A construction process-based cast-in-place underground continuous wall carbon emission metering and reduction method

Abstract

This invention discloses a method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures, belonging to the field of carbon emission measurement technology in building construction. The method includes the following steps: obtaining the construction procedures for cast-in-place diaphragm walls and the construction drawings of the diaphragm wall to be constructed; taking the upcoming construction procedure as the target procedure and determining the construction content of the target procedure based on the construction drawings; obtaining construction requirement data based on the construction content; obtaining construction structure data, generating an optimal mechanical construction strategy based on the construction structure data, construction machinery types, and machinery configuration data, and carrying out construction; calculating a first carbon emission based on building material types and building material consumption data, obtaining construction machinery fuel consumption data, and obtaining a second carbon emission; obtaining the carbon emission of the target procedure based on the first and second carbon emission amounts; repeating the above steps to obtain the carbon emission of each procedure in the cast-in-place diaphragm wall construction process.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission measurement technology in building construction, specifically relating to a method for measuring and reducing carbon emissions from cast-in-place diaphragm walls based on construction procedures. Background Technology

[0002] The construction industry is one of the major sources of global carbon emissions, accounting for nearly 40% of global energy-related carbon emissions. Its energy conservation and carbon reduction are crucial to achieving the "dual carbon" goals. Accurately quantifying carbon emissions is a prerequisite for my country's building carbon reduction assessment and low-carbon transformation. In the entire building life cycle, although the construction phase is shorter than the operation phase, its carbon emission intensity per unit time is extremely high. This is especially true for common technologies in deep foundation pit engineering such as cast-in-place diaphragm walls. The construction process involves multiple high-energy-consuming and high-material-consuming procedures such as trenching, mud wall protection, rebar cage fabrication and hoisting, and concrete pouring, making it a key target for carbon emission accounting and control during the construction phase.

[0003] Currently, research and practice in building carbon emission accounting mainly focus on energy consumption and carbon emissions during the building operation phase, with less attention paid to carbon emissions during the construction phase. Existing construction carbon emission accounting methods primarily focus on the carbon emissions from the production of main building materials (such as steel and concrete) and the fuel consumption of major construction machinery (such as trenching machines and crawler cranes). However, considering the construction characteristics of cast-in-place diaphragm walls, existing practices neglect key carbon sources in the detailed construction process: first, the consumption of auxiliary materials and energy consumption related to the extensive use of bentonite slurry and its recycling system during trenching; second, the energy consumption of auxiliary materials such as welding rods, oxygen, and acetylene consumed in welding operations during the fabrication of reinforcing cages, as well as the energy consumption of their processing equipment; and third, the indirect carbon emissions generated by machinery scheduling and standby during multi-process overlapping operations. This simplified accounting approach inevitably leads to an underestimation of actual construction carbon emissions, failing to accurately reflect the carbon footprint of diaphragm wall construction activities.

[0004] Therefore, there is an urgent need for a carbon emission measurement and reduction method for cast-in-place diaphragm walls based on construction procedures to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention provides a method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures, which solves the problems of existing carbon emission measurement methods, such as extensive calculation, neglect of auxiliary materials, low accuracy and lack of process-level low-carbon basis.

[0006] To achieve the above objectives, this invention provides a method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures, comprising the following steps: S1: Obtain the construction procedures for cast-in-place diaphragm walls and the construction drawings for the diaphragm walls to be constructed; S2: Take the process that will be carried out in the construction process of cast-in-place diaphragm wall as the target process, and determine the construction content of the target process according to the construction drawings; S3: Obtain construction requirement data based on the construction content; the construction requirement data includes: building material types, building material consumption data, construction machinery types and machinery configuration data; S4: Obtain construction structure data, generate the optimal mechanical construction strategy based on the construction structure data, construction machinery types and machinery configuration data, and carry out construction. S5: Calculate the first carbon emission based on the type of building materials and the data on the consumption of building materials, and obtain the fuel consumption data of the construction machinery. Obtain the second carbon emission based on the fuel consumption data of the construction machinery, and obtain the carbon emission of the target process based on the first carbon emission and the second carbon emission. S6: Repeat steps S2 to S5 above until the continuous wall construction is completed, and obtain the carbon emissions of each step in the construction process of the cast-in-place underground continuous wall.

