A Method for Monitoring Assembly Offset of Concrete Structures Based on BIM Technology

Abstract

This invention relates to the field of BIM assembly technology, specifically to a method for monitoring the assembly offset of concrete structures based on BIM technology. For any precast column, this invention acquires the temperature data, preset linear expansion coefficient of concrete, length of the free deformation section of the precast column, and original offset of the construction scene at each moment, obtaining the temperature influence coefficient at each moment; it also acquires the wind speed data, air density, windward area of ​​the precast column, and fixed constraint force of the construction scene at each moment, and, combined with the morphological characteristics of the precast column, obtains the wind speed influence coefficient at each moment; it obtains the comprehensive environmental influence coefficient and random interference factor of different precast columns at different moments, and, combined with the difference between the original offset and the theoretical benchmark deviation, obtains the implicit offset optimal state time series sequence; and monitors the assembly offset. This invention improves the accuracy of assembly offset monitoring by accurately analyzing environmental interference and individual random interference under time series conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of BIM assembly, in particular to a concrete structure assembly offset monitoring method based on BIM technology. BACKGROUND

[0002] The assembly accuracy of prefabricated construction determines the performance of the whole structure, and the slight offset of a single component may be accumulated and superimposed through force transmission. When the deviation exceeds the limit allowed by the specification, it may cause problems such as node stress concentration and reduced fatigue life of the support, resulting in safety hazards. Therefore, by monitoring the deviation change in time, the problems of forced assembly and rework caused by excessive deviation can be avoided.

[0003] In the prior art, BIM technology provides visual data support for assembling concrete structures. However, the original assembly offset data is mixed with irrelevant interference, including systematic offset caused by temperature expansion and contraction and wind load, and random interference of a single prefabricated column, resulting in poor accuracy of monitoring data. SUMMARY

[0004] In order to solve the technical problem that irrelevant interference is mixed in the original assembly offset data and the accuracy of monitoring data is poor, the purpose of the present application is to provide a concrete structure assembly offset monitoring method based on BIM technology, and the technical solution adopted is as follows:

[0005] The present application provides a concrete structure assembly offset monitoring method based on BIM technology, which comprises:

[0006] For any prefabricated column, the temperature data of the construction scene where the prefabricated column is located at each time, the preset concrete linear expansion coefficient, the free deformation section length of the prefabricated column and the original offset are obtained, and the temperature influence coefficient at each time is obtained. The wind speed data, air density, windward area and fixed constraint force of the prefabricated column of the construction scene where the prefabricated column is located at each time are obtained, and the wind speed influence coefficient at each time is obtained in combination with the morphological characteristics of the prefabricated column;

[0007] According to the temperature influence coefficients and wind speed influence coefficients of multiple prefabricated columns at different times under different construction scenes, the comprehensive environmental influence coefficients at each time under each construction scene are obtained. According to the original offset distribution of different prefabricated columns at each time under the same construction scene, the random interference factor of each prefabricated column at each time is obtained;

[0008] According to the differences between the comprehensive environmental influence coefficients, random interference factors and original offset of different prefabricated columns at different times and the theoretical reference deviation, the implicit offset state corresponding to a preset number of data clusters is obtained, and the timing sequence of the optimal implicit offset state is obtained.

[0009] The assembly offset is monitored according to the difference between the implicit offset optimal state distribution in the implicit offset optimal state time sequence and the difference between the original offset and the theoretical reference deviation.

[0010] Further, the temperature influence coefficient acquisition method comprises:

[0011] Obtaining the temperature difference between the temperature data at each moment and the standard reference temperature;

[0012] Obtaining the product of the temperature difference, the preset concrete linear expansion coefficient and the free deformation section length, calculating the ratio of the product result and the original offset as the temperature influence coefficient.

