BIM-based methods for pollution prediction and optimization in demolition projects

Abstract

This invention discloses a BIM-based method for pollution prediction and optimization in demolition projects, belonging to the field of demolition simulation technology. It integrates building model data and multi-source dynamic monitoring data to construct a complete risk identification and path optimization mechanism. By extracting the three-dimensional coordinates of pipelines, material conductivity, and residue properties, combined with oxygen concentration, temperature and pressure changes, and vibration intensity, the risk level is dynamically calculated and coupled for correction, accurately identifying semi-enclosed high-risk areas. Finally, a demolition path is generated based on the risk parameters, enabling the work to proceed gradually from low-risk to high-risk areas. This solution has advantages such as high spatial accuracy, strong dynamic response capability, comprehensive risk assessment, and strong guidance, significantly improving the safety and pollution control level of demolition operations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of demolition engineering simulation, in particular to a demolition engineering pollution prediction and optimization method based on BIM. BACKGROUND

[0002] With the acceleration of industrialization, the demolition engineering of old chemical facilities, factory buildings and related buildings is increasing. Especially in the chemical industry, flammable, explosive or toxic substances are often left inside the building pipes and equipment. If not handled properly during the demolition process, it is easy to cause fire, explosion or environmental pollution accidents. Therefore, in recent years, the Building Information Modeling (BIM) technology is widely used to express the structure, pipeline layout and material information of the building in a digital form, providing visual and detailed data support for building life cycle management and demolition construction.

[0003] In the actual demolition process, more attention is paid to the analysis of static physical parameters, such as pipe material, known residual species and their static volatilization characteristics, or simple alarm of dust concentration and oxygen concentration through fixed threshold. However, such methods usually have the following shortcomings: they do not fully consider the dynamic environmental changes in the operation process, such as the change of residual activity caused by mechanical vibration, or the aggregation effect of pipes in semi-closed space. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application provides a demolition engineering pollution prediction and optimization method based on BIM.

[0005] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0006] The demolition engineering pollution prediction and optimization method based on BIM comprises the following steps:

[0007] Obtain the BIM model of the building to be demolished, and extract the three-dimensional coordinate data, material conductivity parameters and residual volatility parameters of the building pipes from the BIM model;

[0008] Obtain the oxygen concentration data of the semi-closed area of the building to be demolished, the semi-closed area is composed of an area where the distance between adjacent pipes is less than a preset distance threshold and there are more than three continuous pipes, and the distance between adjacent pipes is obtained based on three-dimensional coordinate data analysis;

[0009] Input the material conductivity parameters, residual volatility parameters and oxygen concentration data into the pre-constructed risk assessment model to calculate the basic risk value;

[0010] Obtain the temperature data and pressure data inside the building pipes, and calculate the residual activity coefficient based on the temperature data and pressure data;

[0011] correcting a basic risk value based on the residue activity coefficient to obtain a dynamically corrected risk value;

[0012] obtain vibration energy spectrum data generated by mechanical operation of the building pipeline, and calculate a vibration intensity index based on the vibration energy spectrum data;

[0013] determine whether the vibration intensity index exceeds a preset vibration threshold value;

[0014] if the determination result is yes, perform coupled calculation on the dynamically corrected risk value based on the vibration intensity index to obtain a coupled corrected risk value, and take the coupled corrected risk value as a risk parameter;

[0015] if the determination result is no, take the dynamically corrected risk value as the risk parameter;

[0016] determine whether the risk parameter exceeds a preset safety threshold value, and if the determination result is yes, sort the building pipeline in ascending order of the vibration intensity index to generate a removal path instruction starting from the pipeline with the lowest vibration intensity index to the pipeline with the highest vibration intensity index, for guiding execution of the removal operation.

[0017] Compared with the prior art, the beneficial effects of the present application are:

[0018] 1. Oxygen concentration data is introduced to reflect combustion-supporting conditions, and temperature and pressure real-time data are used to capture mutations caused by mechanical or chemical reactions in the operation, and vibration energy spectrum can identify structural resonance risks under mechanical operation excitation. By introducing multi-source data, a dynamic risk calculation model is constructed, which can real-time correct the risk according to the disturbance caused by mechanical operation;

[0019] 2. By real-time monitoring of the temperature / pressure change rate inside the pipeline, the residue activity coefficient is introduced to capture potential signs of out-of-control chemical reactions; and when calculating the residue activity coefficient, the potential hazards caused by sudden changes are amplified by an exponential function, so that the sensitivity and response speed of risk assessment can be significantly improved when detecting sudden changes in temperature or pressure;

[0020] 3. The vibration intensity index is introduced and coupled with the dynamically corrected risk value to form a multi-dimensional superimposed risk model. This coupling mechanism takes into account the influence of mechanical vibration on residue volatilization and static electricity accumulation, can avoid underestimating the overall risk level due to single risk factor estimation, and improves the comprehensiveness of risk assessment. BRIEF DESCRIPTION OF DRAWINGS

[0021] The disclosed content of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0022] Figure 1 This is a flowchart of the steps of the present invention;

[0023] Figure 2 This is a data flow diagram of the present invention;

[0024] Figure 3 This is a data flow diagram for calculating risk values ​​based on vibration intensity index coupling according to the present invention. Detailed Implementation

[0025] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0026] In traditional chemical facility demolition projects, BIM technology primarily relies on static parameters for risk prediction, such as the conductivity of pipe materials, the initial volatility characteristics of residues, and oxygen concentration monitoring at fixed thresholds. However, vibration energy spectrum data generated by mechanical operations are not included in the risk calculation system, and the dynamic distribution of oxygen concentration caused by changes in pipe spacing within semi-enclosed areas is not effectively modeled. Furthermore, the real-time impact of internal pipe temperature and pressure fluctuations on residue activity is not quantified, resulting in basic risk values ​​failing to reflect dynamic environmental changes during the operation.

