Tunnel construction progress dynamic management method and system

Abstract

The invention provides a tunnel construction progress dynamic management method and system, and the method comprises the steps: obtaining a key construction path with the longest construction period in a plurality of project construction paths in a tunnel construction project; inputting the rate influence factor data of each sub-construction project into a pre-trained tunnel construction rate prediction model, outputting a construction rate prediction value of each sub-construction project, and dividing the workload of each sub-construction project by the corresponding construction rate prediction value to obtain a predicted construction period of each sub-construction project, adding to obtain a tunnel predicted construction period of the tunnel construction project and determining a construction plan date; dynamically setting a plurality of monitoring time points according to supervision requirements of a tunnel construction project, and calculating a current progress deviation rate and a dynamic target construction period deviation rate of each monitoring time point to obtain a construction period delay index; and judging the construction progress of the tunnel construction project at each monitoring time point according to the construction period delay index, and performing prompting and early warning so as to guide and adjust the subsequent construction progress of the tunnel construction project.

Description

Technical Field

[0001] The present invention relates to the technical field of progress management, and in particular to a method and system for dynamic management of tunnel construction progress. Background Art

[0002] Railway construction projects are characterized by high costs, large investment requirements, long lines, many construction parties, strict quality standards, long construction periods, cumbersome procedures, complex external coordination, and a lot of information processing. Therefore, continuous and dynamic regulation is essential during their construction. With the increasing complexity of the construction environment and the continuous expansion of project scale, the traditional management strategy of construction plans can no longer meet the needs of advanced project management. Effectively implementing design goals, reducing project risks, promoting efficient information flow, and ensuring the accuracy of management are the main issues that need to be addressed. Among the three major goals of engineering project management, the optimization of schedule management is of particular importance to improving management quality.

[0003] In the existing technology, the research on construction progress prediction and optimization has achieved rich results, involving project management, data analysis and information technology. These studies mainly focus on two aspects: construction period prediction and progress control. Construction period prediction mainly relies on preliminary project planning and in-depth analysis of uncertain elements in the project to determine the overall expected construction period of the project, while progress control focuses more on optimizing the schedule and resource allocation by using historical data and analyzing key construction paths during the project construction phase. Current progress control research often does not fully consider the dynamic changes in the actual construction process, which leads to large deviations in the allocation of resources and progress planning during actual implementation. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and system for dynamically managing the progress of tunnel construction to eliminate or improve one or more defects existing in the prior art, and solves the problem that the prior art cannot dynamically manage the progress of tunnel construction and adjust the schedule.

[0005] One aspect of the present invention provides a method for dynamic management of tunnel construction progress, the method comprising the following steps:

[0006] Obtaining a key construction path in a tunnel construction project, wherein the key construction path is the one with the longest construction period among multiple project construction paths, and the project construction path is obtained by arranging multiple sub-construction projects in order of construction sequence;

[0007] Based on the pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, the rate influencing factor data of each sub-construction project in the key construction path is obtained, the rate influencing factor data of each sub-construction project is input into the pre-trained tunnel construction rate prediction model and the construction rate prediction value of each sub-construction project is output; the tunnel construction rate prediction model is obtained based on the back propagation neural network model training; the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, the sum is added to obtain the tunnel predicted construction period of the tunnel construction project, and the construction plan date is determined;

[0008] Dynamically set multiple monitoring time points according to the supervision requirements of the tunnel construction project, calculate the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point, and obtain the construction period delay index according to the current progress deviation rate and the dynamic target construction period deviation rate;

[0009] The construction progress of the tunnel construction project at each monitoring time point is judged according to the construction delay index, and prompts and warnings are given to guide the adjustment of the subsequent construction progress of the tunnel construction project.

[0010] In some embodiments, the tunnel construction rate prediction model is trained using the following method:

[0011] A training sample set is obtained, wherein the training sample set includes a plurality of samples, each sample includes rate influencing factor data collected for a single sub-construction project, and a corresponding construction rate prediction value is added as a label; the rate influencing factor data is collected from a pre-established indicator system for personnel management, material reserves, process methods, environmental conditions, and mechanical equipment;

[0012] Obtaining an initial neural network model, wherein the initial neural network model takes the rate influencing factor data in each sample as input and takes the construction rate prediction value as output; the initial neural network model adopts a back propagation neural network model; the back propagation neural network model includes an input layer, a hidden layer and an output layer that are continuously arranged;

[0013] The construction rate prediction value of each sample is compared with the label and a loss function is constructed. The parameters of the initial neural network model are iteratively updated with the goal of minimizing the loss function, and the updated initial neural network model is constructed as a tunnel construction rate prediction model.

