Engineering supervision method and system based on BIM

Abstract

The invention relates to the technical field of engineering supervision and supervision, and particularly discloses a BIM-based engineering supervision and supervision method and system, and the method comprises the steps: enabling an engineering design drawing and material information to trace a database, importing professional BIM modeling software through a format conversion technology, introducing geographic information system data, obtaining a BIM model, collecting construction progress data in the BIM model, and carrying out the construction progress data in the BIM model; the method comprises the steps of obtaining progress evaluation factors, determining a construction progress evaluation grade, determining a construction quality evaluation grade based on a fuzzy comprehensive evaluation model, constructing a BIM construction safety risk evaluation model, outputting a construction safety evaluation grade in combination with a BP neural network model, and sending out BIM engineering supervision risk early warning. The problems that a traditional supervision method is low in information transmission efficiency, misunderstanding and omission are likely to occur, information islands are likely to be caused, the management efficiency is low, a real-time and comprehensive monitoring means is lacked in the aspect of quality control, and potential quality hazards are difficult to find and solve in time are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering supervision and management, and specifically provides a BIM-based engineering supervision and management method and system. Background Art

[0002] With the rapid development of information technology, the construction industry is moving towards informatization and digitalization. The traditional construction project management mode is gradually difficult to meet the complex requirements of modern construction projects. Using advanced information technology to improve the efficiency and quality of construction project management has become an inevitable trend in the industry's development. As an important means of construction industry informatization, BIM technology can integrate various information throughout the life cycle of a construction project, providing new ideas and methods for engineering supervision and management. BIM technology can create a three-dimensional visual building model, enabling supervisors to intuitively understand the structure, layout, and construction process of a construction project. Through construction simulation, potential problems that may occur during construction can also be discovered in advance, providing strong decision-making support for supervision and management.

[0003] Currently, there are still some deficiencies in the research on BIM-based engineering supervision and management. Specifically, information in traditional supervision methods is mainly transmitted through forms such as drawings and documents. The transmission efficiency of information is low, and misunderstandings and omissions are likely to occur. There are many problems in aspects such as information communication and coordination management, which are likely to lead to problems such as information silos and low management efficiency. In terms of quality control, it mainly relies on manual inspection and sampling inspection, lacking real-time and comprehensive monitoring means, and it is difficult to discover and solve quality hazards in a timely manner. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a BIM-based engineering supervision and management method and system, which can effectively solve the problems involved in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: In the first aspect of the present invention, a BIM-based engineering supervision and management method is provided, including the following steps: Import engineering design drawings and a material information traceability database into professional BIM modeling software through format conversion technology, extract basic building structure and mechanical and electrical equipment information to obtain an initial BIM model; Introduce geographic information system data, obtain the coordinates of the construction site, and fuse the coordinates of the construction site with the initial BIM model through a coordinate matching algorithm to obtain a BIM model; Assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model to obtain a progress evaluation factor, and determine the construction progress evaluation level; Analyze the construction quality status based on the component attributes and quality monitoring data in the BIM model, and determine the construction quality evaluation level based on a fuzzy comprehensive evaluation model; Obtain construction safety risk impact data, analyze the construction safety risk impact data, and construct a BIM construction safety risk assessment model. The construction safety risk impact data specifically includes personnel status data, equipment operation status data, and environmental status data; Based on the output of the BIM construction safety risk assessment model, combined with a BP neural network model, output the construction safety evaluation level; Based on the construction progress evaluation level, construction quality evaluation level, and construction safety evaluation level, issue a BIM engineering supervision risk warning.

[0006] As a further method, introduce geographic information system data, obtain the coordinates of the construction site, and fuse the coordinates of the construction site with the initial BIM model through a coordinate matching algorithm to obtain a BIM model. The specific analysis process is as follows: Introduce geographic information system data to obtain the coordinates of the construction site (X GIS , Y GIS , Z GIS ); Based on the coordinate matching algorithm, fuse the coordinates of the construction site with the initial BIM model to obtain the coordinates of the construction site in the BIM model (X BIM , Y BIM , Z BIM ). The coordinate matching formula is:

[0007]

[0008] In the formula, X GIS is the abscissa of the construction site, Y GIS is the ordinate of the construction site, Z GIS is the vertical coordinate of the construction site, X BIM is the abscissa of the construction site in the BIM model, Y BIM is the ordinate of the construction site in the BIM model, Z BIM is the vertical coordinate of the construction site in the BIM model, S x is the scaling factor in the x-axis direction, S yis the scaling factor in the y-axis direction, S z is the scaling factor in the z-axis direction, T x is the translation factor in the x-axis direction, T y is the translation factor in the y-axis direction, T z is the translation factor in the z-axis direction;

[0009] Fuse the coordinates of the construction site in the BIM model with the initial BIM model to obtain the BIM model.

[0010] As a further method, assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model, obtain a progress evaluation factor, and determine the construction progress evaluation level. The specific analysis process is as follows: Assign time attributes to each construction task in the BIM model, collect construction progress data, obtain the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio of each construction task; Based on the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio of each construction task, construct a progress analysis model and output a progress evaluation factor. The progress evaluation factor is used as the analysis basis for determining the construction progress evaluation level; Obtain the progress evaluation factor - construction progress evaluation level mapping table pre-stored in the database, and by looking up the mapping table, according to the progress evaluation factor, find the matching construction progress evaluation level.