[0007] As an embodiment of the present invention, the construction content includes: construction type and construction volume; S3 includes the following steps: Based on the construction type and the pre-set construction material table, the building material mix ratio and building material types corresponding to the construction type are obtained; among them, the pre-set construction material table is a relationship table corresponding to the construction type, building material mix ratio and construction method; The building material consumption data is calculated based on the building material mix ratio and construction volume, and the type of construction machinery is determined according to the construction method. Determine machinery configuration data based on the types of construction machinery and building material consumption data; Construction requirements data are determined based on the types of building materials, building material consumption data, types of construction machinery, and machinery configuration data.

[0008] As an embodiment of the present invention, S4 includes the following steps: Obtain construction structure data; the construction structure data includes: construction site data and data of the constructed structures; Based on the BIM model building method, a BIM model of the construction site is built according to the construction structure data; Based on the types and configuration data of construction machinery, a digital twin model of the construction machinery is constructed in the BIM model of the construction site; the digital twin model includes: the dimensions of the construction machinery, the operating radius, the travel speed, the fuel consumption rate curve, and the power parameters; Based on the types of building materials and building material consumption data, 4D construction simulation is performed in the BIM model of the construction site to obtain a set of candidate construction strategies. Each construction strategy includes: the starting position, movement path, building material operation time node, load status, operation landing point coordinates, and machine standby and start-stop sequence of each construction machine. The fuel consumption of each construction strategy during its execution is calculated based on the fuel consumption rate curve and power parameters. The construction strategy with the lowest fuel consumption was selected as the optimal mechanical construction strategy and the construction was carried out accordingly.

[0009] As an embodiment of the present invention, S5 includes the following steps: By inputting the types of building materials and their consumption data into a pre-trained material carbon emission model, the first carbon emission is obtained. Obtain the total fuel consumption data of each construction machine after the completion of the target process, and input it into the pre-trained fuel consumption carbon emission model to obtain the second carbon emission. The carbon emissions of the target process are obtained by summing the first carbon emissions and the second carbon emissions.

[0010] As one embodiment of the present invention, it also includes: After the target process is completed, obtain the actual construction data during the construction process of the target process; the actual construction data includes: actual building material consumption data, machinery operation data and construction conditions; Construct a carbon emission characteristic vector for the target process based on actual construction data, construction content, and carbon emissions. Based on a pre-defined scoring method, the carbon emission feature vector is compared with cases in a pre-defined case library to obtain the process construction score of the target process; among them, the cases in the pre-defined case library are excellent cases in the construction process; Determine if the construction score of the process is greater than the preset score; if it is, add the target process as an excellent case to the preset case library; if it is not, identify the construction problem points, generate an evaluation report for the target process, and notify the administrator.

[0011] As an embodiment of the present invention, based on a preset scoring method, the carbon emission feature vector is compared with cases in a preset case library to obtain a process construction score for the target process, including: Based on the construction content, select multiple cases from the pre-set case library as comparison cases; An initial decision matrix is ​​constructed based on comparative cases and carbon emission feature vectors, and all indicators in the initial decision matrix are transformed into positive indicators to obtain the final decision matrix. Determine the positive and negative ideal solutions for the decision; where the positive ideal solution is the maximum value of each index in the decision matrix, and the negative ideal solution is the minimum value of each index in the decision matrix. The Euclidean distances between the carbon emission characteristic vector of the target process and the positive and negative ideal solutions are calculated as follows: In the formula, This represents the Euclidean distance between the carbon emission eigenvector and the positive ideal solution. This represents the Euclidean distance between the carbon emission eigenvector and the negative ideal solution. Indicates the number of carbon emission characteristic indicators. This indicates that the target process is the [number]th process in the decision matrix. 1 eigenvalue, In the decision matrix, the first... The positive ideal solution for each feature In the decision matrix, the first... Negative ideal solutions for each characteristic; The construction score of the target process is calculated based on the Euclidean distance between the carbon emission characteristic vector of the target process and the positive and negative ideal solutions, as shown below: In the formula, This indicates the process construction score for the target process.