[0027] For example, in a demolition scenario involving a semi-enclosed area with densely packed pipes, the oxygen concentration in areas with adjacent pipes less than 0.5 meters apart can rise continuously to over 23% due to limited ventilation. If the residue experiences temperature fluctuations due to mechanical vibration (e.g., a temperature change exceeding 15°C within a single hour), its volatility parameters will significantly deviate from their initial values. Existing systems calculate risk values ​​based solely on initial volatility parameters, failing to identify the nonlinear effects of temperature change rates and pressure fluctuations on residue activity. Furthermore, the energy in the 1Hz–50Hz frequency band of the vibration energy spectrum is not resolved into a vibration intensity index weighted by the pipe structure resonance coefficients, resulting in the demolition path planning failing to consider the potential excitation effect of vibration energy on the pipe wall potential gradient.

[0028] If the above issues are not addressed, the discrepancy between dynamic environmental parameters and the static model will lead to an underestimation of risk values, potentially triggering abnormal activation of residues during dismantling. If the coupling effect of oxygen concentration and volatile substances within the semi-enclosed area is not suppressed, a locally explosive gas mixture may form. Furthermore, if the cumulative effect of vibration energy on the pipe wall potential is not analyzed, the risk of electrostatic discharge will continue to rise as the dismantling operation progresses, ultimately triggering a chain reaction of safety accidents and significantly increasing environmental pollution control costs and the probability of project interruption.

[0029] To address the aforementioned challenges, this application first considers how to combine dynamic environmental parameters with static models to improve the accuracy of risk prediction. Traditional methods rely on fixed thresholds and initial parameters, failing to capture changes in residue activity caused by temperature, pressure, and vibration during operations. To resolve this, this application explores establishing a dynamic correction mechanism by real-time monitoring of internal pipe temperature and pressure data, combined with vibration energy spectrum analysis. Specifically, the dynamic distribution of oxygen concentration in semi-enclosed areas needs to be modeled based on the distance threshold between adjacent pipes to identify ventilation-restricted areas. Simultaneously, the vibration energy spectrum needs to be analyzed by frequency band and weighted integral to quantify the impact of structural resonance on vibration intensity. By correlating temperature and pressure fluctuation rates with residue volatility parameters and introducing activation energy constants and pressure suppression factors, a residue activity coefficient calculation model can be constructed. Finally, the dynamically corrected risk value is combined with the vibration intensity index to generate demolition path instructions arranged in ascending order of vibration intensity, simultaneously suppressing dynamic activity changes and vibration-induced risks.

[0030] In this regard, such as Figure 1 As shown, this application proposes a BIM-based method for pollution prediction and optimization in demolition projects, including the following steps:

[0031] Obtain a BIM model of the building to be demolished, and extract the 3D coordinate data, material conductivity parameters, and residual volatility parameters of the building's pipes from the BIM model. The 3D coordinate data refers to the positional information of the pipes in 3D space extracted from the BIM model. This can be achieved by parsing the model file and extracting coordinate data using the API interface provided by the BIM software, and is used to determine the pipe layout and spacing between adjacent pipes. The material conductivity parameters are quantitative indicators of the pipe material's conductivity, which can be obtained through a material database or experimental measurements, and are used to assess the risk of static electricity accumulation. The residual volatility parameters are indicators of the volatilization characteristics of residual substances within the pipes at a specific temperature, which can be obtained through gas chromatography analysis or volatilization rate experiments, and are used to predict the amount of volatiles released.

[0032] Oxygen concentration data of a semi-enclosed area of ​​a building to be demolished is obtained. The semi-enclosed area consists of a region where the distance between adjacent pipes is less than a preset distance threshold and there are three or more consecutive pipes. The distance between adjacent pipes is obtained based on three-dimensional coordinate data analysis. The oxygen concentration data refers to the measured value of the oxygen volume ratio in the semi-enclosed area. Specifically, it can be achieved by real-time monitoring using an electrochemical sensor or an infrared spectrometer, and is used to assess combustion or explosion conditions.

[0033] The material conductivity parameters, residue volatility parameters, and oxygen concentration data are input into a pre-constructed risk assessment model to calculate the basic risk value. The risk assessment model refers to an algorithmic model that calculates the basic risk value based on the input parameters. Specifically, it can be implemented by training historical data using a multiple regression model or a machine learning model to quantify pollution or explosion risks.

[0034] The process involves acquiring temperature and pressure data inside building pipelines and calculating the residue activity coefficient based on this data. The temperature and pressure data refer to the real-time monitored temperature and pressure values ​​inside the pipelines, which can be obtained using thermocouple sensors and pressure transmitters to dynamically assess residue activity. The residue activity coefficient is a quantitative parameter reflecting the reactivity of residues under temperature and pressure changes. It can be calculated using an exponential function combined with the activation energy constant and pressure inhibition factor, and is used to correct the baseline risk value.

[0035] The basic risk value is corrected based on the residue activity coefficient to obtain a dynamically corrected risk value, which is then used as a risk parameter. The dynamically corrected risk value refers to the real-time risk parameter adjusted in conjunction with the residue activity coefficient. Specifically, it can be achieved by correcting the basic risk value through weighted or nonlinear functions, and is used to reflect the impact of dynamic environmental changes on risk.