[0014] In some embodiments, the method further comprises:

[0015] The hidden layer performs weighted summation on the rate influencing factor data in each sample and then uses an activation function to process the data to obtain the construction rate prediction value of each sample.

[0016] In some embodiments, dynamically setting multiple monitoring time points according to the supervision requirements of the tunnel construction project includes:

[0017] According to the time interval supervision requirements of the tunnel construction project, monitoring time points are set at time intervals of weeks, half months and months.

[0018] In some embodiments, the expression for calculating the current progress deviation rate at each monitoring time point is:

[0019]

[0020] Among them, T mi represents the actual construction date from the start of the tunnel construction project to the monitoring time point, T li Indicates the planned construction date from the start of the tunnel construction project to the monitoring time point.

[0021] In some embodiments, the expression for calculating the dynamic target duration deviation rate at each monitoring time point is:

[0022]

[0023] Among them, T i represents the predicted construction period at the monitoring time point, T 0 Indicates the planned construction period at the monitoring time point.

[0024] In some embodiments, the expression for obtaining the construction period delay index according to the current progress deviation rate and the dynamic target construction period deviation rate is:

[0025] e Ti =η 1 ×α i +η 2 ×β i ;

[0026] Among them, η 1 represents the weight of the current progress deviation rate, η 2 Represents the weight of the dynamic target duration deviation rate.

[0027] In some embodiments, judging the construction progress of the tunnel construction project at each monitoring time point according to the construction delay index, and providing prompts and warnings includes:

[0028] According to the difficulty of later control of the tunnel construction project, multiple level interval boundaries of the construction delay index are determined, and at least four level intervals are divided using the level interval boundaries; each level interval corresponds to an extremely difficult control warning requiring a severe warning, a difficult control warning requiring a moderate warning, a relatively difficult control warning requiring a slight warning, and an easy control warning requiring no warning.

[0029] Another aspect of the present invention provides a tunnel construction progress dynamic management system, the system is used to execute the above tunnel construction progress dynamic management method, the system comprises:

[0030] A critical construction path analysis module is used to obtain a critical construction path in a tunnel construction project. The critical construction path is the one with the longest construction period among multiple project construction paths. The project construction path is obtained by arranging multiple sub-construction projects in order of construction sequence.

[0031] A construction period prediction module is used to obtain the rate influencing factor data of each sub-construction project in the key construction path based on a pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, input the rate influencing factor data of each sub-construction project into a pre-trained tunnel construction rate prediction model and output a construction rate prediction value of each sub-construction project; the tunnel construction rate prediction model is obtained by training based on a back propagation neural network model; the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, the sum is added to obtain the tunnel predicted construction period of the tunnel construction project, and the construction plan date is determined;

[0032] A construction delay index calculation module is used to dynamically set multiple monitoring time points according to the supervision requirements of the tunnel construction project, calculate the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point, and obtain the construction delay index according to the current progress deviation rate and the dynamic target construction period deviation rate;

[0033] The construction progress warning and adjustment module is used to determine the construction progress of the tunnel construction project at each monitoring time point according to the construction period delay index, and to provide prompts and warnings to guide the adjustment of the subsequent construction progress of the tunnel construction project.

[0034] In some embodiments, the system further comprises:

[0035] The cloud storage module is used to store the key construction path, the predicted construction rate value of each sub-construction project, the predicted construction period of each sub-construction project, the predicted tunnel construction period and the construction period delay index.

[0036] The beneficial effects of the present invention are at least:

[0037] In the tunnel construction progress dynamic management method and system of the present invention, the key construction path of the tunnel construction project is analyzed and obtained to provide time and progress control for the dynamic control of the construction period. The key construction path is the path with the longest construction period among multiple project construction paths, which determines the shortest completion time of the tunnel construction project. Identifying the key construction path is conducive to fine management of the tunnel construction project; the rate influencing factor data of each sub-construction project is input into a pre-trained tunnel construction rate prediction model and the construction rate prediction value of each sub-construction project is output, and the predicted construction period of each sub-construction project is obtained by dividing the workload of each sub-construction project by the corresponding construction rate prediction value, which is conducive to adjusting resource allocation according to actual construction conditions to shorten the construction period and reduce costs; the construction period delay index is calculated according to the current progress deviation rate and the dynamic target construction period deviation rate to monitor the construction progress and actual completion of the tunnel construction project; the progress of the tunnel construction project at the monitoring time point is obtained according to the construction period delay index, and prompts and warnings are given to adjust the subsequent construction progress of the tunnel construction project, so as to realize dynamic management of the construction progress.