[0013] Further, the wind speed influence coefficient acquisition method comprises:

[0014] Obtaining the wind load shape coefficient based on the morphological characteristics of the prefabricated column;

[0015] Obtaining the product of the square value of the wind speed data at each moment, the wind load shape coefficient, the air density and the windward area of the prefabricated column, calculating the ratio between the product result and the fixed constraint force of the prefabricated column as the wind speed influence coefficient.

[0016] Further, the comprehensive environmental influence coefficient acquisition method comprises:

[0017] According to the temperature influence coefficient and the wind speed influence coefficient of the plurality of prefabricated columns at different moments under different construction scenes, obtaining the temperature weight and the wind speed weight of each construction scene;

[0018] Obtaining the first product of the temperature weight and the temperature environment coefficient; obtaining the second product of the wind speed weight and the wind speed environment coefficient; obtaining the sum value of the first product and the second product as the comprehensive environmental influence coefficient.

[0019] Further, the temperature weight and wind speed weight acquisition method comprises:

[0020] Obtaining the reference offset of the prefabricated column under the condition of no wind and constant temperature; taking the temperature influence coefficient, the wind speed influence coefficient and the no-wind constant temperature reference offset of each prefabricated column at different moments as a sample, based on the sample set of the Catboost algorithm model trained by all prefabricated columns under different construction scenes, obtaining the temperature weight and the wind speed weight of each construction scene.

[0021] Further, the random interference factor acquisition method comprises:

[0022] For the same construction scene, obtaining the mean value of the original offset of all prefabricated columns as the common reference value;

[0023] Obtain the difference between the original offset of each prefabricated column and the common reference value as the offset difference; obtain the fluctuation degree of the original offset of all prefabricated columns as the offset fluctuation degree;

[0024] Calculate the ratio of the offset difference and the offset fluctuation degree of each prefabricated column as the random interference factor of each prefabricated column.

[0025] Further, the method for obtaining the implicit offset state corresponding to the preset number of data clusters comprises:

[0026] According to the comprehensive environmental influence coefficient, the random interference factor, and the difference between the original offset and the theoretical reference deviation of different prefabricated columns at different time, a preset number of data clusters are obtained, one cluster corresponding to one implicit offset state; the average of the sum of the comprehensive environmental influence coefficient and the random interference factor at each time in each cluster at each time is obtained as the overall influence degree of each cluster at each time.

[0027] An implicit offset state increasing sequence is constructed in the order of the overall influence degree from small to large, and the corresponding order is marked.

[0028] Further, the method for obtaining the data cluster comprises:

[0029] Each prefabricated column at each time is formed into a data sequence according to the comprehensive environmental influence coefficient, the random interference factor, and the difference between the original offset and the theoretical reference deviation, and a K-means clustering algorithm is performed to obtain a preset number of data clusters.

[0030] Further, the method for obtaining the implicit offset optimal state comprises:

[0031] In any implicit offset state, the probability density function value of the independent two-dimensional standard normal distribution of the two-dimensional vector composed of the comprehensive environmental influence coefficient and the random interference factor at each time is obtained.

[0032] The number of prefabricated columns of the implicit offset state of each order in the implicit offset state increasing sequence at the minimum time is counted, and the ratio of the number of prefabricated columns of the implicit offset state of each order to the number of all prefabricated columns is calculated as the initial state probability of the implicit offset state of each order.

[0033] According to the time sequence, the implicit offset optimal state time sequence is obtained by using the Viterbi algorithm of the HMM model based on the probability density function value corresponding to the two-dimensional vector in the implicit offset state of each order at each time and the initial state probability.