[0012] As one embodiment of the present invention, identifying construction problem points and generating an evaluation report for the target process includes: The difference between the carbon emission characteristic vector of the target process and the positive ideal solution is calculated as follows: In the formula, The first element representing the carbon emission eigenvector The gap between each characteristic and the ideal solution; The objective weights of each characteristic indicator are determined based on the entropy weight method; The contribution of the gap is calculated based on the difference between the carbon emission characteristic vector and the positive ideal solution, and the objective weights, as shown below: In the formula, Indicates the contribution of the gap. The first element representing the carbon emission eigenvector The objective weight of each feature; The key issues are identified based on the contribution of the gap, and an evaluation report is generated by combining the process construction scores.

[0013] The beneficial effects of this invention are as follows: Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0014] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a flowchart illustrating step 4 of the present invention. Detailed Implementation

[0015] like Figures 1-2 As shown, this invention provides a method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures, including: S1: Obtain the construction procedures for cast-in-place diaphragm walls and the construction drawings for the diaphragm walls to be constructed; S2: Take the process that will be carried out in the construction process of cast-in-place diaphragm wall as the target process, and determine the construction content of the target process according to the construction drawings; S3: Obtain construction requirement data based on the construction content; the construction requirement data includes: building material types, building material consumption data, construction machinery types and machinery configuration data; S4: Obtain construction structure data, generate the optimal mechanical construction strategy based on the construction structure data, construction machinery types and machinery configuration data, and carry out construction. S5: Calculate the first carbon emission based on the type of building materials and the data on the consumption of building materials, and obtain the fuel consumption data of the construction machinery. Obtain the second carbon emission based on the fuel consumption data of the construction machinery, and obtain the carbon emission of the target process based on the first carbon emission and the second carbon emission. S6: Repeat steps S2 to S5 above until the continuous wall construction is completed, and obtain the carbon emissions of each step in the construction process of the cast-in-place underground continuous wall.

[0016] The working principle of the above technical solution is as follows: During the construction of cast-in-place diaphragm walls, the construction method of the cast-in-place diaphragm wall is first obtained and broken down to obtain the construction sequence of the cast-in-place diaphragm wall; for example, the construction sequence includes: Sequence 1 (guide wall construction) - Sequence 2 (slurry preparation) - Sequence 3 (trenching) - Sequence 4 (lowering the reinforcing cage) - Sequence 5 (concrete pouring); at the same time, the necessary construction drawings are obtained; the construction drawings include: the geometric dimensions of the wall, reinforcement information, burial depth, trench segment division, concrete strength grade and impermeability grade, and other technical parameters; then, in the entire construction process of the diaphragm wall, the next process node to be implemented is defined as the target process, and the construction content of the target process is determined according to the construction drawings; the construction content includes: construction type and construction volume; for example, if the target process is "trenching", the depth, width, length of the trench segment and the geological layer information it traverses are determined; then, based on the construction content of the target process, the corresponding construction requirement data is determined; the construction requirement data includes: building material types, including but not limited to steel, cement, sand, gravel, water, admixtures, etc.; building material consumption... The consumption is precisely calculated based on the engineering quantity and loss coefficient in the design drawings; the types of construction machinery are selected according to the characteristics of the process and the site conditions, including trenching machines, crawler cranes, concrete mixer trucks, mud pumps, etc.; the machinery configuration data includes the model, rated power, quantity, etc. of the machinery; then, the structural data of the construction site is obtained, and the optimal machinery construction strategy is generated and construction is carried out based on the construction structure data, the types of construction machinery, and the machinery configuration data; finally, based on the determined types and consumption of building materials, the carbon emissions of all building materials in the target process during the production stage are calculated, and the first carbon emission is obtained; at the same time, during the construction process, the fuel consumption data of the construction machinery in the target process is collected by IoT sensors or fuel flow meters installed on the construction machinery, and the fuel consumption data is converted into carbon emissions to obtain the second carbon emission; finally, the carbon emission of the target process is obtained based on the first and second carbon emission, and after the continuous wall construction is completed, the carbon emission of each process of the cast-in-place underground continuous wall construction process is added together to obtain the total carbon emission of the entire cast-in-place underground continuous wall to be constructed.