[0038] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0039] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0041] Figure 1 The figure is a flow chart of a method for dynamic management of tunnel construction progress according to an embodiment of the present invention.

[0042] Figure 2 It is a schematic diagram of the structure of the tunnel construction rate prediction model described in one embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0044] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0045] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0046] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0047] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0048] In the prior art, the research on construction progress prediction and optimization has achieved rich results, involving project management, data analysis and information technology. These studies mainly focus on two aspects: construction period prediction and progress control. Construction period prediction mainly relies on preliminary project planning and in-depth analysis of uncertain elements in the project to determine the overall expected construction period of the project, while progress control focuses more on optimizing the progress plan and resource allocation by using historical data and analyzing key construction paths during the project construction phase. Current progress control research often does not fully consider the dynamic changes in the actual construction process, which leads to large deviations in the allocation of resources and progress plans during actual execution. The present invention proposes a method and system for dynamic management of tunnel construction progress, which obtains the key construction path with the longest construction period among multiple project construction paths in a tunnel construction project; based on pre-established personnel management, material reserves, process methods, environmental conditions and mechanical equipment The index system obtains the rate influencing factor data of each sub-construction project in the key construction path, inputs the rate influencing factor data of each sub-construction project into the pre-trained tunnel construction rate prediction model and outputs the construction rate prediction value of each sub-construction project; the tunnel construction rate prediction model is obtained based on the training of the back propagation neural network model; the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, and the predicted construction period of the tunnel construction project is obtained by adding them together, and the construction plan date is determined; according to the supervision needs of the tunnel construction project, multiple monitoring time points are dynamically set, the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point are calculated, and the construction period delay index used to judge the construction progress of the tunnel construction project at each monitoring time point is obtained, and the subsequent construction progress of the tunnel construction project is adjusted according to the prompts and early warning guidance.

[0049] Figure 1 The figure is a flow chart of a method for dynamic management of tunnel construction progress according to an embodiment of the present invention. Specifically, the present application provides a method for dynamic management of tunnel construction progress, which includes the following steps S101 to S104:

[0050] Step S101: Acquire a critical construction path in a tunnel construction project. The critical construction path is the one with the longest construction period among multiple project construction paths. The project construction path is obtained by arranging multiple sub-construction projects in order of construction sequence.

[0051] Step S102: Based on the pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, the rate influencing factor data of each sub-construction project in the key construction path is obtained, the rate influencing factor data of each sub-construction project is input into the pre-trained tunnel construction rate prediction model and the construction rate prediction value of each sub-construction project is output; the tunnel construction rate prediction model is obtained based on the back propagation neural network model training; the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, the sum is added to obtain the tunnel predicted construction period of the tunnel construction project, and the construction plan date is determined.

[0052] Step S103: Dynamically set multiple monitoring time points according to the supervision requirements of the tunnel construction project, calculate the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point, and obtain the construction period delay index based on the current progress deviation rate and the dynamic target construction period deviation rate.

[0053] Step S104: judging the construction progress of the tunnel construction project at each monitoring time point according to the construction delay index, and providing prompts and warnings to guide the adjustment of the subsequent construction progress of the tunnel construction project.

[0054] In step S101, according to the construction time parameters of each sub-construction project in the tunnel construction project, a sub-construction project with a total time difference and a free time difference of zero is obtained as a key task, and the key tasks are arranged in order of construction to obtain a key construction path. The key construction path is the path with the longest construction period among multiple project construction paths. The shortest completion time of the tunnel construction project is determined based on the key construction path. Identifying the key construction path is conducive to the refined management of the tunnel construction project to ensure that the tunnel construction project can be executed as planned.

[0055] In step S102, a construction rate prediction value of each sub-construction project is obtained according to the tunnel construction rate prediction model, and the predicted tunnel construction time is obtained by calculating the predicted value with the task workload in the key construction path. Figure 2Schematic diagram of the structure of the tunnel construction rate prediction model according to an embodiment of the present invention. In some embodiments, the training process of the tunnel construction rate prediction model includes steps S1021 to S1023:

[0056] Step S1021: Obtain a training sample set, which includes multiple samples. Each sample contains rate influencing factor data collected for a single sub-construction project, and adds a corresponding construction rate prediction value as a label; the rate influencing factor data is collected from a pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment.

[0057] Step S1022: Obtain an initial neural network model, which takes the rate influencing factor data in each sample as input and the construction rate prediction value as output; the initial neural network model adopts a back propagation neural network model; the back propagation neural network model includes a continuously set input layer, hidden layer and output layer.