[0034] The present application has the following beneficial effects:

[0035] The application obtains temperature data of a construction scene where the prefabricated column is located at each time, a preset concrete linear expansion coefficient, a free deformation section length and an original offset of the prefabricated column, obtains a temperature influence coefficient at each time, and reflects the strength of temperature interference on offset data; obtains wind speed data, air density, a windward area of the prefabricated column and a fixed restraint force of the construction scene where the prefabricated column is located at each time, obtains a wind speed influence coefficient at each time in combination with morphological characteristics of the prefabricated column, and quantifies the interference degree of wind speed on offset data; obtains a comprehensive environmental influence coefficient and a random interference factor of different prefabricated columns at different times, obtains a time sequence sequence of an optimal state of hidden offset in combination with the difference between the original offset and a theoretical reference deviation, and accurately captures the evolution trend of interference intensity at different times; and the assembly offset is monitored. The application accurately analyzes environmental interference and individual random interference under time sequence, and improves the accuracy of assembly offset monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on these drawings.

[0037] Figure 1 A flowchart of a concrete structure assembly offset monitoring method based on BIM technology provided by an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific embodiments, structure, features and effects of a concrete structure assembly offset monitoring method based on BIM technology according to the present application are described in detail as follows by combining with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0040] The specific scheme of the concrete structure assembly offset monitoring method based on BIM technology provided by the present application is specifically described below in combination with the drawings.

[0041] Please refer to Figure 1It shows a flow chart of a concrete structure assembly offset monitoring method based on BIM technology provided by an embodiment of the application, and the specific method comprises the following steps:

[0042] Step S1: For any prefabricated column, obtain the temperature data of the construction scene where the prefabricated column is located at each moment, the preset concrete linear expansion coefficient, the free deformation section length and the original offset of the prefabricated column, and obtain the temperature influence coefficient at each moment; obtain the wind speed data, air density, windward area and fixed constraint force of the construction scene where the prefabricated column is located at each moment, and obtain the wind speed influence coefficient at each moment in combination with the morphological characteristics of the prefabricated column.

[0043] In the embodiment of the application, considering the interference and mixture of the original offset data of the prefabricated column assembly, the assembly offset exists errors, and therefore it is necessary to analyze the environment and assembly deviation; first, install temperature sensors and wind speed sensors in the construction area to obtain the temperature data and wind speed data of the construction scene where the prefabricated column is located in real time; obtain the free deformation section length, windward area, fixed constraint force and original offset of the prefabricated column based on the BIM model. The construction scene can be obtained based on the BIM model, including the installation stage and the construction area, the installation stage is divided into temporary fixing or permanent fixing, and the construction area is divided into indoor area or outdoor area.

[0044] When there is a difference between the construction site temperature and the standard temperature, the prefabricated column will expand and contract in length due to the difference in thermal motion of the internal cement and stone, and when the expansion and contraction are constrained by the temporary fixing device, the prefabricated column will present in the form of offset, and the higher the proportion of the offset caused by temperature, the greater the degree of temperature influence; therefore, by analyzing the temperature data to reflect the change trend of the temperature, the greater the change of the temperature, the greater the change of the temperature influence coefficient; the preset concrete linear expansion coefficient reflects the deformation amount of the unit length of the concrete caused by the unit temperature change, which is a material inherent parameter, and the greater the concrete linear expansion coefficient, the greater the temperature influence coefficient; the free deformation section length reflects the section length of the prefabricated pile that is not fixed and constrained and can freely expand and contract with the temperature, and the greater the section length, the longer the free expansion and contraction with the temperature, and the greater the temperature influence coefficient; for any prefabricated column, obtain the temperature data of the construction scene where the prefabricated column is located at each moment, the preset concrete linear expansion coefficient, the free deformation section length and the original offset of the prefabricated column, and obtain the temperature influence coefficient at each moment.

[0045] In an embodiment of the application, the method for obtaining the temperature influence coefficient comprises the following steps:

[0046] Obtain the temperature difference between the temperature data at each moment and the standard reference temperature; obtain the product of the temperature difference, the preset concrete linear expansion coefficient and the free deformation section length, calculate the ratio of the product result and the original offset, and take the ratio as the temperature influence coefficient.