[0036] The process involves acquiring vibration energy spectrum data of building pipelines generated by mechanical operations, and calculating the vibration intensity index based on this data. Vibration energy spectrum data refers to the distribution of pipeline vibration energy caused by mechanical operations across different frequency bands. Specifically, it can be obtained by collecting vibration signals using accelerometers and analyzing the spectrum through Fourier transform, used to assess the impact of vibration on the pipeline structure. The vibration intensity index is a quantitative indicator of pipeline vibration intensity calculated from the comprehensive vibration energy spectrum data. It can be calculated by weighted integral sound pressure values ​​across frequency bands and combined with the structural resonance coefficient, used to guide the optimization of demolition sequence.

[0037] The system determines whether the risk parameter exceeds a preset safety threshold. If the determination result is yes, the building pipelines are sorted in ascending order of vibration intensity index, generating a demolition path instruction from the pipeline with the lowest vibration intensity index to the pipeline with the highest vibration intensity index. This instruction guides the execution of the demolition operation. The demolition path instruction refers to the pipeline demolition sequence instruction generated by arranging the vibration intensity indices in ascending order. Specifically, this can be achieved by sorting the vibration intensity indices in ascending order using a sorting algorithm and generating an operation list, thereby reducing the cascading risks during the demolition process.

[0038] The core innovation of this application lies in constructing a dynamic risk assessment model by integrating BIM model data, dynamic environmental parameters, and mechanical vibration data, and generating an optimized demolition path based on real-time corrected risk parameters. Specifically, by introducing a residual activity coefficient to dynamically correct the basic risk value and combining it with vibration intensity index ranking to determine the demolition sequence, this approach solves the problem of insufficient static parameter analysis in traditional methods, achieving dynamic collaborative optimization of pollution prediction and demolition operations.

[0039] like Figure 2 The diagram shown is a data flow chart of this application; as a preferred embodiment, the solution of this application is specifically implemented as follows:

[0040] First, import the 3D model of the chemical plant to be demolished using BIM software. Extract the spatial coordinates of the pipeline network, the electrical conductivity data of the pipeline materials (such as carbon steel, stainless steel, etc.), and the saturated vapor pressure data of the residues (such as benzene, toluene, etc.) inside the pipelines from the model.

[0041] Next, based on the extracted 3D coordinate data, the distance between adjacent pipes is calculated. A spacing threshold of 0.5 meters is set, and areas where the spacing between three or more consecutive pipe segments is less than 0.5 meters are identified and marked as semi-enclosed areas. Oxygen concentration sensors are installed in these areas to monitor changes in oxygen concentration in real time.

[0042] Then, the above data is input into a pre-trained machine learning model, which learns the relationship between material conductivity, residue volatility, oxygen concentration and risk level based on historical data, and outputs a preliminary basic risk value.

[0043] Furthermore, temperature and pressure sensors are installed inside the pipeline to collect real-time data. Assume that the temperature inside a section of the pipeline rises from 25°C to 40°C and the pressure rises from atmospheric pressure to 1.2 atmospheres within one hour. Based on this data, and combined with the chemical properties of the residue (such as activation energy), the residue activity coefficient is calculated.

[0044] Therefore, the dynamic adjusted risk value is obtained by multiplying the base risk value by the residue activity coefficient. For example, if the base risk value is 0.6 (out of 1) and the residue activity coefficient is 1.5, then the dynamic adjusted risk value is 0.9.

[0045] Simultaneously, a vibration sensor array was deployed in the demolition area to collect vibration energy spectrum data. The energy in the 1Hz–50Hz frequency band was analyzed, and the vibration intensity index was calculated in conjunction with the pipeline structural characteristics.

[0046] Finally, assuming the preset safety threshold is 0.8, and the dynamic correction risk value for a certain area is 0.9, exceeding the threshold, the system will sort the pipes in the area according to the vibration intensity index from low to high, generating a dismantling sequence instruction. For example, if the vibration intensity indices from low to high are pipes A, B, and C, then the dismantling sequence instruction would be: dismantle A first, then B, and finally C.

[0047] Through the above-described scheme, this application achieves precise assessment and control of dynamic risks during the dismantling of chemical facilities. By comprehensively considering static parameters (such as pipe material and residue properties) and dynamic factors (such as temperature and pressure changes and vibration effects), the accuracy of risk prediction is improved. Particularly in semi-enclosed areas, the formation of locally explosive gas mixtures is effectively prevented by real-time monitoring of oxygen concentration and calculation of residue activity coefficients. Simultaneously, the optimization of the dismantling path based on the vibration intensity index reduces the risk of vibration-induced residue activity. This method not only improves the safety of dismantling operations but also enhances operational efficiency by optimizing the dismantling sequence, reducing potential environmental pollution risks.

[0048] This application further proposes the following calculation process for the residual activity coefficient:

[0049] Get Time Window Temperature variation value inside building pipes Pressure change value Residual volatility parameters, and real-time calculation of temperature change rate. With the rate of pressure change ;

[0050] Determine the rate of temperature change With the rate of pressure change Does it exceed the corresponding preset change threshold?

[0051] If the result is yes, continue with the next steps; otherwise, set the residual activity coefficient. This is the default value;

[0052] The residue type is determined based on the residue volatility parameters, and the activation energy constant is matched from a preset parameter library based on the residue type. and stress inhibitors ;

[0053] The initial volatility baseline value is obtained by mapping based on the volatility parameters of the residue. ;

[0054] Based on initial volatility benchmark value As a baseline value, based on the rate of temperature change With the rate of pressure change Combined with the matching activation energy constant and stress inhibitors The residual activity coefficient was calculated using an exponential function. .