[0017] The beneficial effects of the above technical solution are as follows: By refining carbon emission measurement through construction procedures, the solution addresses the problems of existing carbon emission measurement methods, such as extensive calculations, neglect of auxiliary materials, low accuracy, and lack of process-level low-carbon basis. Furthermore, carbon emission measurement through construction procedures allows managers to identify key points of carbon emission release during construction, enabling optimization and reduction of carbon emissions. Finally, by optimizing construction machinery according to the construction procedures and pre-planning reasonable construction strategies, fuel consumption of construction machinery can be reduced, further reducing carbon emissions during construction.

[0018] In one embodiment, S3 includes the following steps: Based on the construction type and the pre-set construction material table, the building material mix ratio and building material types corresponding to the construction type are obtained; among them, the pre-set construction material table is a relationship table corresponding to the construction type, building material mix ratio and construction method; The building material consumption data is calculated based on the building material mix ratio and construction volume, and the type of construction machinery is determined according to the construction method. Determine machinery configuration data based on the types of construction machinery and building material consumption data; Construction requirements data are determined based on the types of building materials, building material consumption data, types of construction machinery, and machinery configuration data.

[0019] The working principle and beneficial effects of the above technical solution are as follows: When determining construction requirement data, firstly, the system retrieves a pre-set construction material table based on the construction type of the current target process. This material table is a pre-established knowledge database that stores the correspondence between different construction types (such as trenching, rebar cage fabrication, concrete pouring, etc.) and building material mix ratios (such as slurry mix ratio, concrete mix ratio) and construction methods (such as mechanical trenching, manual binding, etc.). Secondly, based on the retrieved building material mix ratios and the construction volumes in the construction drawings (such as trench volume, rebar weight, dimensions, etc.), the actual consumption data required for various building materials is accurately calculated. Simultaneously, the corresponding construction machinery types (such as trenching machines, crawler cranes, welding machines, etc.) are matched according to the determined construction methods. Finally, combining the determined building material consumption data and construction machinery types, the machinery configuration data is further determined, including parameters such as machinery model and quantity. Through the above steps, complete, accurate, and process-specific construction requirement data is ultimately integrated, providing a reliable data input foundation for subsequent carbon emission metering and machinery scheduling optimization. Calculations based on precise building material mix ratios and actual construction volumes not only cover main building materials such as steel and concrete, but also auxiliary materials such as slurry, admixtures, and welding rods. This solves the technical defect in existing technologies where the actual carbon emissions during construction are underestimated due to the neglect of auxiliary material consumption. At the same time, by determining the type of machinery through the construction method and then combining it with the building material consumption to deduce the machinery configuration data, the selection and quantity of machinery are more in line with the actual workload, avoiding energy waste caused by redundant machinery configuration.

[0020] In one embodiment, S4 includes the following steps: Obtain construction structure data; the construction structure data includes: construction site data and data of the constructed structures; Based on the BIM model building method, a BIM model of the construction site is built according to the construction structure data; Based on the types and configuration data of construction machinery, a digital twin model of the construction machinery is constructed in the BIM model of the construction site; the digital twin model includes: the dimensions of the construction machinery, the operating radius, the travel speed, the fuel consumption rate curve, and the power parameters; Based on the types of building materials and building material consumption data, 4D construction simulation is performed in the BIM model of the construction site to obtain a set of candidate construction strategies. Each construction strategy includes: the starting position, movement path, building material operation time node, load status, operation landing point coordinates, and machine standby and start-stop sequence of each construction machine. The fuel consumption of each construction strategy during its execution is calculated based on the fuel consumption rate curve and power parameters. The construction strategy with the lowest fuel consumption was selected as the optimal mechanical construction strategy and the construction was carried out accordingly.