[0047] Wherein, the dimension of temperature difference is ℃, the dimension of preset concrete linear expansion coefficient is , the dimension of free deformation segment length and original offset is mm, and thus the temperature influence coefficient obtained is a dimensionless data.

[0048] It should be noted that in the embodiments of the present application, the standard reference temperature can be obtained by the implementer according to relevant professional knowledge in advance, and is set to 20℃; the concrete linear expansion coefficient is a material inherent parameter, which can be accurately measured by the implementer according to relevant thermal expansion coefficient experiments.

[0049] It should be noted that the temperature difference represents the absolute value of the difference between the calculated temperatures, the greater the temperature difference, the greater the deviation of the temperature at each moment from the standard reference temperature, the greater the possibility of environmental interference, and the greater the temperature influence. The greater the concrete linear expansion coefficient, the greater the free deformation segment length, the longer the temperature free expansion, and the greater the temperature influence coefficient; the molecular product result reflects the offset influence of temperature on the prefabricated column, and the greater the relative original offset, the greater the temperature influence coefficient.

[0050] The interference degree of wind speed on the offset data is that when the wind hits the side of the prefabricated column, the kinetic energy will be converted into wind load acting on the column, the faster the wind speed, the greater the force of air molecules hitting the column, and the greater the wind speed influence coefficient; the wind speed data, air density, windward area of the prefabricated column and fixed constraint force of the prefabricated column at each moment are obtained, and the wind speed influence coefficient at each moment is obtained in combination with the morphological characteristics of the prefabricated column.

[0051] Preferably, in an embodiment of the present application, the method for obtaining the wind speed influence coefficient comprises:

[0052] Based on the morphological characteristics of the prefabricated column, the wind load shape coefficient is obtained; it should be noted that the wind load shape coefficient is determined by the geometric shape and can be obtained by the implementer in advance according to relevant professional knowledge, such as 1.2 for a rectangular shape and 0.8 for a circular shape, which will not be described here.

[0053] The product of the square value of the wind speed data, the wind load shape coefficient, the air density and the windward area of the prefabricated column at each moment is obtained, and the ratio between the product result and the fixed constraint force of the prefabricated column is calculated as the wind speed influence coefficient.

[0054] Wherein, the dimension of wind speed data is m / s, the dimension of wind speed data square value is m 2 / s 2 ; the wind load shape coefficient is a dimensionless data, the dimension of air density is kg / m³, the dimension of windward area of the prefabricated column is m 2 , and the dimension of fixed constraint force of the prefabricated column is kg·m / s², and thus the wind speed influence coefficient obtained is a dimensionless data.

[0055] It should be noted that the air density can be set by the implementer according to the air density at the standard reference temperature, that is, 1.204 kg / m³.

[0056] It should be noted that the essence of the influence of wind speed is the conversion of the kinetic energy of flowing air into lateral thrust, the greater the wind speed, the greater the lateral thrust generated, the greater the possibility of deviation, the greater the wind speed; the greater the air density, the greater the thrust, the greater the wind speed influence coefficient; the windward area of the prefabricated column reflects the carrier of the action, the greater the windward area of the prefabricated column, the greater the wind speed influence coefficient; the final deviation caused by wind is the result of the resistance between wind load and the constraint force of the prefabricated column, the greater the wind load, the smaller the constraint force, the greater the deviation of the prefabricated column caused by the wind load, the greater the wind speed influence coefficient.

[0057] Step S2: According to the temperature influence coefficient and the wind speed influence coefficient of the plurality of prefabricated columns at different time under different construction scenes, the comprehensive environmental influence coefficient at each time under each construction scene is obtained; according to the original deviation distribution of different prefabricated columns at each time under the same construction scene, the random interference factor of each prefabricated column at each time is obtained.