[0058] Step S1023: Compare the predicted construction rate value of each sample with the label and construct a loss function, iteratively update the parameters of the initial neural network model with the goal of minimizing the loss function, and construct the updated initial neural network model as a tunnel construction rate prediction model.

[0059] Specifically, data related to progress management are obtained based on the actual construction data of the railway engineering management platform, and the rate influencing factor data with validity and accuracy are selected as samples of the input layer according to the factors affecting the progress of tunnel construction; the samples include the rate influencing factor data collected for a single sub-construction project. Table 1 is a data table of rate influencing factors. The personnel management index system collects data on the performance of key management personnel, the presence of management personnel, the presence of operating personnel, average monthly indicators, construction site environmental index, hidden danger investigation and treatment data, safety risk control data and geological risk control data; the material reserve index system collects data on the presence of main materials, data on unqualified materials and investment data for the month; the process and method index system collects the planned rate, planned construction period (speed), construction days, number of design changes and construction efficiency under construction conditions; the environmental condition index system collects data on the influence of temperature, precipitation, high ground stress, poor geology and weak surrounding rock, geological forecast risk rate and environmental protection factors; the mechanical equipment index system collects the actual use of machinery. After the samples are input into the initial neural network model, the construction rate prediction value of each sub-construction project is output through hidden layer data processing; in some embodiments, the method for dynamic management of tunnel construction progress also includes: the hidden layer performs weighted summation on the rate influencing factor data in each sample and then uses activation function processing to obtain the construction rate prediction value of each sample.

[0060] Table 1 Rate influencing factors data table

[0061]

[0062] Table 1 Rate influencing factors data table

[0063]

[0064] Furthermore, the initial neural network model adopts the back propagation (BP) neural network model. The back propagation neural network model includes an input layer, a hidden layer, and an output layer. After the sample enters the input layer, it is multiplied by the corresponding weight and then added and input into the activation function to calculate the output result. The output prediction result is compared with the actual result. According to the error generated, the reverse transmission is performed and the weight is adjusted through multiple iterations of the gradient descent method. The loss function is constructed by comparing the construction rate prediction value and the label. The weight is adjusted multiple times by minimizing the loss function to reduce the error. The input layer contains input layer neurons, the hidden layer contains hidden layer neurons, and the data passing through the neurons must be processed by the activation function. The activation function includes the Sigmoid function, the Tanh function, the ReLU function, and the Softmax function; the hidden layer neurons X c1 , X c2 , ..., X ck For the data X in the training sample set 1 , X 2 , X 3 , ..., X 23 The weighted W calculation is performed and the construction rate prediction value is output after being processed by the activation function.

[0065] In step S103 and step S104, multiple monitoring time points are dynamically set according to the supervision requirements of the tunnel construction project, and the monitoring time points are set in various time units such as weeks and months. In some embodiments, dynamically setting multiple monitoring time points according to the supervision requirements of the tunnel construction project includes: setting monitoring time points in time intervals of weeks, half months and months according to the time interval supervision requirements of the tunnel construction project.

[0066] Specifically, the monitoring period lengths reserved between two monitoring time points according to the time interval between the monitoring time points include one week, half a month and one month. The monitoring time points are set on the key construction paths to predict the breakthrough time of the entire tunnel construction project. The real-time changes in the construction progress are monitored through the monitoring data at the monitoring time points, and the progress of the tunnel construction project is adjusted according to the actual completion of the construction at the monitoring time points.

[0067] In some embodiments, the expression for calculating the current progress deviation rate at each monitoring time point is:

[0068]

[0069] Among them, T mi represents the actual construction date from the start of the tunnel construction project to the monitoring time point, T li Indicates the planned construction date from the start of the tunnel construction project to the monitoring time point.

[0070] In some embodiments, the expression for calculating the dynamic target duration deviation rate at each monitoring time point is:

[0071]

[0072] Among them, T i represents the predicted construction period at the monitoring time point, T 0 Indicates the planned construction duration at the monitoring time point.

[0073] Specifically, the current progress deviation rate is used to judge the current construction progress delay of the tunnel construction project and determine the difficulty of the subsequent construction projects; when α>0, α i >α i-1 Indicates that the construction progress is delayed and the risk is accelerated. i =α i-1 Indicates that the construction progress delay remains unchanged, α i <α i-1 It indicates that the construction progress delay is effectively controlled and the risk is reduced; α<0 indicates that the construction progress is advanced and the probability of completion of the tunnel construction project increases; the current progress deviation rate describes the time delay status before the current monitoring time point and the time delay trend around the current monitoring time point, which is conducive to showing the delay status of the entire project; α i Represents α i-1 The current progress deviation rate at the next monitoring time point.