[0021] The working principle and beneficial effects of the above technical solution are as follows: During the construction of diaphragm walls, there are problems such as construction machinery idling and machinery not being in place, leading to repeated and time-consuming material transportation and hoisting, resulting in a surge in fuel consumption of construction machinery and some material waste. Therefore, in order to solve the above problems and reduce carbon emissions during the specific construction process, this technical solution first acquires the construction structure data of the construction site before carrying out the target construction process. The construction structure data includes: construction site data, such as construction location and building material location, and constructed structure data, such as the size data, coordinate data, and connection point coordinates of the completed structures of the completed construction processes. Then, based on the existing BIM model construction method, a construction site BIM model with the same scale is constructed according to the construction structure data. Then, according to the type and model of the construction machinery to be used, the specific parameters corresponding to the machinery data are acquired, such as three-dimensional dimensions, working radius, travel speed, rotation speed, fuel consumption rate curve, and power parameters related to machinery fuel consumption, and a corresponding digital twin model is constructed based on this. Then, according to the determined type of building materials... Using building material consumption as input, a 4D construction simulation of the target process is performed in the BIM model of the construction site. During the simulation, multiple sets of different machine scheduling parameters are set to form different construction strategies. These strategies include, but are not limited to, the starting position, movement path, building material operation time nodes, load status, operation landing point coordinates, and machine standby and start-stop sequence of each construction machine. Then, in the BIM model simulation environment, each candidate machine construction strategy is run. During the simulation, based on the preset machine fuel consumption rate curve in the digital twin model, combined with the machine's movement trajectory length, running time, and load status (obtained from the corresponding power parameters), the total fuel consumption of all construction machines under each candidate construction strategy during the entire target process is dynamically calculated, resulting in the construction fuel consumption of each strategy during execution. Finally, the construction strategy with the lowest construction fuel consumption is selected as the optimal machine construction strategy, guiding construction machine operators or automatic control systems to perform precise operations according to this strategy, thereby achieving low-carbon operation of the construction process. By constructing a BIM model of the construction site and a digital twin of the machinery, fuel consumption is pre-simulated and quantitatively compared for various machinery scheduling strategies in a 4D simulation environment. This allows for the selection of the optimal solution with the lowest total fuel consumption to guide actual construction. This technical solution fundamentally solves the problems of soaring fuel consumption and material waste caused by machinery idling, inadequate positioning, and detours in traditional experience-based scheduling. It not only significantly reduces carbon emissions during construction through precise planning of machinery movements, but also improves the efficiency of multi-machine collaborative operations and reduces material loss, providing a data-driven low-carbon optimization method for diaphragm wall construction.

[0022] In one embodiment, S5 includes the following steps: By inputting the types of building materials and their consumption data into a pre-trained material carbon emission model, the first carbon emission is obtained. Obtain the total fuel consumption data of each construction machine after the completion of the target process, and input it into the pre-trained fuel consumption carbon emission model to obtain the second carbon emission. The carbon emissions of the target process are obtained by summing the first carbon emissions and the second carbon emissions.

[0023] The working principle and beneficial effects of the above technical solution are as follows: By constructing two independent pre-trained carbon emission models, accurate quantitative calculation of carbon emissions for the target process can be achieved. First, for the material carbon emission model, the training method is as follows: historical consumption data of commonly used building materials in various construction projects (including main and auxiliary materials such as steel, concrete, mortar, and welding rods) and their corresponding measured carbon emission values ​​at different production stages are collected. Combined with national or industry-published building material carbon emission factor databases, a training dataset is constructed. Algorithms such as linear regression or neural networks are used, with building material type and consumption as input features and the implicit carbon emission of building materials as the output label, to train the model, enabling it to automatically learn and establish a mapping relationship between building material consumption and carbon emissions. For the fuel consumption carbon emission model, the training method is as follows: fuel consumption data and corresponding carbon emission conversion values, and fuel type carbon emission factors are collected to construct a training dataset. Algorithms such as linear regression or neural networks are used, with construction machinery... Fuel consumption data is used as input, and operational carbon emissions are used as output for model training, enabling the model to accurately convert different fuel consumption scenarios into corresponding carbon emissions. In practical applications, the types of building materials determined in step S3 and their corresponding building material consumption data are input into the trained material carbon emission model, which automatically calculates the carbon emissions implied in the upstream stages of building material production and transportation, i.e., the first carbon emission. After the target process is completed, the actual total fuel consumption data of all construction machinery during the process is collected and input into the trained fuel consumption carbon emission model, which is accurately converted into the direct carbon emissions generated during the operation of the construction machinery, i.e., the second carbon emission. Finally, the first carbon emission and the second carbon emission are combined to obtain the complete and accurate total carbon emissions of the target process during this construction process.