[0011] As a further method, for the progress analysis model, the specific analysis process is as follows:

[0012]

[0013] In the formula, sv is the progress evaluation factor, finish is the number of completed construction tasks, ev i is the completion ratio of the i-th construction task, pv i is the planned completion ratio of the i-th construction task, i is the construction task number, i = 1, 2, 3,..., w, and w is the total number of construction tasks.

[0014] As a further method, based on the component attributes and quality monitoring data in the BIM model, analyze the construction quality status. The specific analysis process is as follows: Obtain the actual strength f of the concrete actual and the designed strength f of the concrete design , and calculate the concrete strength quality factor fy:

[0015]

[0016] Obtain the appearance defect rate d of the concrete component defect , and calculate the appearance quality factor dy of the concrete component:

[0017]

[0018] In the formula, e is the natural constant;

[0019] Obtain the actual size L of the concrete component actual and the designed size L of the concrete component design , and calculate the size quality factor Ly of the concrete component;

[0020]

[0021] As a further method, based on the fuzzy comprehensive evaluation model, determine the construction quality evaluation level. The specific analysis process is as follows: Determine the fuzzy comprehensive evaluation factor set U, U = {u1, u2, u3}, where u1 represents the concrete strength quality factor, u2 represents the appearance quality factor of the concrete component, and u3 represents the size quality factor of the concrete component; Determine the construction quality evaluation level V, V = {v1, v2, v3}, where v1 is the first level, v2 is the second level, and v3 is the third level; Obtain the membership degree r of each evaluation factor in the database for each evaluation level mn , m = 1, 2, 3, n = 1, 1, 2, 3, and the single-factor evaluation matrix R is: r mn represents the membership degree that the evaluation factor u m belongs to the evaluation level v n , and 0 ≤ r mn ≤ 1, m is the number of the evaluation factor, and n is the number of the evaluation level;

[0022] Use the analytic hierarchy process to determine the weight a of each evaluation factor m , obtain the factor weight vector A = [a1, a2, a3], and Input the single-factor evaluation matrix R and the factor weight vector A; Calculate the comprehensive evaluation vector B through fuzzy transformation: where, represents the fuzzy composition operator;

[0023] Output the comprehensive evaluation vector B, B = [b1, b2, b3], respectively represent the comprehensive membership degrees that the construction quality belongs to each evaluation level; According to the maximum membership degree principle, select the level with the largest membership degree in the comprehensive evaluation vector B as the final evaluation level of the construction quality.

[0024] As a further method, the personnel status data specifically includes the personnel density ρ people ; The equipment operation status data specifically includes the number of equipment failures f equn and the equipment failure frequency f equp ; The environmental status data specifically includes the environmental precipitation envt 、Environmental wind speed env f ; Construct a BIM construction safety risk assessment model based on construction safety risk impact data:

[0025]

[0026] In the formula, BIMf is the BIM construction safety risk assessment factor, and e is the natural constant.

[0027] As a further method, combined with the BP neural network model, the construction safety assessment level is output. The specific analysis process is as follows: Obtain the training data set of the BP neural network model, normalize the input variables and output variables, and map them to the interval [0,1]; Determine that the number of neurons in the input layer of the BP neural network model is equal to the number of input variables. The input variable is the BIM construction safety risk assessment factor in the training data set, which is 1; The number of neurons in the output layer is equal to the number of categories of the construction safety assessment level. The number of categories of the construction safety assessment level in the training data set is 3, which are 1, 2, and 3, representing first level, second level, and third level respectively. Use one-hot encoding to convert it into a numerical vector. The first level is represented as [1,0,0], the second level is represented as [0,1,0], and the third level is represented as [0,0,1]; The number of neurons in the hidden layer is h; Randomly initialize the connection weights from the input layer to the hidden layer and from the hidden layer to the output layer, as well as the thresholds of the hidden layer and the output layer; Input the BIM construction safety risk assessment factor in the preprocessed training data set;

[0028] Use the Sigmoid activation function to calculate the output yc of the s-th neuron in the hidden layer s : In the formula, qz s is the connection weight from the input layer to the s-th neuron in the hidden layer, and cy s is the threshold of the s-th neuron in the hidden layer. s is the neuron number in the hidden layer, s = 1, 2, 3...h; Use the Sigmoid activation function to calculate the output oc of the t-th neuron in the output layer t : In the formula, b st is the connection weight from the s-th neuron in the hidden layer to the t-th neuron in the output layer, and d t is the threshold of the t-th neuron in the output layer. t is the neuron number in the output layer, t = 1, 2, 3; For the error e of the t-th neuron in the output layer t The calculation formula is: e t = t t - o t ; In the formula, t t is the expected output of the t-th neuron in the output layer, and o tis the actual output of the t-th neuron in the output layer; the connection weights and thresholds are updated using the gradient descent method until the error of the network is less than the accuracy threshold stored in the database or the maximum number of iterations is reached;

[0029] Input the preprocessed current BIM construction safety risk assessment factors into the trained BP neural network model; obtain the output vector of the output layer; according to the output vector, determine the construction safety assessment level using the principle of maximum membership degree.