[0074] In some embodiments, the expression for calculating the construction period delay index according to the current progress deviation rate and the dynamic target construction period deviation rate is:

[0075] e Ti =η 1 ×α i +η 2 ×β i ;

[0076] Among them, η 1 Represents the weight of the current progress deviation rate, η 2 Represents the weight of the dynamic target duration deviation rate.

[0077] Specifically, η 1 With η 2The sum of is 1; based on the preset tunnel progress management standard, the construction progress of each monitoring point is judged according to the construction delay index calculated by the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point. The construction period of the tunnel construction project is affected by many factors and will change during the construction process. By establishing an effective monitoring system to monitor the real-time changes in the construction progress, the progress of the construction project can be dynamically adjusted and controlled in a timely manner; the progress of the tunnel construction project at the current monitoring time point is obtained according to the construction period delay index, and a progress warning is issued according to the level of the tunnel construction project progress.

[0078] In some embodiments, the progress warning of the tunnel construction project at the monitoring time point obtained according to the construction period delay index includes an extremely difficult control warning requiring a severe warning, a difficult control warning requiring a moderate warning, a relatively difficult control warning requiring a slight warning, and an easy control warning requiring no warning. Table 2 is a standard table for tunnel progress management. The level interval boundary value ε is set according to the actual construction situation of the tunnel construction project 1 , ε 3 and ε 3 The construction progress is divided into different levels according to the level interval boundary value. The levels are divided into controllable working surface, basic controllable working surface requiring slight warning, difficult controllable working surface and uncontrollable working surface according to the control difficulty of the construction progress from easy to difficult. When e>ε 3 It is an uncontrollable operation surface when ε 2 <e≤ε 3 It is a difficult to control working surface when ε 1 <e≤ε 2 It belongs to the basic controllable working surface when e<ε 1 It is a controllable working surface.

[0079] Table 2 Tunnel construction progress management standard table

[0080] Indicator grade Difficulty of post-control describe <![CDATA[e>ε 3 ]]> Level IV Very difficult Serious warning, unacceptable, avoid or take measures to reduce the level <![CDATA[ε 2 <e≤ε 3 ]]> Grade III difficulty China police, no hope, strengthen monitoring and handling <![CDATA[ε 1 <e≤ε 2 ]]> Level II Difficult Light warning, acceptable, monitored <![CDATA[e<ε 1 ]]> Level I easy No alarm, can be ignored

[0081] On the other hand, the present invention also provides a tunnel construction progress dynamic management system, which is used to execute the above-mentioned tunnel construction progress dynamic management method, and the system includes:

[0082] The critical construction path analysis module is used to obtain the critical construction path in the tunnel construction project. The critical construction path is the one with the longest construction period among multiple project construction paths. The project construction path is obtained by arranging multiple sub-construction projects in order of construction sequence.

[0083] The construction period prediction module is used to obtain the rate influencing factor data of each sub-construction project in the key construction path based on the pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, input the rate influencing factor data of each sub-construction project into the pre-trained tunnel construction rate prediction model and output the construction rate prediction value of each sub-construction project; the tunnel construction rate prediction model is obtained by training based on the back propagation neural network model. The workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, and the sum is added to obtain the tunnel predicted construction period of the tunnel construction project, and the construction plan date is determined.

[0084] The construction delay index calculation module is used to dynamically set multiple monitoring time points according to the supervision requirements of the tunnel construction project, calculate the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point, and obtain the construction delay index based on the current progress deviation rate and the dynamic target construction period deviation rate.

[0085] The construction progress warning and adjustment module is used to determine the construction progress of the tunnel construction project at each monitoring time point according to the construction delay index, and to provide prompts and warnings to guide the adjustment of the subsequent construction progress of the tunnel construction project.

[0086] In some embodiments, the system further comprises:

[0087] The cloud storage module is used to store the key construction paths, the predicted construction rate value of each sub-construction project, the predicted construction period of each sub-construction project, the predicted construction period of the tunnel and the construction period delay index.

[0088] The present invention is described below in conjunction with a specific embodiment:

[0089] The present invention provides a method and system for dynamic management of tunnel construction, which aims to combine the data and existing specifications in the railway engineering management platform, comprehensively identify and dynamically track the construction progress by studying and analyzing the factors affecting the progress of tunnel construction, such as personnel management, material reserves, process methods, environmental conditions and mechanical equipment, and build a coupling relationship between multiple factors such as man-machine-material-method loop and the construction period through BP neural network, and establish a tunnel construction rate prediction model; propose a dynamic control method for construction progress based on key construction path constraints, and verify it by taking a railway tunnel in the project as an example. Continuously improve the optimal configuration of on-site resources under the conditions of the construction period, improve the construction efficiency of different sections and work areas under the premise of ensuring quality, quantity and safety, promote different construction companies to form more advanced construction concepts and management models, and realize dynamic management of tunnel construction progress.