[0024] By processing carbon emissions from both the building materials and machinery sides separately, this approach encompasses the implicit carbon from main and auxiliary building materials, as well as the energy consumption carbon from dynamic machinery operations. This solves the problem of underestimating actual carbon emissions due to incomplete accounting methods in existing technologies, ensuring the completeness and accuracy of process carbon emissions. Using actual fuel consumption data obtained after construction is completed as input can accurately reflect energy consumption differences caused by actual factors such as machinery scheduling, on-site conditions, and operating habits, making carbon emission measurement results closer to the actual project. By summing the first and second carbon emissions to obtain the process carbon emissions and comparing them with the BIM simulation prediction value in step S4, the simulation accuracy can be verified, abnormal carbon emission links can be identified, and data experience can be accumulated for low-carbon optimization of subsequent construction projects, forming a closed-loop management mechanism of "simulation prediction - actual measurement - feedback optimization".

[0025] In one embodiment, it also includes: After the target process is completed, obtain the actual construction data during the construction process of the target process; the actual construction data includes: actual building material consumption data, machinery operation data and construction conditions; Construct a carbon emission characteristic vector for the target process based on actual construction data, construction content, and carbon emissions. Based on a pre-defined scoring method, the carbon emission feature vector is compared with cases in a pre-defined case library to obtain the process construction score of the target process; among them, the cases in the pre-defined case library are excellent cases in the construction process; Determine if the construction score of the process is greater than the preset score; if it is, add the target process as an excellent case to the preset case library; if it is not, identify the construction problem points, generate an evaluation report for the target process, and notify the administrator.

[0026] Specifically, based on a pre-defined scoring method, the carbon emission feature vector is compared with cases in a pre-defined case library to obtain the construction score of the target process, including: Based on the construction content, select multiple cases from the pre-set case library as comparison cases; An initial decision matrix is ​​constructed based on comparative cases and carbon emission feature vectors, and all indicators in the initial decision matrix are transformed into positive indicators to obtain the final decision matrix. Determine the positive and negative ideal solutions for the decision; where the positive ideal solution is the maximum value of each index in the decision matrix, and the negative ideal solution is the minimum value of each index in the decision matrix. The Euclidean distances between the carbon emission characteristic vector of the target process and the positive and negative ideal solutions are calculated as follows: In the formula, This represents the Euclidean distance between the carbon emission eigenvector and the positive ideal solution. This represents the Euclidean distance between the carbon emission eigenvector and the negative ideal solution. Indicates the number of carbon emission characteristic indicators. This indicates that the target process is the [number]th process in the decision matrix. 1 eigenvalue, In the decision matrix, the first... The positive ideal solution for each feature In the decision matrix, the first... Negative ideal solutions for each characteristic; The construction score of the target process is calculated based on the Euclidean distance between the carbon emission characteristic vector of the target process and the positive and negative ideal solutions, as shown below: In the formula, This indicates the process construction score for the target process.

[0027] This includes identifying construction problem areas and generating an evaluation report for the target process, including: The difference between the carbon emission characteristic vector of the target process and the positive ideal solution is calculated as follows: In the formula, The first element representing the carbon emission eigenvector The gap between each characteristic and the ideal solution; The objective weights of each characteristic indicator are determined based on the entropy weight method; The contribution of the gap is calculated based on the difference between the carbon emission characteristic vector and the positive ideal solution, and the objective weights, as shown below: In the formula, Indicates the contribution of the gap. The first element representing the carbon emission eigenvector The objective weight of each feature; The key issues are identified based on the contribution of the gap, and an evaluation report is generated by combining the process construction scores.