[0030] As a further method, based on the construction progress assessment level, construction quality assessment level, and construction safety assessment level, issue a BIM project supervision risk warning. The specific analysis process is as follows: accumulate the construction progress assessment level, construction quality assessment level, and construction safety assessment level to obtain the BIM project supervision risk level; compare the BIM project supervision risk level with the BIM project supervision risk critical level stored in the database; if the BIM project supervision risk level is not higher than the BIM project supervision risk critical level, transmit the BIM project supervision risk level to the data center, and the data center sends a BIM project supervision safety reminder to the construction management control terminal; if the BIM project supervision risk level is higher than the BIM project supervision risk critical level, transmit the BIM project supervision risk level to the data center, the data center issues a BIM project supervision risk warning to the construction management control terminal, and display the construction progress assessment level, construction quality assessment level, and construction safety assessment level on the construction management control terminal.

[0031] The second aspect of the present invention provides a BIM-based project supervision and management system, including an initial BIM model acquisition module, an initial BIM model update module, a construction progress assessment level determination module, a construction quality assessment level determination module, a safety risk impact data analysis module, a construction safety assessment level matching module, and a supervision risk warning issuance module, where: the initial BIM model acquisition module is used to import engineering design drawings and material information traceability databases into professional BIM modeling software through format conversion technology, extract basic building structure and mechanical and electrical equipment information, and obtain an initial BIM model; the initial BIM model update module is used to introduce geographic information system data, obtain the construction site coordinates, and fuse the construction site coordinates with the initial BIM model through a coordinate matching algorithm to obtain a BIM model;

[0032] The construction progress evaluation level determination module is used to assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model, obtain progress evaluation factors, and determine the construction progress evaluation level; the construction quality evaluation level determination module is used to analyze the construction quality status based on the component attributes and quality monitoring data in the BIM model, and determine the construction quality evaluation level based on the fuzzy comprehensive evaluation model; the safety risk impact data analysis module is used to obtain construction safety risk impact data, analyze the construction safety risk impact data, and construct a BIM construction safety risk assessment model. The construction safety risk impact data specifically includes personnel status data, equipment operation status data, and environmental status data; the construction safety evaluation level matching module is used to output the construction safety evaluation level based on the output of the BIM construction safety risk assessment model and in combination with the BP neural network model; the supervision risk warning issuing module is used to issue a BIM project supervision risk warning based on the construction progress evaluation level, the construction quality evaluation level, and the construction safety evaluation level.

[0033] Compared with the prior art, the embodiments of the present invention at least have the following advantages or beneficial effects:

[0034] (1) By providing a project supervision and management method and system based on BIM, the present invention imports engineering design drawings into professional BIM modeling software, integrates basic information such as building structures and mechanical and electrical equipment, changes the situation where various types of information are scattered in different drawings and documents in the traditional mode, makes project information more concentrated and clear, introduces geographic information system data and performs coordinate matching and fusion, so that the BIM model is accurately corresponding to the actual geographical location of the construction site, provides an accurate spatial positioning basis for subsequent construction management, and can effectively avoid construction problems caused by geographical location deviation.

[0035] (2) Based on the evaluation levels in three aspects of construction progress, quality, and safety, the present invention can provide comprehensive and integrated information for project supervision and project management, enabling managers to grasp the project situation as a whole, make more scientific and reasonable decisions, and avoid management mistakes caused by only focusing on a single factor.

[0036] (3) By combining the BP neural network model and outputting the construction safety evaluation level, and including personnel status data, equipment operation status data, and environmental status data in the analysis scope, the present invention can comprehensively cover various influencing factors of construction safety risks. The BP neural network model has strong non-linear mapping ability and data learning ability, and can accurately output the prediction result of the safety risk probability according to historical data and current risk influencing factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on the following drawings without creative efforts.

[0038] Figure 1 It is a schematic flow diagram of the method steps of the present invention.

[0039] Figure 2 It is a schematic diagram of the connection of the system modules of the present invention. Specific embodiments

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0041] Refer to Figure 1 As shown, the first aspect of the present invention provides a BIM-based engineering supervision and management method, including: importing engineering design drawings and material information traceability databases into professional BIM modeling software through format conversion technology, and extracting basic building structure and mechanical and electrical equipment information to obtain an initial BIM model.

[0042] Introduce geographic information system data, obtain the coordinates of the construction site, and fuse the coordinates of the construction site with the initial BIM model through a coordinate matching algorithm to obtain a BIM model.

[0043] The specific analysis process is as follows: introduce geographic information system data to obtain the coordinates of the construction site (X GIS , Y GIS , Z GIS ); based on the coordinate matching algorithm, fuse the coordinates of the construction site with the initial BIM model to obtain the coordinates of the construction site in the BIM model (X BIM , Y BIM , Z BIM ), and the coordinate matching formula is:

[0044]

[0045] In the formula, X GIS is the abscissa of the construction site, Y GIS is the ordinate of the construction site, Z GIS is the vertical coordinate of the construction site, X BIM is the abscissa of the construction site in the BIM model, Y BIM is the ordinate of the construction site in the BIM model, and Z BIMis the vertical coordinate of the construction site in the BIM model, S x is the scaling factor in the x-axis direction, S y is the scaling factor in the y-axis direction, S z is the scaling factor in the z-axis direction, T x is the translation factor in the x-axis direction, T y is the translation factor in the y-axis direction, T z is the translation factor in the z-axis direction;

[0046] Fuse the coordinates of the construction site in the BIM model with the initial BIM model to obtain the BIM model.