[0090] Establish a tunnel construction rate prediction model. Based on the pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, obtain the impact of rate influencing factor data on the construction period of each sub-construction project in the key construction path; first analyze the rate influencing factor data based on text mining; then use the BP neural network to analyze and learn the existing data, calculate the impact of different rate influencing factor data on the construction period, and predict the construction time required to complete the construction plan for a specific period under different influencing conditions. Through this model, control the progress of a certain working face of the tunnel construction project and the overall progress of the tunnel construction project, and understand the impact of resource allocation on the construction progress, providing a basis for timely updating the progress plan to control the tunnel breakthrough time.

[0091] The progress of tunnel construction projects is managed through critical construction paths. First, the existing preliminary project data is obtained, including multiple sub-construction projects in the tunnel construction project and the time influencing parameters of each sub-construction project. Then, the critical construction path is obtained to provide time and progress constraints for the dynamic control of the construction period. The critical construction path refers to the path in the project that has the greatest impact on the total construction period and determines the shortest completion time of the tunnel construction project. Identifying the critical construction path is conducive to the refined management of the construction progress.

[0092] The time and progress constraints provided by the critical construction path are combined with the calculation results of the tunnel construction rate prediction model to achieve construction period prediction and optimal resource allocation. The expression is:

[0093]

[0094] The tunnel construction period is calculated by the workload of key tasks in the key construction path and the construction rate predicted by the tunnel construction rate prediction model, and the time of tunnel breakthrough is obtained to achieve reasonable arrangement of construction activities. This method helps to adjust resource allocation according to actual construction conditions to achieve the purpose of shortening construction period and reducing costs.

[0095] 1. Filter out data related to progress management through the railway engineering management platform, and obtain the rate influencing factor data of each sub-construction project in the key construction path based on the pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment.

[0096] 2. Construct a tunnel construction rate prediction model based on BP neural network.

[0097] (1) The railway engineering management platform collects and sets the types of input layer data and their numerical limits.

[0098] (2) The model parameters of the input layer data were preprocessed to form a training sample set for duration rate prediction.

[0099] (3) The code for the initial neural network model was written using Matlab software.

[0100] (4) The training sample set for duration prediction is imported and iterative calculation is performed.

[0101] (5) Analyze the calculation results and select the weight with the smallest error to construct a tunnel construction rate prediction model; obtain the prediction result of the tunnel construction rate by inputting the data sample to be predicted.

[0102] (6) Compare and analyze whether the accuracy of the forecast data has improved over time; compare and analyze the accuracy of forecasts based on weekly, biweekly, and monthly data collection, and select a reasonable time for data collection.

[0103] The training samples required for the tunnel construction rate prediction model include 23 variables X that may affect the construction progress, and an output variable construction rate prediction value; the selected input training samples will be different according to different construction locations and work processes. The input training samples are selected based on the actual construction data recorded by the management platform, and the influence of the factors affecting the progress on the specific process is reasonably selected and eliminated to ensure the validity and accuracy of the data. At the same time, due to the uncertainty of tunnel construction, there is not a daily footage. At the same time, due to the uncertainty of drilling and blasting, the record of daily footage is not completely accurate, so this study gives priority to weekly data for statistics. The footage is the distance the tunnel excavation surface advances forward during the tunnel excavation process.

[0104] 3. Progress control methods for tunnel construction projects.

[0105] According to the establishment principle of the construction delay warning indicator system and referring to the existing literature, the current progress deviation rate (α i ) and the dynamic target duration deviation rate (β i ) as a dynamic control indicator.

[0106] 3.1 Setting of monitoring time points.

[0107] Since the construction period is affected by many factors and may change during the tunnel construction process, building an effective monitoring system is crucial to capture real-time changes in the construction progress. In order to ensure the achievement of the established tunnel construction project goals, it is usually necessary to deploy monitoring equipment on site to effectively track and control the time schedule and actual completion of the tunnel construction project. Using monitoring data to adjust and improve the construction progress is an efficient means. In this process, it is key to reasonably determine the monitoring time point. According to the specific requirements for monitoring the progress of the tunnel construction project, various time units such as weeks, half months and months can be selected as the benchmark points for progress monitoring. These monitoring time points are set on the key construction paths of the construction network plan. The setting of the monitoring time points also needs to have a certain time span, and the completion time of the working face is estimated to control the breakthrough time of the entire tunnel.