[0028] The working principle of the above technical solution is as follows: After the construction of the target process is completed, the actual construction data of the target process is obtained. This actual construction data includes: actual building material consumption data (such as the actual consumption of water, steel bars, welding rods, etc.), machinery operation data (such as the types and models of machinery actually used), and construction conditions (such as the construction season and work teams). Then, based on the actual construction data, construction content, and carbon emissions, a carbon emission feature vector for the target process is generated. This carbon emission feature vector includes dimensions such as machinery utilization rate, construction efficiency, and carbon emissions per unit project. Finally, based on the construction type of the target process and similar construction environmental conditions, a matching process is performed in a pre-set case library (all of which are high-quality cases under the same construction process). Similar cases are used as comparison cases, and the construction score of the target process is obtained according to a preset scoring method. The preset scoring method is as follows: a carbon emission feature vector (with the same dimension as the target process) is generated based on the content of the comparison cases; then, an initial decision matrix (of size 1) is constructed by combining the carbon emission feature vector of the target process with the comparison case's carbon emission feature vector. The process involves converting all indicators in the initial decision matrix into positive indicators (larger values ​​indicate better low-carbon performance) to obtain the decision matrix; then determining the positive and negative ideal solutions of the decision matrix and calculating the Euclidean distance (the larger the distance, the greater the deviation between the characteristic indicators of the target process and the ideal solution); and obtaining the process construction score of the target process based on the calculated Euclidean distance.

[0029] The process involves determining whether the construction score of a specific process exceeds a preset value. If it does, it indicates good performance in the construction of the target process. If it does not exceed the preset value, it indicates problems in the construction process that require improvement. First, the gap between the carbon emission characteristic vector of the target process and the ideal solution is calculated. Then, the objective weights of each characteristic indicator are determined based on the entropy weight method (existing technology, not elaborated upon here). The gap contribution is calculated based on the gap contribution and the objective weights. Key issues are identified based on the gap contribution (the contribution of each characteristic indicator is sorted from largest to smallest, and indicators with a cumulative contribution of 80% are selected as key issue indicators; each key indicator is transformed into a specific key issue point according to the preset process-indicator-problem mapping table). An evaluation report is then generated by combining the process construction score with the evaluation. The beneficial effects of the above technical solution are as follows: By acquiring data on actual building material consumption and machinery operation in real time after the completion of a single target process, a carbon emission feature vector containing dimensions such as machinery utilization rate and carbon emissions per unit project is generated. This vector is then compared with cases of the same process type and similar construction environment in a high-quality case library to calculate the process construction score. When the score is lower than the preset value, an evaluation report containing the score and problem diagnosis is generated. This mechanism enables construction teams to instantly identify problems and clarify improvement directions in subsequent processes of the same project, and quickly adjust their operational behavior (such as optimizing mud ratio and adjusting the way the optimal construction strategy is implemented). This achieves a rapid closed loop of "construction, evaluation, and improvement simultaneously," fundamentally solving the problem of the disconnect between carbon emission measurement and construction improvement in traditional methods. It effectively promotes the continuous improvement of the low-carbon operation level of construction teams, thereby achieving carbon reduction in the construction process.

[0030] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures, characterized in that, Includes the following steps: S1: Obtain the construction procedures for cast-in-place diaphragm walls and the construction drawings for the diaphragm walls to be constructed; S2: Take the process that will be carried out in the construction process of cast-in-place diaphragm wall as the target process, and determine the construction content of the target process according to the construction drawings; S3: Obtain construction requirement data based on the construction content; the construction requirement data includes: building material types, building material consumption data, construction machinery types and machinery configuration data; S4: Obtain construction structure data, generate the optimal mechanical construction strategy based on the construction structure data, construction machinery types and machinery configuration data, and carry out construction. S5: Calculate the first carbon emission based on the type of building materials and the data on the consumption of building materials, and obtain the fuel consumption data of the construction machinery. Obtain the second carbon emission based on the fuel consumption data of the construction machinery, and obtain the carbon emission of the target process based on the first carbon emission and the second carbon emission. S6: Repeat steps S2 to S5 above until the continuous wall construction is completed, and obtain the carbon emissions of each step in the construction process of the cast-in-place underground continuous wall.

2. The method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures as described in claim 1, characterized in that, Construction scope includes: construction type and construction volume; S3 includes the following steps: Based on the construction type and the pre-set construction material table, the building material mix ratio and building material types corresponding to the construction type are obtained; among them, the pre-set construction material table is a relationship table corresponding to the construction type, building material mix ratio and construction method; The building material consumption data is calculated based on the building material mix ratio and construction volume, and the type of construction machinery is determined according to the construction method. Determine machinery configuration data based on the types of construction machinery and building material consumption data; Construction requirements data are determined based on the types of building materials, building material consumption data, types of construction machinery, and machinery configuration data.