[0047] GIS focuses on macroscopic geographical environment information, such as topography, distribution of surrounding buildings, transportation network, etc.; BIM focuses on the microscopic details of the building project itself, such as building structure, internal facilities, etc. After fusion, the macroscopic geographical background of the construction site can be intuitively presented in the BIM model. Supervisors can not only view the internal structure and construction details of the building, but also understand its relationship with the surrounding environment, which helps to grasp the overall situation of the project, such as judging the impact of the surrounding terrain on the construction site layout and material transportation route.

[0048] Through the coordinate matching algorithm, realize the accurate conversion and fusion of GIS coordinates and BIM model coordinates, and provide a unified and accurate coordinate system for the positioning of the construction site. This helps supervisors accurately determine the construction location. For example, during foundation construction, the pile position, foundation boundary, etc. can be accurately measured based on the fused coordinate information to ensure that the construction location meets the design requirements.

[0049] Combining geographical information and construction progress information in the BIM model, a more realistic construction progress simulation can be carried out. Considering the impact of the geographical environment on construction, simulate the progress under different construction plans, so as to optimize the construction plan. Supervisors can more accurately evaluate whether the construction progress is reasonable, timely discover potential progress delay risks and take measures.

[0050] Assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model, obtain progress evaluation factors, and determine the construction progress evaluation level.

[0051] The specific analysis process is as follows: Assign time attributes to each construction task in the BIM model, collect construction progress data, and obtain the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio of each construction task. Based on the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio of each construction task, construct a progress analysis model and output a progress evaluation factor. The progress evaluation factor serves as the analysis basis for determining the construction progress evaluation level. Obtain the pre-stored progress evaluation factor - construction progress evaluation level mapping table in the database, and by looking up the mapping table and according to the progress evaluation factor, find the matching construction progress evaluation level.

[0052] For the progress analysis model, the specific analysis process is as follows:

[0053]

[0054] In the formula, sv is the progress evaluation factor, finish is the number of completed construction tasks, ev i is the completion ratio of the i-th construction task, pv i is the planned completion ratio of the i-th construction task, i is the construction task number, i = 1, 2, 3,..., w, and w is the total number of construction tasks.

[0055] By assigning time attributes to the construction tasks in the BIM model and collecting multi-dimensional data such as the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio, the constructed progress analysis model can output accurate progress evaluation factors. This makes the construction progress evaluation no longer a vague qualitative judgment but a quantitative analysis based on specific data. For example, the supervision personnel can accurately know the deviation degree between the current project progress and the planned progress based on the progress evaluation factor, providing solid data support for decision-making and avoiding mistakes caused by relying solely on experience or subjective judgment.

[0056] The calculation of the progress evaluation factor takes into account the completion situation of each construction task and can be refined to the specific task level. This helps the supervision personnel to deeply understand the progress of each link in the project, find out which tasks are ahead or behind schedule, and thus take targeted measures.

[0057] Constructing a progress analysis model and determining the evaluation level by querying the pre-stored progress evaluation factor - construction progress evaluation level mapping table realizes the automation and standardization of the construction progress analysis. Compared with the traditional way of manually counting and analyzing the progress, it greatly reduces the labor and time costs and improves the work efficiency.

[0058] Analyze the construction quality status based on the component attributes and quality monitoring data in the BIM model, and determine the construction quality evaluation level based on the fuzzy comprehensive evaluation model.

[0059] The specific analysis process is: Get the actual strength of concrete f actual and concrete design strength f design , calculate the concrete strength quality factor fy:

[0060]

[0061] Get the appearance defect rate d of concrete components defect , calculate the appearance quality factor dy of the concrete component:

[0062]

[0063] In the formula, e is a natural constant;

[0064] Get the actual size L of the concrete component actual Design size L of concrete components design , calculate the concrete component size quality factor Ly;

[0065]

[0066] By calculating the concrete strength quality factor, appearance quality factor and size quality factor respectively, the quality of concrete components is quantitatively evaluated from multiple key dimensions. Not only the core performance indicator of concrete strength is taken into account, but also important factors affecting quality such as appearance defect rate and size deviation are covered. It can fully and accurately reflect the actual quality status of concrete components and avoid the one-sidedness of single indicator evaluation.

[0067] As an information carrier, the BIM model integrates various attribute information and quality monitoring data of concrete components. This enables all project participants to obtain and share quality-related information on the same platform, avoiding the problem of information silos and poor information transmission.

[0068] The specific analysis process is as follows: determine the fuzzy comprehensive evaluation factor set U, U = {u1, u2, u3}, u1 represents the concrete strength quality factor, u2 represents the concrete component appearance quality factor, and u3 represents the concrete component size quality factor; determine the construction quality evaluation level V, V = {v1, v2, v3}, v1 is the first level, v2 is the second level, and v3 is the third level; obtain the membership degree r of each evaluation factor stored in the database to each evaluation level mn , m=1,2,3, n=1,1,2,3, the single factor evaluation matrix R is: r mn Indicates the evaluation factor u m Belongs to the evaluation level v n The membership degree of , and 0≤r mn ≤1, m is the number of the evaluation factor, and n is the number of the evaluation level;

[0069] Use the analytic hierarchy process to determine the weight a of each evaluation factor m , the factor weight vector A = [a1, a2, a3], and Input the single-factor evaluation matrix R and the factor weight vector A; calculate the comprehensive evaluation vector B through fuzzy transformation: where represents the fuzzy composition operator;

[0070] Output the comprehensive evaluation vector B, B = [b1, b2, b3], respectively representing the comprehensive membership degrees of the construction quality belonging to each evaluation grade; according to the maximum membership degree principle, select the grade with the largest membership degree in the comprehensive evaluation vector B as the final evaluation grade of the construction quality.