[0108] 3.2 The expression of the current progress deviation rate is:

[0109]

[0110] Among them, T mi represents the actual construction date from the start of the tunnel construction project to the monitoring time point, T li represents the planned construction date from the start of the tunnel construction project to the monitoring time point. When α>0, α i >α i-1 It indicates that the risk of progress delay is accelerating and the situation continues to deteriorate. i =α i-1 Indicates that the progress delay remains at the original speed, α i <α i-1 It means that the progress delay is effectively controlled and the risk is relatively reduced; when α<0, it means that the probability of completion of the tunnel work point is increased and the progress is advanced.

[0111] 3.3 The expression of dynamic target duration deviation rate is:

[0112]

[0113]

[0114] Among them, T i represents the predicted construction period at the monitoring time point, T 0 It indicates the planned construction period at the monitoring time point, the current surrounding rock grade natural day indicates the actual number of days of the tunnel construction project, and the current surrounding rock grade construction day indicates the number of days with tunnel progress.

[0115] Current progress deviation rate (α i ) describes the time delay status before the monitoring time point and the time delay trend close to the monitoring time point, which helps to show the delay status of the entire tunnel construction project.

[0116] 3.4 The expression of construction delay index is:

[0117] e Ti =η 1 ×α i +η 2 ×β i ;

[0118] Among them, η 1 Represents the weight of the current progress deviation rate, η 2 Represents the weight of the dynamic target duration deviation rate, η 1 With η 2 The sum of is 1.

[0119] 3.5 Tunnel construction progress management standards.

[0120] The progress control sets the grade interval boundary value ε according to the actual tunnel construction project 1 , ε 3 and ε 3 Different construction progress situations are divided into controllable working surface, basically controllable working surface, difficult to control working surface and uncontrollable working surface. 3 It is an uncontrollable operation surface, which is extremely difficult to control in the later stage. It is a severe warning, and you should avoid it or take measures to reduce the level; 2 <e≤ε 3 It is a difficult-to-control operation surface, which is difficult to control in the later stage, and has a moderate warning, requiring enhanced monitoring and treatment; 1 <e≤ε 2 It is a basically controllable working surface, which is difficult to control in the later stage, with a slight warning and needs to be monitored; e<ε 1 It is a controllable working surface and no warning is required and it can be ignored.

[0121] In summary, the present invention provides a method and system for dynamic management of tunnel construction progress, which obtains a key construction path in the tunnel construction project, the key construction path is the one with the longest construction period among multiple project construction paths, the project construction path is obtained by arranging multiple sub-construction projects in order of construction, the rate influencing factor data of each sub-construction project is input into a pre-trained tunnel construction rate prediction model and a construction rate prediction value of each sub-construction project is output, the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, the predicted construction period of the tunnel construction project is obtained by adding them together, and the construction plan date is determined; multiple monitoring time points are dynamically set according to the supervision needs of the tunnel construction project, and the current progress deviation rate and the dynamic target construction period deviation rate of each monitoring time point are calculated, and the construction period delay index is obtained according to the current progress deviation rate and the dynamic target construction period deviation rate; the construction progress of the tunnel construction project at each monitoring time point is judged according to the construction period delay index, and prompts and warnings are given to guide the adjustment of the subsequent construction progress of the tunnel construction project.

[0122] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0123] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0124] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for dynamic management of tunnel construction progress, characterized in that: The method comprises the following steps: Obtaining a key construction path in a tunnel construction project, wherein the key construction path is the one with the longest construction period among multiple project construction paths, and the project construction path is obtained by arranging multiple sub-construction projects in order of construction sequence; Based on the pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, the rate influencing factor data of each sub-construction project in the key construction path is obtained, the rate influencing factor data of each sub-construction project is input into the pre-trained tunnel construction rate prediction model and the construction rate prediction value of each sub-construction project is output; the tunnel construction rate prediction model is obtained based on the back propagation neural network model training; the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, the sum is added to obtain the tunnel predicted construction period of the tunnel construction project, and the construction plan date is determined; Dynamically set multiple monitoring time points according to the supervision requirements of the tunnel construction project, calculate the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point, and obtain the construction period delay index according to the current progress deviation rate and the dynamic target construction period deviation rate; The construction progress of the tunnel construction project at each monitoring time point is judged according to the construction delay index, and prompts and warnings are given to guide the adjustment of the subsequent construction progress of the tunnel construction project.

