temperature data T(t) at each time point t, the mean filter algorithm is used for data denoising, which is expressed as:

[0072]

[0073] Among them, T filtered (t) is the denoised temperature data, N is the window size, T(i) is the temperature value at time i, and the window sliding range is N moments;

[0074] Data smoothing: Use the exponentially weighted moving average method to smooth the denoised temperature data, expressed as:

[0075] T smooth (t) = αT filtered (t)+(1-α)T smooth (t-1);

[0076] Among them, T smooth (t) is the temperature data after smoothing, α is the smoothing factor, T smooth (t-1) is the smoothed temperature value at the previous time point;

[0077] Through the above content, the accuracy and stability of temperature data are effectively improved. The denoising algorithm eliminates short-term fluctuations and external interference, and the smoothing algorithm reduces data mutations, making the temperature data smoother and more reliable, thereby providing accurate data support for subsequent temperature gradient calculations and local hotspot risk identification.

[0078] The temperature gradient calculation in S21 includes:

[0079] S211, temperature gradient calculation: Based on the pre-processed temperature data, the temperature gradient between the monitoring points in the tunnel is calculated using a gradient calculation algorithm, which is expressed as:

[0080]

[0081] in, is the temperature gradient at position (x, y, z), T(x, y, z) is the temperature value at position (x, y, z) in the tunnel, Δx, Δy, Δz are the position intervals in each direction respectively;

[0082] S212, obtaining local temperature gradient information: obtaining local temperature gradient information of each area in the tunnel by calculating the temperature gradient between each monitoring point;

[0083] Through the above content, the temperature changes between the monitoring points in the tunnel are accurately quantified, which provides reliable data support for identifying local hot spots. It can effectively capture the drastic changes in the temperature gradient in the tunnel, timely discover potential uneven temperature distribution or high-temperature areas, and avoid thermal stress concentration problems caused by local temperature changes. By calculating the temperature gradient, it is possible to identify abnormal temperature areas that may affect the safety of tunnel construction in advance, thereby providing an accurate basis for subsequent risk assessment and stability judgment, and ensuring the safety and stability of the tunnel construction process.

[0084] Abnormal area identification in S22 includes:

[0085] S221, temperature gradient difference calculation: perform difference analysis on the temperature gradient data, calculate the difference of gradient change D(x, y, z), and assume that the temperature gradient at a certain position in the tunnel is The temperature gradient at multiple locations adjacent to this location is The difference is expressed as:

[0086]

[0087] Among them, D(x,y,z) is the difference of gradient change;

[0088] S222, Identification of abnormal temperature gradient change area: Identify the area with abnormal temperature gradient change based on the calculated difference D(x, y, z). When the difference D(x, y, z) is greater than the set temperature threshold T threshold When , the area is marked as an abnormal temperature gradient change area, which means that there is uneven temperature distribution inside the tunnel or the influence of external heat source;

[0089] Temperature threshold T threshold The settings include:

[0090] Temperature gradient fluctuation range analysis: By statistically analyzing the temperature gradient data of multiple monitoring points in the tunnel at different time periods, the fluctuation range of the conventional temperature gradient in the tunnel is calculated, and the temperature gradient fluctuation range of each monitoring point is set to It is expressed as:

[0091]

[0092] in, and are the maximum and minimum values ​​of the temperature gradient of the monitoring point in one cycle respectively;

[0093] Statistical analysis: Statistical analysis is performed on the temperature gradient fluctuation range of multiple monitoring points to obtain the mean and standard deviation of the temperature gradient fluctuation of all monitoring points in the tunnel, expressed as:

[0094]

[0095] Where N is the total number of monitoring points, μ is the mean of the temperature gradient fluctuation range, and σ is the standard deviation of the temperature gradient fluctuation range;

[0096] Threshold setting: According to the statistical analysis results of the temperature gradient fluctuation range, the threshold T of the abnormal temperature gradient change area is set. threshold , expressed as:

[0097] T threshold =μ+k·σ;

[0098] Among them, k is a constant, ranging from 2 to 3;

[0099] Through the above content, accurate identification of areas with abnormal temperature gradient changes can be achieved, which can not only effectively filter out normal temperature fluctuations and avoid misidentification, but also flexibly adapt to temperature changes in different construction environments, improving the accuracy and reliability of identification. In addition, through real-time monitoring and risk identification, potential high-temperature or uneven heat source areas can be discovered in time, providing strong data support for risk assessment, scheduling optimization and safety assurance during tunnel construction, greatly improving construction stability and safety.