[0071] In the evaluation of construction quality, the quality grades are not always clearly defined and there are many ambiguities. For example, the quality of concrete components may be between two grades and it is difficult to accurately define. The fuzzy comprehensive evaluation model can handle this uncertainty well by introducing the concept of membership degree.

[0072] The construction quality is affected by a variety of factors comprehensively, and the relationships between various factors are complex and difficult to describe with an accurate mathematical model. The fuzzy comprehensive evaluation model is based on the principles of fuzzy mathematics, can effectively handle these complex relationships, comprehensively consider multiple quality factors, and avoid evaluation biases caused by simplistic processing.

[0073] Clearly determine the evaluation factor set including the concrete strength quality factor, appearance quality factor, and dimensional quality factor, and evaluate the construction quality from multiple key dimensions. This covers the main aspects affecting the quality of concrete components, avoids the one-sidedness of evaluating quality based on a single factor, and can comprehensively and accurately reflect the actual level of construction quality.

[0074] According to the maximum membership degree principle, select the grade with the largest membership degree from the comprehensive evaluation vector as the final evaluation grade of the construction quality, providing a clear grade determination standard for the construction quality.

[0075] Analyze the data on the impact of construction safety risks. The data on the impact of construction safety risks specifically includes personnel status data, equipment operation status data, and environmental status data, and construct a BIM construction safety risk assessment model.

[0076] The specific analysis process is as follows: The personnel status data specifically includes the personnel density ρ people ; the equipment operation status data specifically includes the number of equipment failures f equn , the equipment failure frequency f equp ; the environmental status data specifically includes the environmental precipitation envt 、Ambient wind speed env f ; Construct a BIM construction safety risk assessment model based on construction safety risk impact data:

[0077]

[0078] In the formula, BIMf is the BIM construction safety risk assessment factor, and e is the natural constant.

[0079] Comprehensively consider data such as personnel status, equipment operation status, and environmental status, covering all key factors affecting construction safety risks. Personnel density affects the safety and operation efficiency of the construction site. The number of equipment failures and frequencies reflect the reliability of the equipment. Environmental factors such as environmental precipitation and wind speed have a direct impact on construction safety. By integrating these data, the construction safety risks can be evaluated from multiple dimensions, avoiding the one-sidedness of evaluation caused by only focusing on a single factor and more accurately reflecting the real safety risk status of the construction site.

[0080] Calculate the BIM construction safety risk assessment factor BIMf through a specific formula to quantify the construction safety risks. This quantification method gives a specific numerical measurement standard for safety risks and can more accurately reflect the risk level compared with traditional qualitative or fuzzy evaluations.

[0081] Based on the output of the BIM construction safety risk assessment model, combined with the BP neural network model, output the construction safety assessment level.

[0082] The specific analysis process is as follows: Obtain the training data set of the BP neural network model, perform normalization processing on the input variables and output variables, and map them to the interval [0,1]; Determine that the number of neurons in the input layer of the BP neural network model is equal to the number of input variables. The input variable is the BIM construction safety risk assessment factor in the training data set, which is 1; The number of neurons in the output layer is equal to the number of categories of the construction safety assessment level. The number of categories of the construction safety assessment level in the training data set is 3, which are 1, 2, and 3, representing first level, second level, and third level respectively. Use one-hot encoding to convert them into numerical vectors. The first level is represented as [1,0,0], the second level is represented as [0,1,0], and the third level is represented as [0,0,1]; The number of neurons in the hidden layer is h; Randomly initialize the connection weights from the input layer to the hidden layer and from the hidden layer to the output layer, as well as the thresholds of the hidden layer and the output layer; Input the BIM construction safety risk assessment factor in the preprocessed training data set;

[0083] Use the Sigmoid activation function to calculate the output yc of the s-th neuron in the hidden layer s : In the formula, qz s$c_{sy}$ is the connection weight from the input layer to the $s$-th neuron in the hidden layer, s $\theta_{s}$ is the threshold of the $s$-th neuron in the hidden layer, where $s$ is the neuron number in the hidden layer, $s = 1, 2, 3, \cdots, h$; the output $o_{ct}$ of the $t$-th neuron in the output layer is calculated using the Sigmoid activation function t : where $b$ st is the connection weight from the $s$-th neuron in the hidden layer to the $t$-th neuron in the output layer, $d$ t is the threshold of the $t$-th neuron in the output layer, where $t$ is the neuron number in the output layer, $t = 1, 2, 3$; for the $t$-th neuron in the output layer, the error $e_{t}$ t is calculated as: $e_{t}$ t $= t$ t $- o_{ct}$ t ; where $t$ t is the expected output of the $t$-th neuron in the output layer, and $o_{ct}$ t is the actual output of the $t$-th neuron in the output layer; the connection weights and thresholds are updated using the gradient descent method until the error of the network is less than the precision threshold stored in the database or the maximum number of iterations is reached;

[0084] Input the preprocessed current BIM construction safety risk assessment factors into the trained BP neural network model; obtain the output vector of the output layer; according to the output vector, determine the construction safety assessment level using the maximum membership degree principle.