2. The tunnel construction progress dynamic management method according to claim 1 is characterized in that: The tunnel construction rate prediction model is trained using the following method: A training sample set is obtained, wherein the training sample set includes a plurality of samples, each sample includes rate influencing factor data collected for a single sub-construction project, and a corresponding construction rate prediction value is added as a label; the rate influencing factor data is collected from a pre-established indicator system for personnel management, material reserves, process methods, environmental conditions, and mechanical equipment; Obtaining an initial neural network model, wherein the initial neural network model takes the rate influencing factor data in each sample as input and takes the construction rate prediction value as output; the initial neural network model adopts a back propagation neural network model; the back propagation neural network model includes an input layer, a hidden layer and an output layer that are continuously arranged; The construction rate prediction value of each sample is compared with the label and a loss function is constructed. The parameters of the initial neural network model are iteratively updated with the goal of minimizing the loss function, and the updated initial neural network model is constructed as a tunnel construction rate prediction model.

3. The tunnel construction progress dynamic management method according to claim 2 is characterized in that: The method further comprises: The hidden layer performs weighted summation on the rate influencing factor data in each sample and then uses an activation function to process the data to obtain the construction rate prediction value of each sample.

4. The tunnel construction progress dynamic management method according to claim 1, characterized in that: According to the supervision requirements of the tunnel construction project, multiple monitoring time points are dynamically set, including: According to the time interval supervision requirements of the tunnel construction project, monitoring time points are set at time intervals of weeks, half months and months.

5. The tunnel construction progress dynamic management method according to claim 1, characterized in that: The expression for calculating the current progress deviation rate at each monitoring time point is: Among them, T mi represents the actual construction date from the start of the tunnel construction project to the monitoring time point, T li Indicates the planned construction date from the start of the tunnel construction project to the monitoring time point.

6. The tunnel construction progress dynamic management method according to claim 1, characterized in that: The expression for calculating the dynamic target duration deviation rate at each monitoring time point is: Among them, T i T0 represents the predicted construction period at the monitoring time point, and T1 represents the planned construction period at the monitoring time point.

7. The tunnel construction progress dynamic management method according to claim 1, characterized in that: The expression for obtaining the construction period delay index according to the current progress deviation rate and the dynamic target construction period deviation rate is: and Ti =η1×α i +η2×β i ; Among them, η1 represents the weight of the current progress deviation rate, and η2 represents the weight of the dynamic target duration deviation rate.

8. The tunnel construction progress dynamic management method according to claim 1, characterized in that: The construction progress of the tunnel construction project at each monitoring time point is judged according to the construction delay index, and prompts and early warnings are provided, including: According to the difficulty of later control of the tunnel construction project, multiple level interval boundaries of the construction delay index are determined, and at least four level intervals are divided using the level interval boundaries; each level interval corresponds to an extremely difficult control warning requiring a severe warning, a difficult control warning requiring a moderate warning, a relatively difficult control warning requiring a slight warning, and an easy control warning requiring no warning.

9. A tunnel construction progress dynamic management system, characterized in that: The system is used to execute the tunnel construction progress dynamic management method according to claims 1 to 8, and the system comprises: A critical construction path analysis module is used to obtain a critical construction path in a tunnel construction project. The critical construction path is the one with the longest construction period among multiple project construction paths. The project construction path is obtained by arranging multiple sub-construction projects in order of construction sequence. A construction period prediction module is used to obtain the rate influencing factor data of each sub-construction project in the key construction path based on a pre-established indicator system for personnel management, material reserves, process methods, environmental conditions and mechanical equipment, input the rate influencing factor data of each sub-construction project into a pre-trained tunnel construction rate prediction model and output a construction rate prediction value of each sub-construction project; the tunnel construction rate prediction model is obtained by training based on a back propagation neural network model; the workload of each sub-construction project is divided by the corresponding construction rate prediction value to obtain the predicted construction period of each sub-construction project, the sum is added to obtain the tunnel predicted construction period of the tunnel construction project, and the construction plan date is determined; A construction delay index calculation module is used to dynamically set multiple monitoring time points according to the supervision requirements of the tunnel construction project, calculate the current progress deviation rate and the dynamic target construction period deviation rate at each monitoring time point, and obtain the construction delay index according to the current progress deviation rate and the dynamic target construction period deviation rate; The construction progress warning and adjustment module is used to determine the construction progress of the tunnel construction project at each monitoring time point according to the construction period delay index, and to provide prompts and warnings to guide the adjustment of the subsequent construction progress of the tunnel construction project.

10. The tunnel construction progress dynamic management system according to claim 9, characterized in that: The system further comprises: The cloud storage module is used to store the key construction path, the predicted construction rate value of each sub-construction project, the predicted construction period of each sub-construction project, the predicted tunnel construction period and the construction period delay index.

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