[0055] Rate of temperature change With the rate of pressure change Real-time computation via time window The internal data differential operation is implemented, and the preset change threshold is set based on historical accident data statistics. The residue type is determined by comparing volatile parameters with a preset substance database, and the activation energy constant is used. Pressure inhibition factor reflects the sensitivity of different substances to temperature changes. Characterizing the inhibitory effect of pressure on the volatility of a substance. Initial volatility baseline value. By extracting parameters from a standardized volatile parameter table using linear interpolation and employing an exponential function to amplify the potential hazards caused by sudden changes in temperature or pressure, the sensitivity and response speed of risk assessment can be significantly improved when drastic temperature or pressure changes are detected.

[0056] Specifically, when the rate of change in temperature or pressure exceeds a threshold, the activity coefficient calculation process is automatically triggered. Residue type identification is achieved by matching volatility parameters with characteristic curves of substances in the database; for example, a volatility parameter in the 0.5-0.7 range corresponds to toluene-like substances. Activation energy constant. and stress inhibitors Automatically retrieved based on the type of substance, such as the activation energy constant of toluene. The pressure inhibition factor is 120 kJ / mol. It is 0.85. Initial volatility baseline value. Obtained by referring to a table, for example, the initial volatility benchmark value of toluene at 25°C. It is 0.6.

[0057] This calculation method can reflect the impact of sudden temperature and pressure changes on the activity of residues in real time, and can perform differentiated treatment for different material characteristics, thereby improving the accuracy of risk value correction.

[0058] Through the above technical solution, this application can dynamically calculate the residue activity coefficient in real time, accurately reflecting the impact of temperature and pressure changes on residue volatility. This improves the accuracy and real-time nature of risk assessment, providing more reliable data support for subsequent risk correction and demolition path optimization. Furthermore, by introducing the activation energy constant and pressure inhibition factor, the characteristic differences of different types of residues are considered, making the calculation results more consistent with actual conditions.

[0059] This application further proposes the following calculation process for dynamically adjusted risk values:

[0060] Obtain the material conductivity parameters, residual volatility parameters, residual activity coefficient, and basic risk value of building pipes. ;

[0061] Risk sensitivity weights are determined based on material conductivity parameters. ;

[0062] The actual saturated vapor pressure of the residue is obtained based on the volatility parameters of the residue. Actual saturated vapor pressure Activity coefficient with residue Combined, the overall activity coefficient of the residue is calculated. :

[0063] ;in, This represents the reference saturated vapor pressure of the corresponding substance under standard conditions.

[0064] Determine the overall activity coefficient of the residue Is it below the preset activity threshold? ;

[0065] If the judgment result is yes, then the risk value is dynamically adjusted using the ordinary adjustment method. The calculation formula is:

[0066] ;

[0067] If the judgment result is negative, an enhanced correction mode is adopted to dynamically adjust the risk value. The calculation formula is:

[0068] ,in This is the theoretical maximum value of the comprehensive activity coefficient of the residue, set by staff based on historical experience.

[0069] Overall activity coefficient of residue The ratio of actual saturated vapor pressure to a standard reference value reflects the degree of activity change. Risk-sensitive weighting. The activity level is determined based on the material's conductivity; materials with higher conductivity have a higher weight. The preset activity threshold is 0.5. When the overall activity coefficient of the residue... When this value is exceeded, an enhanced correction mode is activated, which prevents overcorrection by doubling the weights and setting a maximum value limit.

[0070] Actual saturated vapor pressure The reference saturated vapor pressure under standard conditions is set to the value at 25°C and 101.325 kPa, obtained through laboratory measurements or physical property databases.

[0071] For example, the actual saturated vapor pressure of a residue 12 kPa, reference saturated vapor pressure The residual activity coefficient is 10 kPa. If the value is 0.8, then the overall activity coefficient of the residue is... The calculation is 0.8 * 12 / 10 = 0.96. When the preset activity threshold is 0.9, this coefficient exceeds the threshold, triggering the enhanced correction mode. If the risk sensitivity weight is 0.3, then the dynamically corrected risk value is:

[0072] ;in The theoretical maximum value of the comprehensive activity coefficient of residues is set at 1.5. This calculation method considers both the influence of actual environmental parameters and controls the reasonable range of risk values ​​through a dual correction mechanism.

[0073] For example, for a section of stainless steel pipe, the material conductivity parameter is 5.8 × 10⁻⁶. 6 S / m, the volatility parameter of the residue is 0.85, and the activity coefficient of the residue is... The base risk value is 1.2. It is 0.6.

[0074] The conductivity parameter 5.8 × 10⁻⁶ was calculated by looking up a table or interpolation. 6 Risk-sensitive weight corresponding to S / m The value is determined to be 0.7.

[0075] Actual saturated vapor pressure of the residue The reference saturated vapor pressure is 15 kPa under standard conditions. If the Pa is 10 kPa, then the overall activity coefficient of the residue is... The calculation is: 1.2 * 15 / 10 = 1.8.

[0076] Assuming the preset activity threshold is 1.5, since 1.8 > 1.5, the result is negative.

[0077] Assuming the maximum theoretical value of the overall activity coefficient of residues If the value is 2.5, the dynamic adjustment risk value is calculated as follows: .

[0078] Through the above technical solution, this application can dynamically adjust the risk value according to the actual activity state of the residue, thereby improving the accuracy of risk assessment. By distinguishing between ordinary adjustment and enhanced adjustment modes, corresponding risk assessment strategies can be adopted for different levels of residue activity, avoiding overly conservative or overly optimistic risk assessments. In addition, introducing a theoretical maximum value as an upper limit prevents excessive amplification of the risk value in extreme cases, ensuring the rationality and reliability of the risk assessment results.