[0058] Considering that the action weights of the two influence coefficients are different under different construction scenes, only using a single coefficient correction or simple superposition may ignore the dynamic correlation of the construction scene and the weight, therefore, it is necessary to combine the temperature influence coefficient and the wind speed influence coefficient to convert the two independent environmental interference factors into a unified scene index, which provides a quantitative basis for reflecting the real deviation of the process level in the subsequent, and ensures that the final quality judgment result is consistent with the actual situation of the prefabricated column construction. According to the temperature influence coefficient and the wind speed influence coefficient of the plurality of prefabricated columns at different time under different construction scenes, the comprehensive environmental influence coefficient at each time is obtained.

[0059] Preferably, in an embodiment of the present application, the method for obtaining the comprehensive environmental influence coefficient comprises:

[0060] According to the temperature influence coefficient and the wind speed influence coefficient of the plurality of prefabricated columns at different time under different construction scenes, the temperature weight and the wind speed weight of each construction scene are obtained.

[0061] Preferably, in an embodiment of the present application, the method for obtaining the temperature weight and the wind speed weight comprises:

[0062] The reference deviation of the prefabricated column under the condition of no wind and constant temperature is obtained; the temperature influence coefficient, the wind speed influence coefficient and the no-wind constant-temperature reference deviation of each prefabricated column at different time are taken as a sample, the sample set for training the Catboost algorithm model composed of all the prefabricated columns under different construction scenes is obtained, and the temperature weight and the wind speed weight of each construction scene are obtained.

[0063] obtaining a first product of a temperature weight and a temperature environment coefficient; obtaining a second product of a wind speed weight and a wind speed environment coefficient; obtaining a sum value of the first product and the second product as a comprehensive environment coefficient.

[0064] It should be noted that in the embodiments of the present application, 150-500 precast columns can be selected for training; after the model learns the influence law of environmental factors on the offset under different construction scenes, the temperature weight and the wind speed weight of each construction scene are output through a feature importance decomposition method such as SHAP value analysis; the specific CatBoost algorithm and feature importance decomposition method are well known to those skilled in the art, and will not be described here.

[0065] The residual single-column random interference in the original offset data can be mixed with the process signal, and the common process signal of the column under the same scene needs to be stripped to extract the interference specifically reflecting the individual independent factors of the single column.

[0066] Preferably, in an embodiment of the present application, the method for obtaining the random interference factor comprises:

[0067] For the same construction scene, the mean value of the original offset of all precast columns is obtained as a common reference value;

[0068] The difference between the original offset of each precast column and the common reference value is obtained as an offset difference; the fluctuation degree of the original offset of different precast columns is obtained as an offset fluctuation degree;

[0069] The ratio of the offset difference and the offset fluctuation degree of each precast column is calculated as the random interference factor of each precast column.

[0070] It should be noted that the difference represents the absolute value of the difference, and in the embodiments of the present application, the fluctuation degree is represented by calculating the standard deviation; the greater the standard deviation, the greater the fluctuation degree; the smaller the standard deviation, the smaller the fluctuation degree; in other embodiments of the present application, the fluctuation degree can be reflected by calculating the variance; the specific means are well known to those skilled in the art, and will not be described here.

[0071] Step S3: obtaining the implicit offset state corresponding to the preset number of data clusters according to the comprehensive environmental influence coefficient, the random interference factor, and the difference between the original offset and the theoretical reference deviation of different precast columns at different times, and obtaining the implicit offset optimal state time sequence.

[0072] The comprehensive environmental influence coefficient and the random interference factor are combined to form a double-factor combined observation vector, and the optimization model outputs a sequence reflecting the influence level of the factors; the single-column random interference remaining in the original offset data will be mixed with the process signal, and the common process signal of the column in the same scene needs to be stripped, and the interference specifically reflecting the individual independent factors of the single column is extracted, and therefore, according to the differences of the comprehensive environmental influence coefficient, the random interference factor, the original offset and the theoretical reference deviation of different prefabricated columns at different time points, the preset number of data clusters corresponding to the implicit offset state are obtained, and the timing sequence of the optimal implicit offset state is obtained.