[0085] The BP neural network model has a powerful non - linear mapping ability and can learn the complex relationship between BIM construction safety risk assessment factors and safety risk levels from a large amount of training data. Through learning and training on the input variables (BIM construction safety risk assessment factors), it can capture the hidden rules and features in the data, thus predicting the construction safety assessment level more accurately. Compared with simple linear models or empirical judgments, the BP neural network can handle the interactions between more complex safety risk influencing factors and improve the accuracy of the assessment.

[0086] The BIM construction safety risk assessment model has already integrated data from multiple aspects such as personnel, equipment, and environment to calculate risk assessment factors. On this basis, the BP neural network further processes these assessment factors and makes full use of the information of multi - source data. It can integrate the influence of different types of data, avoid the limitations brought by a single data source or simple calculations, making the assessment results more comprehensive and reliable, and being able to more truly reflect the safety status of the construction site.

[0087] Construction safety risks themselves have a certain degree of uncertainty and ambiguity. The BP neural network model can handle these uncertainties by learning a large amount of historical data. It can give corresponding safety assessment levels according to different combinations of risk assessment factors. Even when there is a certain amount of noise or incompleteness in the data, it can relatively stably output relatively reliable assessment results and adapt to the challenges brought by the complex and changeable construction environment. The construction safety assessment level output by the BP neural network model provides a clear and accurate reference for construction safety management. Corresponding safety management strategies and measures can be formulated according to different safety levels.

[0088] Based on the construction progress assessment level, construction quality assessment level, and construction safety assessment level, a BIM project supervision risk warning is issued. Based on the construction progress assessment level, construction quality assessment level, and construction safety assessment level, a BIM project supervision risk warning is issued.

[0089] The specific analysis process is as follows: The construction progress assessment level, construction quality assessment level, and construction safety assessment level are accumulated to obtain the BIM project supervision risk level; the BIM project supervision risk level is compared with the BIM project supervision risk critical level stored in the database; if the BIM project supervision risk level is not higher than the BIM project supervision risk critical level, the BIM project supervision risk level is transmitted to the data center, and the data center sends a BIM project supervision safety reminder to the construction management control terminal; if the BIM project supervision risk level is higher than the BIM project supervision risk critical level, the BIM project supervision risk level is transmitted to the data center, the data center sends a BIM project supervision risk warning to the construction management control terminal, and the construction progress assessment level, construction quality assessment level, and construction safety assessment level are displayed on the construction management control terminal.

[0090] This method accumulates the assessment levels of the three dimensions of construction progress, quality, and safety, which are crucial to project construction, to form the BIM project supervision risk level. It changes the limitation of only focusing on single - aspect risks in the past and can comprehensively reflect the overall risk situation of the project. Delayed construction progress may lead to increased costs and rising safety risks, and quality problems may cause rework, thus affecting progress and safety. Through this comprehensive assessment, potential hazards caused by ignoring risks in a certain dimension can be effectively avoided.

[0091] By comparing with the BIM project supervision risk critical level stored in the database, hierarchical early warning is implemented. When the risk level is higher than the critical level, a risk warning is issued, clearly informing that the project has a relatively high risk and immediate measures need to be taken. This hierarchical method can reasonably allocate resources and attention according to the severity of the risk, improving the efficiency of risk response. The display of risk warning information on the construction management control terminal facilitates the collaborative work of all parties involved in the project and improves the coordination and efficiency of project management.

[0092] Referring to Figure 2 As shown, the second aspect of the present invention provides a BIM-based project supervision and management system, including an initial BIM model acquisition module, an initial BIM model update module, a construction progress evaluation level determination module, a construction quality evaluation level determination module, a safety risk impact data analysis module, a construction safety evaluation level matching module, and a supervision risk warning issuance module.

[0093] The initial BIM model acquisition module is used to import engineering design drawings and material information traceability databases into professional BIM modeling software through format conversion technology, extract basic building structure and mechanical and electrical equipment information, and obtain an initial BIM model.

[0094] The initial BIM model update module is used to introduce geographic information system data, obtain the construction site coordinates, and fuse the construction site coordinates with the initial BIM model through a coordinate matching algorithm to obtain a BIM model.

[0095] The construction progress evaluation level determination module is used to assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model, obtain a progress evaluation factor, and determine the construction progress evaluation level.

[0096] The construction quality evaluation level determination module is used to analyze the construction quality status based on the component attributes and quality monitoring data in the BIM model, and determine the construction quality evaluation level based on a fuzzy comprehensive evaluation model.

[0097] The safety risk impact data analysis module is used to analyze the construction safety risk impact data, which specifically includes personnel status data, equipment operation status data, and environmental status data, and construct a BIM construction safety risk assessment model.

[0098] The construction safety evaluation level matching module is used to output the construction safety evaluation level based on the output of the BIM construction safety risk assessment model and in combination with a BP neural network model.

[0099] The supervision risk warning issuance module is used to issue a BIM project supervision risk warning based on the construction progress evaluation level, the construction quality evaluation level, and the construction safety evaluation level.