3. The method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures as described in claim 1, characterized in that, S4 includes the following steps: Obtain construction structure data; the construction structure data includes: construction site data and data of the constructed structures; Based on the BIM model building method, a BIM model of the construction site is built according to the construction structure data; Based on the types and configuration data of construction machinery, a digital twin model of the construction machinery is constructed in the BIM model of the construction site; the digital twin model includes: the dimensions of the construction machinery, the operating radius, the travel speed, the fuel consumption rate curve, and the power parameters; Based on the types of building materials and building material consumption data, 4D construction simulation is performed in the BIM model of the construction site to obtain a set of candidate construction strategies. Each construction strategy includes: the starting position, movement path, building material operation time node, load status, operation landing point coordinates, and machine standby and start-stop sequence of each construction machine. The fuel consumption of each construction strategy during its execution is calculated based on the fuel consumption rate curve and power parameters. The construction strategy with the lowest fuel consumption was selected as the optimal mechanical construction strategy and the construction was carried out accordingly.

4. The method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures as described in claim 1, characterized in that, S5 includes the following steps: By inputting the types of building materials and their consumption data into a pre-trained material carbon emission model, the first carbon emission is obtained. Obtain the total fuel consumption data of each construction machine after the completion of the target process, and input it into the pre-trained fuel consumption carbon emission model to obtain the second carbon emission. The carbon emissions of the target process are obtained by summing the first carbon emissions and the second carbon emissions.

5. The method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures according to claim 1, characterized in that, Also includes: After the target process is completed, obtain the actual construction data during the construction process of the target process; the actual construction data includes: actual building material consumption data, machinery operation data and construction conditions; Construct a carbon emission characteristic vector for the target process based on actual construction data, construction content, and carbon emissions. Based on a pre-defined scoring method, the carbon emission feature vector is compared with cases in a pre-defined case library to obtain the process construction score of the target process; among them, the cases in the pre-defined case library are excellent cases in the construction process; Determine if the construction score of the process is greater than the preset score; if it is, add the target process as an excellent case to the preset case library; if it is not, identify the construction problem points, generate an evaluation report for the target process, and notify the administrator.

6. The method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures as described in claim 5, characterized in that, Based on a pre-defined scoring method, the carbon emission feature vector is compared with cases in a pre-defined case library to obtain the construction score of the target process, including: Based on the construction content, select multiple cases from the pre-set case library as comparison cases; An initial decision matrix is ​​constructed based on comparative cases and carbon emission feature vectors, and all indicators in the initial decision matrix are transformed into positive indicators to obtain the final decision matrix. Determine the positive and negative ideal solutions for the decision; where the positive ideal solution is the maximum value of each index in the decision matrix, and the negative ideal solution is the minimum value of each index in the decision matrix. The Euclidean distances between the carbon emission characteristic vector of the target process and the positive and negative ideal solutions are calculated as follows: In the formula, This represents the Euclidean distance between the carbon emission eigenvector and the positive ideal solution. This represents the Euclidean distance between the carbon emission eigenvector and the negative ideal solution. Indicates the number of carbon emission characteristic indicators. This indicates that the target process is the [number]th process in the decision matrix. 1 eigenvalue, In the decision matrix, the first... The positive ideal solution for each feature In the decision matrix, the first... Negative ideal solutions for each characteristic; The construction score of the target process is calculated based on the Euclidean distance between the carbon emission characteristic vector of the target process and the positive and negative ideal solutions, as shown below: In the formula, This indicates the process construction score for the target process.

7. The method for carbon emission measurement and reduction of cast-in-place diaphragm walls based on construction procedures as described in claim 5, characterized in that, Identify construction problem areas and generate an assessment report for the target process, including: The difference between the carbon emission characteristic vector of the target process and the positive ideal solution is calculated as follows: In the formula, The first characteristic vector representing carbon emissions The gap between each characteristic and the ideal solution; The objective weights of each characteristic indicator are determined based on the entropy weight method; The contribution of the gap is calculated based on the difference between the carbon emission characteristic vector and the positive ideal solution, and the objective weights, as shown below: In the formula, Indicates the contribution of the gap. The first characteristic vector representing carbon emissions The objective weight of each feature; The key issues are identified based on the contribution of the gap, and an evaluation report is generated by combining the process construction scores.

Images