[0100] Local hotspot risk identification in S23 includes:

[0101] S231, thermal stress calculation: Based on the identification results of the abnormal temperature gradient change area in the tunnel, combined with the thermal expansion coefficient and mechanical properties of the tunnel material, calculate the thermal stress σ caused by the temperature gradient change in this area thermal , expressed as:

[0102]

[0103] Where E is the elastic modulus of the tunnel material, w1 is the thermal expansion coefficient of the tunnel material, σ thermal is the thermal stress in the area;

[0104] S232, Risk level calculation: Based on the calculated thermal stress σ thermal , combined with the temperature resistance and safety threshold of the tunnel structure, calculate the risk level R of the area level , expressed as:

[0105]

[0106] Among them, σ threshold is the safe thermal stress threshold of the tunnel structure under high temperature conditions;

[0107] Safety thermal stress threshold σ thresholdIt is set according to the mechanical properties, temperature resistance and safety standards of tunnel construction materials, including:

[0108] Material mechanical properties determination: Based on the experimental data of the building materials of the tunnel structure (such as reinforced concrete, prestressed concrete, etc.), determine the thermal expansion coefficient w1 and elastic modulus E of the material;

[0109] Temperature field analysis: Based on the historical temperature data and prediction model of the tunnel construction area, the highest possible temperature T in the area is determined. max and the effect of this temperature on the structure;

[0110] Safety factor setting: Set the safety factor C according to national standards or industry safety specifications. safety ;

[0111] Set threshold: Set the safety thermal stress threshold σ threshold , expressed as:

[0112] σ threshold =w1·E·(T max -T ref )·C safety ;

[0113] Where w1 is the thermal expansion coefficient of the material, E is the elastic modulus of the material, T max is the possible maximum temperature in the tunnel construction area, T ref is the reference temperature, is room temperature (20℃), C safety is the safety factor, set to 1.2 to 2.0;

[0114] S233, Risk area determination: Based on the calculated risk level R level , the risk level is higher than the risk setting value k high The area is determined as a local hot spot risk area;

[0115] Risk setting value k high The setting is expressed as:

[0116]

[0117] Among them, σ threshold is the safety thermal stress threshold, σ typical is the working stress level of the tunnel structure, which is given based on the tunnel's design standards, construction technology and material properties;

[0118] Through the above content, it is possible to accurately identify areas with abnormal temperature gradient changes in the tunnel and give early warning of potential high-temperature areas, thereby providing a scientific basis for thermal stress control during construction. By real-time monitoring of temperature gradient changes and combining risk level assessment, it is possible to effectively predict and prevent structural damage risks caused by abnormal temperature fields. Based on data-driven analysis methods, it is possible to identify hidden high-temperature hot spots and reduce construction accidents and quality problems caused by temperature changes, thereby improving the safety, reliability and efficiency of tunnel construction.

[0119] The risk assessment and construction stability determination in S3 include:

[0120] S31, thermal stress concentration assessment: Based on the identified local hot spot risk areas, assess the thermal stress concentration areas;

[0121] S32, Structural stability analysis: Based on the results of thermal stress concentration assessment and combined with the mechanical properties of the tunnel, the influence of thermal stress concentration in local hot spots on the stability of the overall tunnel structure is analyzed;

[0122] S33, construction risk identification: based on the results of structural stability analysis, the construction area is divided into risk levels, including low risk, medium risk and high risk;

[0123] Through the above content, combined with the thermal stress distribution and the mechanical properties of the tunnel, local hot spots can be accurately identified and their potential threats to the overall stability of the tunnel can be evaluated. Further construction risk identification can be based on the results of the structural stability analysis. The risk level of different areas can be divided, thereby achieving more targeted construction scheduling and safety management, effectively reducing the construction risks caused by temperature field changes, ensuring the safety and stability of the tunnel construction process, and improving the predictability and controllability of the project.