[0073] Preferably, in an embodiment of the present application, the method for obtaining the implicit offset state corresponding to the preset number of data clusters comprises:

[0074] According to the differences of the comprehensive environmental influence coefficient, the random interference factor, the original offset and the theoretical reference deviation of different prefabricated columns at different time points, the preset number of data clusters corresponding to the implicit offset state are obtained.

[0075] Preferably, in an embodiment of the present application, the method for obtaining the data cluster comprises:

[0076] The differences of the comprehensive environmental influence coefficient, the random interference factor, the original offset and the theoretical reference deviation of each prefabricated column at each time point form a data sequence, and the K-means clustering algorithm is performed to obtain the preset number of data clusters.

[0077] It should be noted that, in the embodiments of the present application, the K-means clustering algorithm clusters data with similar characteristics into a cluster, and there is a difference between different clusters; the K-means clustering algorithm is performed, the preset number is set to 3 according to relevant historical experience, 3 clusters are obtained, and the 3 clusters correspond to 3 implicit states; the specific clustering algorithm is a technical means familiar to those skilled in the art, and will not be described here.

[0078] The average of the sum of the comprehensive environmental influence coefficient and the random interference factor of all time points in each cluster at each time point is obtained as the overall influence degree of each cluster at each time point.

[0079] An implicit offset state increasing sequence is constructed in the order from small to large according to the overall influence degree, and the corresponding order is marked.

[0080] Based on this, for the divided 3 data clusters, the overall influence degree is sorted from small to large, the first data sequence corresponds to the implicit offset state of the first order, the second data cluster corresponds to the implicit offset state of the second order, and the third data cluster corresponds to the implicit offset state of the third order, and the implicit offset state value is in an increasing state.

[0081] It should be noted that the theoretical reference deviation is the theoretical process reference specified in the design drawing.

[0082] Preferably, in one embodiment of the present application, the method for obtaining the optimal implicit offset state comprises:

[0083] Considering that the comprehensive environmental impact coefficient and the random interference factor are two independent variables, in any implicit offset state, the probability density function value of the independent two-dimensional standard normal distribution of the two-dimensional vector composed of the comprehensive environmental impact coefficient and the random interference factor at each time is obtained;

[0084] The number of precast columns of the implicit offset state of each order in the implicit offset state increasing sequence at the statistical minimum time is counted, and the ratio of the number of precast columns of the implicit offset state of each order to the total number of precast columns is calculated as the initial state probability of the implicit offset state of each order.

[0085] According to the time sequence, the corresponding probability density function value in the implicit offset state of each order based on the two-dimensional vector at each time and the initial state probability are analyzed in sequence, and the Viterbi algorithm of the HMM model is used to obtain the implicit offset optimal state time sequence.

[0086] It should be noted that the offset evolution is a dynamic time sequence process, and the state of the offset process cannot be directly observed, but can only be indirectly inferred through observable factors. Therefore, the hidden Markov model HMM and the Viterbi algorithm based on the dynamic programming idea can efficiently find the state sequence with the maximum probability. The Viterbi algorithm of the HMM model is a well-known technical means to those skilled in the art, and will not be described here. As an example, if there are 3 time points, the probabilities of the 3 states at the initial time are: for any state, the product of the initial state probability and the probability density function value is obtained as the path probability of each state; for each state at each subsequent time, the product of the initial state probability, the probability density function value, and the path probability of each state at the previous time is obtained as the path probability from each state at the previous time to each state at the next time, and the path probability with the maximum value at each state is obtained as the optimal path of each state. Backtracking is performed to find the state with the maximum path probability in the optimal path of all states at each time, and the corresponding state is taken as the optimal implicit offset state. The optimal implicit offset states are arranged in time sequence from small to large to form the implicit offset optimal state time sequence.

[0087] Step S4: monitoring the assembly offset according to the distribution of the optimal implicit offset state in the optimal implicit offset state time sequence and the difference between the original offset and the theoretical reference deviation.