[0100] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. A BIM-based engineering supervision and management method, characterized in that, It includes the following steps: Import engineering design drawings and the material information traceability database into a professional BIM modeling software through format conversion technology, extract basic building structure and mechanical and electrical equipment information, and obtain an initial BIM model; Introduce geographic information system data, obtain the construction site coordinates, and fuse the construction site coordinates with the initial BIM model through a coordinate matching algorithm to obtain a BIM model; Assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model, obtain a progress evaluation factor, and determine the construction progress evaluation level; Analyze the construction quality status based on the component attributes and quality monitoring data in the BIM model, and determine the construction quality evaluation level based on a fuzzy comprehensive evaluation model; Obtain construction safety risk impact data, analyze the construction safety risk impact data, and construct a BIM construction safety risk assessment model. The construction safety risk impact data specifically includes personnel status data, equipment operation status data, and environmental status data; Based on the output of the BIM construction safety risk assessment model, combined with a BP neural network model, output the construction safety evaluation level; Based on the construction progress evaluation level, construction quality evaluation level, and construction safety evaluation level, issue a BIM project supervision risk warning.

2. The method for engineering supervision and management based on BIM according to claim 1, characterized in that: The specific analysis process of the step of introducing geographic information system data, obtaining the construction site coordinates, and fusing the construction site coordinates with the initial BIM model through a coordinate matching algorithm to obtain a BIM model is as follows: Introduce geographic information system data to obtain the coordinates (X GIS , Y GIS , Z GIS ) of the construction site; Based on the coordinate matching algorithm, the coordinates of the construction site are integrated with the initial BIM model to obtain the coordinates (X BIM , Y BIM , Z BIM ) of the construction site in the BIM model. The coordinate matching formula is as follows: Wherein, X GIS is the abscissa of the construction site, Y GIS is the ordinate of the construction site, Z GIS is the vertical coordinate of the construction site, X BIM is the abscissa of the construction site in the BIM model, Y BIM is the ordinate of the construction site in the BIM model, Z BIM is the vertical coordinate of the construction site in the BIM model, S x is the scaling factor in the x-axis direction, S y is the scaling factor in the y-axis direction, S z is the scaling factor in the z-axis direction, T x is the translation factor in the x-axis direction, T y is the translation factor in the y-axis direction, T z is the translation factor in the z-axis direction; Fuse the coordinates of the construction site in the BIM model with the initial BIM model to obtain a BIM model.

3. The method for engineering supervision and management based on BIM according to claim 1 is characterized in that: The specific analysis process of the step of assigning time attributes to each construction task in the BIM model, collecting construction progress data, constructing a progress analysis model, obtaining a progress evaluation factor, and determining the construction progress evaluation level is as follows: Assign time attributes to each construction task in the BIM model, collect construction progress data, and obtain the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio of each construction task; Based on the total number of construction tasks, the number of completed construction tasks, the completion ratio of each construction task, and the planned completion ratio of each construction task, construct a progress analysis model and output a progress evaluation factor. The progress evaluation factor is used as the analysis basis for determining the construction progress evaluation level; Obtain the progress evaluation factor - construction progress evaluation level mapping table pre-stored in the database, and through looking up the mapping table, find the matching construction progress evaluation level according to the progress evaluation factor.

4. The method for engineering supervision and management based on BIM according to claim 3, characterized in that: The specific analysis process of the progress analysis model is as follows: where sv is the progress evaluation factor, finish is the number of construction tasks completed, ev i is the completion ratio of the i-th construction task, pv i is the planned completion ratio of the i-th construction task, i is the construction task number, i = 1, 2, 3,..., w, and w is the total number of construction tasks.

5. A BIM-based project supervision and management method according to claim 1, characterized in that: The specific analysis process of analyzing the construction quality status based on the component attributes and quality monitoring data in the BIM model is as follows: Obtain the actual strength f of the concrete actual and the designed strength f of the concrete design , and calculate the concrete strength quality factor fy: Obtain the appearance defect rate d of the concrete component defect , and calculate the appearance quality factor dy of the concrete component: In the formula, e is the natural constant; Obtain the actual size L of the concrete component actual and the designed size L of the concrete component design , and calculate the size quality factor Ly of the concrete component; 6. The method for engineering supervision and management based on BIM according to claim 5, characterized in that: The specific analysis process of determining the construction quality evaluation level based on the fuzzy comprehensive evaluation model is as follows: Determine the fuzzy comprehensive evaluation factor set U, U = {u1, u2, u3}, where u1 represents the concrete strength quality factor, u2 represents the concrete component appearance quality factor, and u3 represents the concrete component size quality factor; Determine the construction quality evaluation level V, where V = {v1, v2, v3}, v1 is the first level, v2 is the second level, and v3 is the third level; Obtain the membership degree \(r\) of each evaluation factor stored in the database for each evaluation level mn , where \(m = 1, 2, 3\), \(n = 1, 1, 2, 3\), and the single-factor evaluation matrix \(R\) is as follows: r mn represents the membership degree of the evaluation factor u m belonging to the evaluation level v n , and 0 ≤ r mn ≤ 1, where m is the number of the evaluation factor and n is the number of the evaluation level; Determine the weight a of each evaluation factor by using the analytic hierarchy process m , obtain the factor weight vector A = [a1, a2, a3], and Input the single-factor evaluation matrix R and the factor weight vector A; Calculate the comprehensive evaluation vector B through fuzzy transformation: Among them, represents a fuzzy composition operator; Output the comprehensive evaluation vector B, B = [b1, b2, b3], which respectively represent the comprehensive membership degrees of the construction quality belonging to each evaluation level; According to the principle of maximum membership degree, select the level with the largest membership degree in the comprehensive evaluation vector B as the final evaluation level of the construction quality.