[0124] The thermal stress concentration assessment in S31 includes:

[0125] S311, obtaining thermal stress distribution: performing thermal stress calculation on the identified local hot spot area to obtain thermal stress distribution;

[0126] S312, thermal stress concentration assessment: Based on the thermal stress distribution, calculate the thermal stress concentration and identify areas with high thermal stress concentration, expressed as:

[0127]

[0128] Among them, σ′ is the thermal stress concentration, σ max is the maximum thermal stress value in this area, σ avg is the average thermal stress value of the area;

[0129] Through the above content, the thermal stress distribution in the local hot spot area is analyzed, and the thermal stress concentration is calculated by combining the maximum thermal stress value and the average thermal stress value. It can intuitively reflect the uniformity and concentration risk of thermal stress distribution in the region, and can quickly identify high thermal stress concentration areas, especially in the case of uneven thermal stress distribution, highlighting potential high-risk points.

[0130] Structural stability analysis in S32 includes:

[0131] S321, Structural stability index calculation: Based on the results of thermal stress concentration assessment, calculate the structural stability index S stability , expressed as:

[0132]

[0133] Among them, σ tensile is the tensile strength of the tunnel structure material, σ max is the maximum thermal stress in the local hot spot area;

[0134] S322, overall structural stability assessment: Combined with the structural stability index and thermal stress concentration, the stability of the overall tunnel structure is comprehensively assessed, and the stability risk coefficient R of the overall structure is calculated. risk , expressed as:

[0135]

[0136] Among them, V region is the volume of each region, V total is the overall volume of the tunnel,

[0137] Through the above content, the potential impact of local hot spots on the overall structural stability of the tunnel is comprehensively evaluated, and the threat degree of thermal stress concentration to the tunnel structure can be accurately quantified. It not only pays attention to local high-risk areas, but also takes into account the stability of the overall structure. The complex thermal stress distribution and structural instability problems are concretized, providing a scientific judgment basis for construction safety. It can help engineers identify risk points in advance and optimize construction plans, thereby effectively reducing the possibility of structural instability and improving the safety and reliability of tunnel construction.

[0138] Construction risk identification in S33 includes:

[0139] S331, low risk: when the risk factor R risk ≤ risk threshold lower limit R low When the construction area is determined to be a low-risk area;

[0140] S332, medium risk: when the lower limit of the risk threshold R low <Risk Factor R risk ≤ Risk threshold upper limit Rhigh When the construction area is determined to be a medium-risk area;

[0141] S333, high risk: when the risk factor R risk >Risk threshold upper limit R high When the construction area is determined to be a high-risk area;

[0142] Risk threshold lower limit R low , upper limit of risk threshold R high The settings include:

[0143] Determination of material performance parameters: According to the material characteristics of the tunnel structure, determine its key mechanical parameters, including tensile strength, elastic modulus and thermal expansion coefficient;

[0144] Calculation of thermal stress safety limit: Combined with the actual working environment and material properties of the tunnel, calculate the thermal stress safety limit σ of the tunnel structure safe , which indicates the maximum value of thermal stress that the material can withstand in a long-term stable state, expressed as:

[0145] σ safe =C safety ·σ tensile ;

[0146] Among them, C safety is the safety factor, ranging from 0.7 to 0.9, σ tensile is the tensile strength of the tunnel material;

[0147] Risk threshold upper limit setting: Risk threshold upper limit R high Represents the limit boundary of structural stability risk, that is, when the risk factor exceeds this value, structural instability or damage may occur, expressed as:

[0148]

[0149] Risk threshold lower limit setting: Risk threshold lower limit R low Represents the low risk range allowed during construction, that is, below this value, the structural stability is high and no additional intervention is required, expressed as:

[0150]

[0151] Through the above content, the construction area is divided into low-risk, medium-risk and high-risk areas, which can accurately locate potential high-risk areas and avoid the subjectivity and uncertainty of traditional empirical judgments. At the same time, the risk level classification provides a clear guiding basis for construction scheduling and response measures, which helps to optimize the construction plan, improve construction safety and reliability, and ensure the efficiency and controllability of the tunnel construction process.

[0152] Construction scheduling optimization in S4 includes:

[0153] S41, risk level matching construction strategy: According to the risk level of the construction area (low risk, medium risk, high risk), the corresponding construction strategy is matched, including:

[0154] Low-risk areas: Construction will proceed as planned, without adjusting the construction pace and process;

[0155] Medium-risk areas: slow down the construction progress, reduce mechanical loads, strengthen monitoring, collect temperature and stress data in real time, and ensure construction safety;

[0156] High-risk areas: suspend construction and formulate supplementary reinforcement plans (such as cooling treatment or adding support structures);

[0157] S42, Dynamic construction plan adjustment: Based on real-time temperature data and risk level assessment results, the construction plan is dynamically adjusted, and the scheduling optimization algorithm is used to optimize the construction sequence and resource allocation to ensure that low-risk areas are given priority and construction in medium- and high-risk areas is reasonably postponed. The construction plan optimization objective function is expressed as:

[0158]

[0159] Among them, C i is the cost of the i-th construction area, T i is the estimated construction time of the area, R risk,i is the risk factor of the area, n is the total number of construction areas;

[0160] Through the above content, accurate matching of risks and resources is achieved, and targeted construction strategies can be formulated according to the risk level of the construction area, giving priority to low-risk areas, while delaying or suspending construction in high-risk areas to ensure construction safety.

[0161] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0162] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the construction stability of a long highway tunnel considering the influence of temperature field, characterized in that: The following steps are involved: S1, temperature data collection and preprocessing: by setting up multiple temperature sensors in the tunnel, the temperature data of various locations inside the tunnel are collected in real time, and the collected temperature data are preprocessed, including denoising and smoothing; S2, temperature gradient monitoring and local hot spot risk identification: Based on the pre-processed temperature data, by calculating the temperature gradient between various locations in the tunnel, the area with abnormal temperature gradient changes is identified, and the local hot spot risk area is determined, including: S21, temperature gradient calculation: based on the preprocessed temperature data, the temperature gradient between the monitoring points in the tunnel is calculated using a gradient calculation algorithm to obtain local temperature gradient information; S22, abnormal area identification: based on the obtained local temperature gradient information, identifying the area where the temperature gradient changes abnormally; S23, local hot spot risk identification: risk level assessment is performed on the identified abnormal temperature gradient change area, and the local hot spot risk area is determined based on the result of the risk level assessment; S3, risk assessment and construction stability judgment: Based on the identified local hot spot risk areas and combined with the mechanical properties of the tunnel, the impact of thermal stress concentration caused by temperature field changes on the tunnel structure is evaluated, and stability judgment is performed; S4, construction scheduling optimization: Automatically adjust tunnel construction scheduling based on the results of risk assessment and construction stability judgment.

2. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 1 is characterized in that: The temperature data acquisition and preprocessing in S1 include: S11, temperature data collection: by setting temperature sensors at multiple monitoring points in the tunnel, the temperature data of each position in the tunnel is collected in real time; S12, data preprocessing: preprocessing the collected temperature data, specifically including: Data denoising: For the temperature data T(t) at each time point t, the mean filter algorithm is used to denoise the data; Data smoothing: Use the exponentially weighted moving average method to smooth the denoised temperature data.

3. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 1 is characterized in that: The temperature gradient calculation in S21 includes: S211, temperature gradient calculation: based on the pre-processed temperature data, the temperature gradient between the monitoring points in the tunnel is calculated using a gradient calculation algorithm; S212, obtaining local temperature gradient information: obtaining local temperature gradient information of each area in the tunnel by calculating the temperature gradient between each monitoring point.

4. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 3 is characterized in that: The abnormal area identification in S22 includes: S221, temperature gradient difference calculation: performing difference analysis on the temperature gradient data by calculating the difference D(x, y, z) of the gradient change; S222, Identification of abnormal temperature gradient change area: Identify the area with abnormal temperature gradient change based on the calculated difference D(x, y, z). When the difference D(x, y, z) is greater than the set temperature threshold T threshold When , the area is marked as an abnormal temperature gradient change area, indicating that there is uneven temperature distribution inside the tunnel or the influence of external heat sources.

5. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 4 is characterized in that: The local hotspot risk identification in S23 includes: S231, thermal stress calculation: Based on the identification results of the abnormal temperature gradient change area in the tunnel, combined with the thermal expansion coefficient and mechanical properties of the tunnel material, calculate the thermal stress σ caused by the temperature gradient change in this area thermal ; S232, Risk level calculation: Based on the calculated thermal stress σ thermal , combined with the temperature resistance and safety threshold of the tunnel structure, calculate the risk level R of the area level ; S233, Risk area determination: Based on the calculated risk level R level , the risk level is higher than the risk setting value k high The area is identified as a local hot spot risk area.

6. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 1 is characterized in that: The risk assessment and construction stability determination in S3 include: S31, thermal stress concentration assessment: Based on the identified local hot spot risk areas, assess the thermal stress concentration areas; S32, Structural stability analysis: Based on the results of thermal stress concentration assessment and combined with the mechanical properties of the tunnel, the influence of thermal stress concentration in local hot spots on the stability of the overall tunnel structure is analyzed; S33, construction risk identification: Based on the results of structural stability analysis, the construction area is divided into risk levels, including low risk, medium risk and high risk.

7. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 6 is characterized in that: The thermal stress concentration assessment in S31 includes: S311, obtaining thermal stress distribution: performing thermal stress calculation on the identified local hot spot area to obtain thermal stress distribution; S312, Thermal stress concentration assessment: Calculate the thermal stress concentration based on the thermal stress distribution and identify areas with high thermal stress concentration.

8. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 7 is characterized in that: The structural stability analysis in S32 includes: S321, Structural stability index calculation: Based on the results of thermal stress concentration assessment, calculate the structural stability index S stability ; S322, overall structural stability assessment: Combined with the structural stability index and thermal stress concentration, the stability of the overall tunnel structure is comprehensively assessed, and the stability risk coefficient R of the overall structure is calculated. risk .

9. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 8, characterized in that: The construction risk identification in S33 includes: S331, low risk: when the risk factor R risk ≤ risk threshold lower limit R low When the construction area is determined to be a low-risk area; S332, medium risk: when the lower limit of the risk threshold R low <Risk Factor R risk ≤ Risk threshold upper limit R high When the construction area is determined to be a medium-risk area; S333, high risk: when the risk factor R risk >Risk threshold upper limit R high The construction area is judged as a high-risk area.

10. The method for determining the construction stability of a super-long highway tunnel considering the influence of temperature field according to claim 9, characterized in that: The construction scheduling optimization in S4 includes: S41, risk level matching construction strategy: Match the corresponding construction strategy according to the risk level of the construction area, including: Low-risk areas: Construction will proceed as planned, without adjusting the construction pace and process; Medium-risk areas: slow down construction progress, reduce mechanical loads, strengthen monitoring, and collect temperature and stress data in real time; High-risk areas: suspend construction and formulate supplementary reinforcement plans; S42, dynamic construction plan adjustment: Based on real-time temperature data and risk level assessment results, the construction plan is dynamically adjusted, and the scheduling optimization algorithm is used to optimize the construction sequence and resource allocation.

Images