[0079] This application further proposes risk-sensitive weights. The determination process also includes:

[0080] Obtain ambient temperature data for the area where building pipelines are located. With ambient humidity data ;

[0081] Determine whether the ambient temperature is higher than a preset temperature threshold. And whether the ambient humidity is higher than a preset humidity threshold. ;

[0082] If any judgment result is yes, then the temperature correction factor shall be determined according to the following rules. Humidity correction factor :

[0083] like ,but ,in These are preset temperature-sensitive parameters;

[0084] like ,but ,in These are preset humidity-sensitive parameters;

[0085] If the judgment result is all negative, then , All are 1;

[0086] Risk sensitivity weight sequentially with temperature correction factor Humidity correction factor Multiply to obtain the corrected weights. .

[0087] For example: preset temperature threshold Set to 30℃, preset humidity threshold Set to 60%, temperature sensitive parameter Set to 0.02 / ℃, humidity sensitive parameter. Set to 0.015 / %RH.

[0088] When the ambient temperature Temperature correction factor at 35℃ and 70% humidity The calculation is as follows:

[0089] 1 + 0.02 * (35 - 30) = 1.1;

[0090] Humidity correction factor The calculation is: 1 + 0.015 * (70 - 60) = 1.15, adjusted weight. Original risk-sensitive weight The product of 1.1 and 1.15. By introducing a dual correction mechanism of ambient temperature and humidity, the correction weight can dynamically reflect the physical effects of high temperature leading to increased material conductivity or high humidity leading to reduced volatility of residues.

[0091] Through the above technical solution, this application can dynamically adjust the risk sensitivity weights based on real-time environmental temperature and humidity data, improving the accuracy and adaptability of risk assessment. This allows for a more precise reflection of the impact of environmental condition changes on demolition operation risks, providing a more reliable basis for the safety management of demolition projects. Furthermore, by introducing temperature and humidity correction coefficients, the degree of influence of different environmental factors is quantitatively characterized, making the risk assessment results more convincing and operable.

[0092] like Figure 3 The diagram shown is a data flow chart for calculating risk values ​​based on the vibration intensity index coupling; this application further proposes that the calculation of the vibration intensity index also includes:

[0093] Determine whether the vibration intensity index exceeds a preset vibration threshold;

[0094] If the judgment result is yes, then the dynamic correction risk value is coupled and calculated based on the vibration intensity index to obtain the coupled correction risk value, and the coupled correction risk value is used as the risk parameter;

[0095] If the judgment result is negative, the dynamically adjusted risk value is maintained as the risk parameter.

[0096] The preset vibration threshold is determined by laboratory simulation of the structural stability of pipes of different materials under mechanical vibration. The test data includes the critical vibration energy value of pipe fracture.

[0097] Through the above technical solution, this application can dynamically adjust the risk assessment results according to the actual vibration conditions. When the vibration intensity is high, the accuracy of the risk assessment is improved through coupled calculations, avoiding underestimation of the actual risk due to neglecting the impact of vibration. When the vibration intensity is low, the original risk assessment results are maintained, avoiding unnecessary computational overhead. This threshold-based dynamic adjustment mechanism ensures both the accuracy of the risk assessment and the operational efficiency of the solution.

[0098] This application further proposes the following calculation process for the coupling correction risk value:

[0099] Obtain the spatial propagation path length between the building duct and the vibration source, and calculate the sound wave attenuation factor based on the propagation path length. ;

[0100] Obtaining the vibration intensity index of building pipelines and dynamically adjusted risk values Calculate the coupling correction risk value according to the following formula. :

[0101] ,in, This is the preset vibration coupling coefficient.

[0102] The spatial propagation path length is determined by the difference in three-dimensional coordinates between the pipe and the vibration source in the BIM model, and the acoustic attenuation factor. The preset vibration coupling coefficient is obtained by calculating the vibration propagation path length and the medium absorption coefficient. The values ​​are obtained from a preset parameter library based on the pipe material density and elastic modulus, with a range of 0.1-0.3.

[0103] Vibration intensity index The dynamically corrected risk value, obtained through frequency-band weighted integration of vibration energy spectrum data, integrates the combined effects of material conductivity, residue activity, and environmental parameters. Acoustic attenuation factor. Further considering the attenuation characteristics of vibration energy in the pipe structure, the vibration coupling coefficient Used to quantify the nonlinear correlation between vibration and risk value.

[0104] For example, the vibration intensity index of a section of steel pipeline The sound wave attenuation factor is 1.5 kPa·s, the propagation path length is 8 meters, and the sound wave attenuation factor is... The calculated value is 0.85, the vibration coupling coefficient. If the matching value is 0.2, the risk value of the coupling correction increases by a factor of 1.5 × 0.85 × 0.2 = 0.255. This calculation process quantifies the energy transfer effect caused by mechanical vibration into a risk parameter correction factor, enabling demolition path planning to prioritize vibration-sensitive areas.

[0105] By introducing the coupling calculation of spatial propagation parameters and vibration parameters, the quantitative accuracy of the impact of risk parameters on the dynamics of mechanical vibration is effectively improved, providing more accurate data support for demolition path optimization.

[0106] Through the above technical solution, this application can incorporate the impact of vibration on risk, achieving further optimization of risk assessment. By considering the attenuation effect of sound waves during propagation, the risk assessment results are more accurate. Simultaneously, by introducing a vibration coupling coefficient, personalized risk assessments can be conducted for different types of pipelines, improving the relevance and applicability of the assessment. This coupling correction method effectively enhances the comprehensiveness and accuracy of risk assessment, providing a more reliable decision-making basis for the safety management of demolition projects.

[0107] This application further proposes a method for calculating the vibration intensity index based on vibration energy spectrum data, including:

[0108] Vibration energy spectrum data is acquired, and frequency band analysis is performed on the vibration energy spectrum data to extract the sound pressure value of each frequency band within the frequency range of 1Hz to 50Hz. The frequency band analysis uses a bandpass filter to divide the vibration energy spectrum into multiple sub-frequency bands, each sub-frequency band covering a specific frequency range.