[0088] Based on this, the maximum value and the minimum value of the difference between the original offset and the theoretical reference deviation corresponding to each implicit offset state constitute a range, which is taken as the reference measurement deviation range of each implicit offset state. In the sequence of the optimal implicit offset state, if the first sequence of implicit offset states is continuously output and the actual measurement deviation is stable in the first sequence of the reference measurement deviation range, it indicates that the true offset is risk-free, and normal construction and monitoring frequency are maintained. If the sequence of adjacent implicit offset states increases or the second sequence of implicit offset states appears continuously at adjacent time points, and the measurement deviation rises within the second sequence of the reference measurement deviation range, it indicates that the true offset begins to accumulate. When the third sequence of implicit offset states appears or the second sequence of implicit offset states appears continuously for three time points, and the measurement deviation is in the third sequence of implicit offset states, it indicates that even if the interference effect is deducted, the true offset is still close to the safety threshold, and the risk of deviation distortion caused by interference is increased, so further encryption monitoring and correction are prepared. If there are two or more time points corresponding to the third sequence of implicit offset states, whether the measurement deviation fluctuates or not, it is determined that the true offset risk is out of control, and the assembly is immediately stopped for correction.

[0089] Based on this, the evolution trend of the intensity of interference at different time points is captured through the time sequence representation of the strength of the factor influence, and a direct basis is provided for formulating a hierarchical early warning rule to improve the accuracy of assembly offset monitoring.

[0090] In summary, for any prefabricated column, the temperature data of the construction scene where the prefabricated column is located at each time point, the preset concrete linear expansion coefficient, the free deformation section length of the prefabricated column, and the original offset are obtained, and the temperature influence coefficient at each time point is obtained. The wind speed data of the construction scene where the prefabricated column is located at each time point, the air density, the windward area of the prefabricated column, and the fixed restraint force are obtained, and the wind speed influence coefficient at each time point is obtained in combination with the morphological characteristics of the prefabricated column. The comprehensive environmental influence coefficient and the random interference factor of different prefabricated columns at different time points are obtained, the difference between the original offset and the theoretical reference deviation is obtained, and the sequence of the optimal implicit offset state is obtained. The assembly offset is monitored. The present application improves the accuracy of assembly offset monitoring by accurately analyzing the environmental interference and individual random interference at different time points.