7. A BIM-based project supervision and management method according to claim 1, characterized in that: The personnel status data specifically includes the personnel density ρ people ; The device operation status data specifically includes the number of device failures f equn , the device failure frequency f equp ; The environmental status data specifically includes environmental precipitation env t , environmental wind speed env f ; Construct a BIM construction safety risk assessment model based on the construction safety risk impact data: In the formula, BIMf is the BIM construction safety risk assessment factor, and e is the natural constant.

8. The method for engineering supervision and management based on BIM according to claim 7, characterized in that: Combined with the BP neural network model, output the construction safety assessment level. The specific analysis process is as follows: Obtain the training data set of the BP neural network model, normalize the input variables and output variables, and map them to the interval [0, 1]; Determine that the number of neurons in the input layer of the BP neural network model is equal to the number of input variables. The input variable is the BIM construction safety risk assessment factor in the training data set, which is 1; The number of neurons in the output layer is equal to the number of categories of the construction safety assessment level. The number of categories of the construction safety assessment level in the training data set is 3, which are 1, 2, and 3, representing the first level, the second level, and the third level respectively. Use one-hot encoding to convert them into numerical vectors. The first level is represented as [1, 0, 0], the second level is represented as [0, 1, 0], and the third level is represented as [0, 0, 1]; The number of neurons in the hidden layer is h; Randomly initialize the connection weights from the input layer to the hidden layer and from the hidden layer to the output layer, as well as the thresholds of the hidden layer and the output layer; Input the BIM construction safety risk assessment factor in the preprocessed training data set; Calculate the output yc of the s-th neuron in the hidden layer using the Sigmoid activation function s : wherein, qz s is the connection weight from the input layer to the s-th neuron in the hidden layer, and cy s is the threshold of the s-th neuron in the hidden layer, s is the neuron number in the hidden layer, and s = 1, 2, 3... h; Calculate the output $o_c$ of the $t$-th neuron in the output layer using the Sigmoid activation function t : where b st is the connection weight from the s-th neuron in the hidden layer to the t-th neuron in the output layer, and d t is the threshold of the t-th neuron in the output layer, t is the neuron number in the output layer, and t = 1, 2, 3; For the error e of the t-th neuron in the output layer t The calculation formula is as follows: e t = t t - o t ; where t t is the expected output of the t-th neuron in the output layer, and o t is the actual output of the t-th neuron in the output layer; Use the gradient descent method to update the connection weights and thresholds until the error of the network is less than the precision threshold stored in the database or reaches the maximum number of iterations; Input the preprocessed current BIM construction safety risk assessment factor into the trained BP neural network model; Obtain the output vector of the output layer; According to the output vector, determine the construction safety assessment level using the principle of maximum membership degree.

9. The method for engineering supervision and management based on BIM according to claim 1, characterized in that: Based on the construction progress assessment level, construction quality assessment level, and construction safety assessment level, issue a BIM project supervision risk warning. The specific analysis process is as follows: Accumulate the construction progress assessment level, construction quality assessment level, and construction safety assessment level to obtain the BIM project supervision risk level; Compare the BIM project supervision risk level with the BIM project supervision risk critical level stored in the database; If the BIM project supervision risk level is not higher than the BIM project supervision risk critical level, transmit the BIM project supervision risk level to the data center, and the data center sends a BIM project supervision safety reminder to the construction management control terminal; If the BIM project supervision risk level is higher than the BIM project supervision risk critical level, transmit the BIM project supervision risk level to the data center, the data center sends a BIM project supervision risk warning to the construction management control terminal, and display the construction progress assessment level, construction quality assessment level, and construction safety assessment level on the construction management control terminal.

10. A BIM-based project supervision and management system applied to the BIM-based project supervision and management method according to any one of claims 1-9, characterized in that, It includes an initial BIM model acquisition module, an initial BIM model update module, a construction progress assessment level determination module, a construction quality assessment level determination module, a safety risk impact data analysis module, a construction safety assessment level matching module, and a supervision risk warning issuance module, where: The initial BIM model acquisition module is used to import engineering design drawings and a material information traceability database into professional BIM modeling software through format conversion technology, extract basic building structure and mechanical and electrical equipment information, and obtain an initial BIM model; The initial BIM model update module is used to introduce geographic information system data, obtain the coordinates of the construction site, and fuse the coordinates of the construction site with the initial BIM model through a coordinate matching algorithm to obtain a BIM model; The construction progress assessment level determination module is used to assign time attributes to each construction task in the BIM model, collect construction progress data, construct a progress analysis model, obtain a progress assessment factor, and determine the construction progress assessment level; The construction quality assessment level determination module is used to analyze the construction quality status based on the component attributes and quality monitoring data in the BIM model, and determine the construction quality assessment level based on a fuzzy comprehensive evaluation model; The safety risk impact data analysis module is used to obtain construction safety risk impact data, analyze the construction safety risk impact data, and construct a BIM construction safety risk assessment model. The construction safety risk impact data specifically includes personnel status data, equipment operation status data, and environmental status data; The construction safety assessment level matching module is used to output the construction safety assessment level based on the output of the BIM construction safety risk assessment model and in combination with a BP neural network model; The supervision risk warning issuance module is used to issue a BIM project supervision risk warning based on the construction progress assessment level, the construction quality assessment level, and the construction safety assessment level.

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