[0109] Based on the BIM model of the building pipeline, the pipeline structure resonance coefficient is determined from the preset parameter library. The pipeline structure resonance coefficient is calculated by the pipeline material parameters, geometric dimensions and support conditions in the BIM model, and the specific value is stored in the preset parameter library.

[0110] The sound pressure value is multiplied by the resonance coefficient of the pipe structure at the corresponding frequency to obtain the weighted vibration energy of each frequency band.

[0111] The vibration intensity index is calculated by integrating the weighted vibration energy over a range of 1Hz to 50Hz. During the calculation of the weighted vibration energy, the structural resonance coefficient is greater than 1 for frequency bands close to the pipe's natural frequency, amplifying the sound pressure level in that band. The integration calculation uses the trapezoidal rule to accumulate the weighted vibration energy across discrete frequency bands, ultimately outputting a dimensionless vibration intensity index.

[0112] Vibration energy spectrum data was decomposed into energy distributions of different frequency components after Fast Fourier Transform. Frequency band analysis focused on the 1Hz–50Hz range, covering the main vibration frequencies generated by mechanical operations and the natural frequency range of common pipelines. Pipe wall thickness, diameter, and support spacing parameters were extracted using the BIM model, and combined with the finite element modal analysis results to determine the structural resonance coefficients corresponding to each frequency point.

[0113] For example, if the natural frequency of a section of steel pipe is 28Hz, the structural resonance coefficient for the 28Hz±2Hz frequency band is set to 1.8, while the coefficients for the other frequency bands are 1.0. In the weighted vibration energy calculation, the sound pressure level of the 28Hz frequency band is multiplied by 1.8, significantly enhancing its contribution to the vibration intensity index. The integration process sums the weighted energy values ​​of each frequency band to form a single index reflecting the overall vibration intensity. This index is positively correlated with the probability of pipe structure damage, providing a quantitative basis for demolition path planning.

[0114] Through the above technical solution, this application can accurately assess the vibration impact of mechanical operations on building pipelines. By using frequency band analysis and introducing the pipeline structure resonance coefficient, the differentiated impact of vibrations at different frequencies on the pipeline is considered, improving the calculation accuracy of the vibration intensity index. Furthermore, this method can identify vibration frequency bands that have a significant impact on the pipeline structure, providing a basis for developing targeted vibration reduction measures and effectively reducing safety risks caused by vibration during demolition.

[0115] This application further proposes the following process for calculating the basic risk value using a risk assessment model:

[0116] Obtain dust concentration data inside building ducts ;

[0117] The residue type is determined based on the residue volatility parameters, and the minimum ignition energy corresponding to the residue type is matched from a preset parameter library. ;

[0118] Electrostatic coefficient determined based on material conductivity parameters ;

[0119] Based on the oxygen concentration data, match the oxygen concentration correction factor. Among them, the oxygen concentration correction factor when the oxygen concentration is below the lower explosive limit Approaching zero;

[0120] Based on the above parameters, calculate the basic risk value. The calculation formula is:

[0121] .

[0122] Dust concentration data The minimum ignition energy is determined by real-time monitoring of suspended particulate matter content inside the pipeline using sensors. The electrostatic coefficient is obtained by matching the minimum ignition energy values ​​of different substances in a preset parameter library. The normalized conductivity of the pipe material is mapped to a numerical range of 0.1-1.5, with an oxygen concentration correction factor. The ratio of oxygen concentration to the lower explosive limit is set as a continuous variable of 0-1.

[0123] Base risk value Dust concentration data in the calculation formula The exponential term is used to enhance the nonlinear effect of high-concentration dust on the risk value, and the minimum ignition energy is introduced into the denominator. To reflect the differences in flammability of different substances, electrostatic coefficient With oxygen concentration correction factor These respectively reflect the risk of static electricity accumulation and the combustion-supporting effect of oxygen.

[0124] This calculation method couples the degree of dust accumulation, the flammability of the material, the static electricity risk, and the oxygen conditions to quantitatively assess the explosion risk level during pipeline dismantling.

[0125] Through the above technical solution, this application constructs a multi-parameter coupled calculation model by integrating dust concentration, minimum ignition energy, electrostatic coefficient, and oxygen concentration correction factor, thus solving the problem of insufficient consideration of the coupling effect of dynamic environmental parameters and material properties in traditional methods. This method can accurately quantify the combustion and explosion risk level of pipeline residues under specific environmental conditions, effectively identify high-risk areas, and thus provide data support for optimizing the dismantling operation sequence, avoiding chain explosion accidents caused by the accumulation of local risks.

[0126] This application further proposes that when the risk parameter exceeds a preset safety threshold, the following also applies:

[0127] Obtain the wall potential gradient data of building pipelines;

[0128] The pipe wall potential gradient data is mapped to the surface of the corresponding pipe segment in the BIM model to generate a potential distribution surface.

[0129] Extract grid cells with gradient values ​​exceeding 3kV / m from the potential distribution surface;

[0130] For continuous areas exceeding 0.1m 2 The unit groups are merged into independent coating regions;

[0131] The independent coating area is designated as the antistatic coating area and marked in the BIM model. The coating thickness of the antistatic coating area is linearly proportional to the gradient value of the corresponding position in the potential distribution surface.