[0091] It should be noted that the above-mentioned embodiment order of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0092] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring an assembly offset of a concrete structure based on BIM technology, characterized by, The method comprises: For any prefabricated column, the temperature data of the construction scene where the prefabricated column is located at each time, the preset concrete linear expansion coefficient, the free deformation section length and the original offset of the prefabricated column are obtained, and the temperature influence coefficient at each time is obtained; the wind speed data, air density, windward area and fixed restraint force of the construction scene where the prefabricated column is located at each time are obtained, and the wind speed influence coefficient at each time is obtained in combination with the morphological characteristics of the prefabricated column; According to the temperature influence coefficient and the wind speed influence coefficient of the plurality of prefabricated columns at different times under different construction scenes, the comprehensive environmental influence coefficient at each time under each construction scene is obtained; according to the original offset distribution of different prefabricated columns at each time under the same construction scene, the random interference factor of each prefabricated column at each time is obtained; According to the differences between the comprehensive environmental influence coefficient, the random interference factor, the original offset and the theoretical reference deviation of the prefabricated columns at different times, the implicit offset state corresponding to the preset number of data clusters is obtained, and the implicit offset optimal state time sequence is obtained; According to the difference between the implicit offset optimal state distribution in the implicit offset optimal state time sequence and the difference between the original offset and the theoretical reference deviation, the assembly offset is monitored; The method for obtaining the comprehensive environmental influence coefficient comprises: obtaining the temperature weight and the wind speed weight of each construction scene according to the temperature influence coefficient and the wind speed influence coefficient of the plurality of prefabricated columns at different times under different construction scenes; obtaining the first product of the temperature weight and the temperature influence coefficient; obtaining the second product of the wind speed weight and the wind speed influence coefficient; obtaining the sum of the first product and the second product as the comprehensive environmental coefficient; The method for obtaining the temperature weight and the wind speed weight comprises: obtaining the reference offset of the prefabricated column under the condition of no wind and constant temperature; taking the temperature influence coefficient, the wind speed influence coefficient and the no-wind constant temperature reference offset of each prefabricated column at different times as a sample, and based on the sample set for training the Catboost algorithm model composed of all the prefabricated columns under different construction scenes, the temperature weight and the wind speed weight of each construction scene are obtained; The method for obtaining the random interference factor comprises: for the same construction scene, obtaining the mean value of the original offset of all the prefabricated columns as a common reference value; according to the difference between the original offset of each prefabricated column and the common reference value, and the fluctuation degree of the original offset of all the prefabricated columns, the random interference factor of each prefabricated column is obtained. 2.The method of claim 1, wherein, The method for obtaining the temperature influence coefficient comprises: Obtaining the temperature difference between the temperature data at each time and the standard reference temperature; Obtaining the product of the temperature difference, the preset concrete linear expansion coefficient and the free deformation section length, calculating the ratio of the product result and the original offset as the temperature influence coefficient. 3.The method of claim 1, wherein, The method for obtaining the wind speed influence coefficient comprises: Based on the morphological characteristics of the prefabricated column, the wind load shape coefficient is obtained; Obtaining the product of the square value of the wind speed data at each time, the wind load shape coefficient, the air density and the windward area of the prefabricated column, and calculating the ratio between the product result and the fixed restraint force of the prefabricated column as the wind speed influence coefficient. 4.The method of claim 1, wherein, The method for obtaining the random interference factor comprises: Obtain the difference between the original offset of each prefabricated column and the common reference value as the offset difference; obtain the fluctuation degree of the original offset of all prefabricated columns as the offset fluctuation degree; Calculate the ratio of the offset difference and the offset fluctuation degree of each prefabricated column as the random interference factor of each prefabricated column. 5.The method of claim 1, wherein, The method for obtaining the implicit offset state corresponding to the preset number of data clusters comprises: According to the comprehensive environmental influence coefficient, the random interference factor, and the difference between the original offset and the theoretical reference deviation of different prefabricated columns at different times, obtain the preset number of data clusters, one cluster corresponding to one implicit offset state; obtain the average of the sum of the comprehensive environmental influence coefficient and the random interference factor at each time in each cluster at each time as the overall influence degree of each cluster at each time; Construct an implicit offset state increasing sequence according to the overall influence degree from small to large, and mark the corresponding order.

6. The method of claim 5, wherein the method further comprises: The method for obtaining the data cluster comprises: Form a data sequence by the comprehensive environmental influence coefficient, the random interference factor, and the difference between the original offset and the theoretical reference deviation of each prefabricated column at each time, and perform K-means clustering algorithm to obtain the preset number of data clusters. 7.The method of claim 5, wherein, The method for obtaining the optimal implicit offset state comprises: In any implicit offset state, obtain the probability density function value of the independent two-dimensional standard normal distribution of the two-dimensional vector composed of the comprehensive environmental influence coefficient and the random interference factor at each time; Statistically count the number of prefabricated columns in the implicit offset state of each order in the implicit offset state increasing sequence at the minimum time, calculate the ratio of the number of prefabricated columns in the implicit offset state of each order to the total number of all prefabricated columns as the initial state probability of the implicit offset state of each order; According to the time sequence, analyze in turn, based on the corresponding probability density function value in the implicit offset state of each order of the two-dimensional vector at each time and the initial state probability, obtain the time sequence sequence of the optimal implicit offset state by using the Viterbi algorithm of the HMM model.

Images