[0132] When risk parameters during demolition operations exceed preset safety thresholds, potential gradient data of the pipe wall surface is collected using a potential sensor array. This potential gradient data is projected in real-time onto the corresponding pipe segment surface in the BIM model using a 3D coordinate mapping algorithm, generating a continuous potential distribution surface. The potential distribution surface is discretized using mesh generation technology, extracting mesh cells with potential gradient values ​​exceeding 3kV / m. Cluster analysis is performed on spatially adjacent mesh cells with continuously exceeding gradient values, identifying those with a continuous coverage area exceeding 0.1m². 2 The unit groups are merged into independent coating areas. Finally, the independent coating areas are visualized in the BIM model using a polygon annotation algorithm. At the same time, based on the gradient values ​​at each location in the potential distribution surface, the antistatic coating construction parameters are automatically generated according to a linear relationship of 0.5 mm thickness for every 1 kV / m gradient.

[0133] Through the above technical solution, this application can accurately identify high-risk areas of static electricity accumulation on the surface of building pipes, and automatically label dangerous areas through dynamic potential gradient mapping and spatial clustering analysis. Based on the linear correlation mechanism between gradient value and coating thickness, the protective strength of the antistatic coating can be adaptively adjusted, effectively reducing the probability of flammable residues igniting due to electrostatic discharge during demolition operations, thereby improving engineering safety.

[0134] This application further proposes that when the risk parameter exceeds a preset safety threshold, the following also applies:

[0135] Identify the coordinates of flange connection points or welds between pipelines based on BIM models;

[0136] Obtain the vibration intensity index at the corresponding location of the flange connection point or weld;

[0137] Select the damper stiffness level according to the vibration intensity index. Install hydraulic dampers at locations where the vibration intensity index exceeds the preset stiffness threshold, and install rubber dampers at other locations.

[0138] The coordinates of flange connection points or welds are automatically identified using preset component attribute labels in the BIM model, and the coordinate data is located using a three-dimensional spatial coordinate system. The comparison between the vibration intensity index and the preset stiffness threshold uses linear interpolation, and the preset stiffness threshold is set based on the yield strength of the pipe material and the frequency domain distribution characteristics of the vibration energy spectrum. The stiffness level of hydraulic shock absorbers is adjusted using a combination of piston diameter and oil viscosity parameters, while the stiffness level of rubber shock absorbers is graded based on the hardness value of the vulcanized rubber.

[0139] Specifically, flange connection points or weld coordinates are marked as high-stress concentration areas in the BIM model, and their coordinate data is extracted through geometric topology analysis. Vibration intensity indices are collected in real-time by accelerometers installed on the mechanical equipment and mapped to corresponding coordinate locations. When the vibration intensity index exceeds a preset stiffness threshold, hydraulic shock absorbers are deployed at that coordinate point, and their damping characteristics dissipate vibration energy by adjusting the oil flow rate. At locations where the threshold is not exceeded, rubber shock absorbers are used, absorbing low-frequency vibration energy through elastic deformation. This solution effectively suppresses the propagation of vibration energy along the pipeline structure by matching the type of shock absorber with the local vibration intensity, reducing secondary risks caused by mechanical operations.

[0140] As a preferred embodiment, the specific implementation of this application is as follows: During the risk assessment of the demolition operation, when the risk parameters exceed the preset safety threshold, the geometric coordinate positions of all flange bolt connection nodes and welds in the pipeline system are automatically identified through the three-dimensional coordinate analysis function of the BIM model. The vibration intensity index of each connection node is collected in real time using a vibration sensor array. For flange connection points, a six-axis accelerometer is used to measure the three-dimensional vibration energy, and for weld locations, a laser Doppler vibration meter is used to obtain the surface vibration velocity spectrum. Based on the preset stiffness threshold classification standard, vibration intensity indices exceeding 3000 m / s² are... 2 A dual-chamber hydraulic shock absorber is installed at the node coordinate position, with a piston rod diameter of 28mm and a built-in pressure compensation valve; for node positions where the vibration intensity index is lower than the threshold, a nitrile rubber shock absorber block with a Shore hardness of 65±5 is installed, and the rubber block is fixed to the outer wall of the pipe with a high-strength adhesive.

[0141] Through the above technical solution, this application effectively solves the problem of dynamic impact of mechanical vibration on pipeline connection structures during demolition operations. By precisely matching vibration intensity data with the selection of vibration damping devices, it avoids the localized stress concentration phenomenon caused by traditional single vibration damping schemes. Specifically, the installation of hydraulic vibration dampers in high-intensity vibration areas can significantly reduce the risk of structural resonance, while the use of rubber vibration damping blocks in low-vibration areas ensures both vibration damping effect and reduces equipment costs. The resulting graded vibration damping system significantly improves the structural stability of the pipeline system during demolition.

[0142] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A BIM-based deconstruction project pollution prediction and optimization method, characterized in that: The method comprises the following steps: obtaining a BIM model of a building to be demolished, and extracting three-dimensional coordinate data, material conductivity parameters and residue volatility parameters of the building pipeline from the BIM model; obtaining oxygen concentration data of a semi-closed area of the building to be demolished, the semi-closed area being composed of an area in which the spacing between adjacent pipelines is less than a preset spacing threshold and in which more than three pipelines continuously exist, the spacing between adjacent pipelines being obtained based on three-dimensional coordinate data analysis; inputting the material conductivity parameters, residue volatility parameters and oxygen concentration data into a pre-constructed risk assessment model to calculate a basic risk value; obtaining temperature data and pressure data inside the building pipeline, and calculating a residue activity coefficient based on the temperature data and the pressure data; based on the residue activity coefficient, correcting the basic risk value to obtain a dynamically corrected risk value, and taking the dynamically corrected risk value as a risk parameter; obtaining vibration energy spectrum data generated by mechanical work on the building pipeline, and calculating a vibration intensity index based on the vibration energy spectrum data; determining whether the risk parameter exceeds a preset safety threshold, and if the determination result is yes, sorting the building pipeline in ascending order of the vibration intensity index, and generating a demolition path instruction starting from the pipeline with the lowest vibration intensity index to the pipeline with the highest vibration intensity index, to guide the execution of the demolition work; the calculation process of the dynamically corrected risk value is as follows: Obtaining material conductivity parameters, residue volatility parameters, and residue activity coefficients of building ducts , base risk values ; Determining a risk sensitivity weight according to the material conductivity parameter ; The actual saturation vapor pressure of the residue is calculated in combination with the residue activity coefficient , the actual saturation vapor pressure and the residue activity coefficient to calculate the overall activity coefficient of the residue : ; wherein is the reference saturated vapor pressure of the corresponding substance at standard conditions; determining whether the residual integrated activity coefficient is below a predetermined activity threshold ; If the result of the judgment is yes, the ordinary correction method is adopted to dynamically correct the risk value The calculation formula is: ； If the result of the judgment is no, the enhanced correction mode is adopted to dynamically correct the risk value The calculation formula is: wherein is the maximum value of the theoretical integrated residual activity coefficient, set by the operator on the basis of historical experience.

2. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: the calculation process of the residue activity coefficient is as follows: obtaining temperature change values, pressure change values and residue volatility parameters inside the building pipeline within a time window, and calculating temperature change rates and pressure change rates in real time; determining whether the temperature change rates and the pressure change rates exceed corresponding preset change thresholds, and if the determination result is yes, continuing to execute the subsequent steps; if the determination result is no, setting the residue activity coefficient as a default value; determining a residue type according to the residue volatility parameters, and matching activation energy constants and pressure inhibition factors based on the residue type from a preset parameter library; obtaining an initial volatility reference value through mapping according to the residue volatility parameters; taking the initial volatility reference value as a base value, combining the matched activation energy constants and pressure inhibition factors according to the temperature change rates and the pressure change rates, and calculating the residue activity coefficient by using an exponential function.

3. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: the risk sensitive weight The determination process further comprises: Acquiring ambient temperature data of an area in which a building pipe is located with ambient humidity data ; determining whether the ambient temperature is higher than a preset temperature threshold and whether the ambient humidity is higher than a preset humidity threshold ; If any of the results of the determinations is yes, then the temperature correction factor and the humidity correction factor are determined according to the following rules, respectively : If the result of the determination of the temperature is yes, then the temperature correction factor is determined according to the following rule: : If the result of the determination of the humidity is yes If then wherein is a preset temperature sensitive parameter; If then where is a preset humidity sensitive parameter; If the judgment result is all no, then , are all 1. Risk sensitivity weight sequentially with temperature correction factor Humidity correction factor Multiply to obtain the corrected weights. .

4. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: the vibration intensity index calculation further comprises the following steps: determining whether the vibration intensity index exceeds a preset vibration threshold; if the determination result is yes, coupling calculating the dynamically corrected risk value based on the vibration intensity index to obtain a coupling corrected risk value, and taking the coupling corrected risk value as a risk parameter; if the determination result is no, maintaining the dynamically corrected risk value as a risk parameter.

5. The BIM-based deconstruction project contamination prediction and optimization method of claim 4, wherein: the calculation process of the coupling corrected risk value is as follows: acquiring a spatial propagation path length between the building duct and the vibration source, and calculating a sound wave attenuation factor based on the propagation path length ; Obtaining a vibration intensity index of a building duct and dynamically revising the risk value The coupling revised risk value is calculated according to the following formula : wherein, is a preset vibration coupling coefficient.

6. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: the calculation of the vibration intensity index based on the vibration energy spectrum data comprises the following steps: obtaining vibration energy spectrum data, and performing frequency band analysis on the vibration energy spectrum data to extract sound pressure values of each frequency band within a frequency range of 1 Hz to 50 Hz; determining a pipeline structure resonance coefficient from a preset parameter library according to the BIM model of the building pipeline; multiplying the sound pressure values and the pipeline structure resonance coefficients corresponding to the frequencies to obtain weighted vibration energies of each frequency band; The vibration intensity index is calculated by integrating the weighted vibration energy in the range of 1 Hz to 50 Hz.

7. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: The process of calculating the basic risk value by the risk assessment model is: Acquiring dust concentration data inside a building duct ; determining a residue type according to the residue volatility parameter, and matching a minimum ignition energy corresponding to the residue type from a preset parameter library ; Determining electrostatic coefficient based on material conductivity parameter ; According to the oxygen concentration data, matching oxygen concentration correction factor wherein the oxygen concentration correction factor approaches zero when the oxygen concentration is below the lower explosion limit approaches zero. Based on the above parameters, a base risk value is calculated , the calculation formula is: 。 8. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: The risk parameter exceeding the preset safety threshold further includes: Obtain the pipe wall potential gradient data of the building pipeline; Map the pipe wall potential gradient data to the surface of the corresponding pipe segment of the BIM model to generate a potential distribution surface; Extract the grid elements in the potential distribution surface whose gradient values exceed 3 kV / m; Units with a continuous area exceeding 0.1 m 2 are merged into an independent coating area; Treat the independent coating area as an antistatic coating area, and mark it in the BIM model; the coating thickness of the antistatic coating area is in linear proportion to the gradient value in the corresponding position in the potential distribution surface.

9. The BIM-based deconstruction project contamination prediction and optimization method of claim 1, wherein: The risk parameter exceeding the preset safety threshold further includes: Identify the flange connection points or weld coordinates between the pipelines based on the BIM model; Obtain the vibration intensity index of the position corresponding to the flange connection points or weld coordinates; According to the vibration intensity index, select the stiffness grade of the shock absorber, install hydraulic shock absorbers at positions whose vibration intensity index exceeds the preset stiffness threshold, and install rubber shock absorbers at the remaining positions.

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