Construction waste secondary utilization design scheme generation method and system

Abstract

The invention relates to the technical field of modern building construction, in particular to a construction waste secondary utilization design scheme generation method and system. The method comprises the following steps: acquiring image data of a construction waste area, and identifying physical characteristics of construction waste according to the image data of the construction waste area; in combination with the engineering BIM model and the engineering progress, extracting structural features of engineering later-stage components; generating a feasible secondary utilization design scheme according to the physical characteristics of the construction waste and the structural characteristics of the engineering later-stage component; and constructing a waste secondary utilization scheme evaluation model, and evaluating the secondary utilization design scheme by using the waste secondary utilization scheme evaluation model to obtain an optimal secondary utilization design scheme. According to the method, the secondary utilization scheme is automatically generated by identifying the waste, and the problems that in the existing construction waste secondary utilization process, the recycling rate is low, a systematic design scheme generation method is lacked, and the scheme matching degree and feasibility are poor are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of modern construction technology, in particular to a construction waste secondary utilization design scheme generation method and system. BACKGROUND

[0002] Currently, there are still significant limitations in the field of construction waste resource utilization. Firstly, the resource conversion rate needs to be improved. Most construction waste is only subjected to primary crushing treatment and is mainly used in low-value landfill or site backfill engineering, and a high-value recycling system has not yet been formed. Secondly, there is a lack of standardized and intelligent secondary utilization scheme generation mechanism. The existing disposal mode relies on engineering experience and cannot accurately adapt to specific engineering projects based on core indicators such as waste mineral composition and mechanical properties, combined with technical parameters and functional requirements of the project, resulting in insufficient resource recycling efficiency. Thirdly, the data collection system has structural defects. Key data such as the source traceability, physicochemical properties, and inventory distribution of waste are missing or incomplete, which cannot provide effective support for the technical and economic feasibility analysis of secondary utilization schemes, and restricts the maximization of circular economy benefits.

[0003] Therefore, there is an urgent need for a method that can efficiently and accurately generate a construction waste secondary utilization design scheme to improve the utilization rate of construction waste, reduce environmental pollution, and promote the green and sustainable development of the construction industry. SUMMARY

[0004] In view of the deficiencies of existing methods and the needs of practical applications, on the one hand, the present application provides a construction waste secondary utilization design scheme generation method, comprising the following steps: obtaining construction waste area image data, identifying the physical characteristics of the construction waste according to the construction waste area image data; extracting the structural characteristics of the post-construction components in combination with the engineering BIM model and the engineering progress; generating a feasible secondary utilization design scheme according to the physical characteristics of the construction waste and the structural characteristics of the post-construction components; constructing a waste secondary utilization scheme evaluation model, evaluating the secondary utilization design scheme using the waste secondary utilization scheme evaluation model, and obtaining the optimal secondary utilization design scheme. The present application automatically generates a secondary utilization scheme by identifying waste, solves the problems of low recycling rate, lack of systematic design scheme generation method, and poor scheme matching degree and feasibility in the existing secondary utilization process of construction waste, effectively improves the utilization rate of construction materials, and further helps to reduce environmental pollution and promote the green and sustainable development of the construction industry.

[0005] Optionally, the construction waste area image data is obtained, and the physical characteristics of the construction waste are identified according to the construction waste area image data, comprising the following steps: Image acquisition equipment is deployed to collect image data from the construction waste area; the image data is preprocessed to obtain standard-format image data; a physical feature extraction model is constructed and combined with the standard-format image data to obtain the physical characteristics of the construction waste. By extracting the physical characteristics of construction waste from standard image data, this method overcomes the efficiency bottlenecks and error limitations of manual identification. Using image algorithms, it rapidly extracts features such as size, shape, and material in batches, providing data support for waste classification and sorting, improving the level of preprocessing automation, and providing a quantitative basis for subsequent secondary utilization solutions (such as reorganization, processing, and recycling), avoiding the problem of poor solution adaptability caused by ambiguous feature judgment.

[0006] Optionally, the performing data preprocessing on the image data comprises the following steps: The image data is subjected to image denoising, image enhancement, and image segmentation. By pre-processing image data, the present invention can eliminate noise, correct distortion, and improve image clarity and consistency, providing a high-quality data foundation for subsequent feature recognition, reducing recognition errors, and further improving the efficiency of the present invention.

[0007] Optionally, the constructing of the physical feature extraction model includes the following steps: The present invention improves the Sparrow algorithm through a dynamic adaptive weighting algorithm; obtains optimal hyperparameters of a neural network based on the improved Sparrow algorithm, and utilizes the optimal hyperparameters to construct a physical feature extraction model. By improving the Sparrow algorithm, the present invention improves the extraction efficiency of the physical feature extraction model, further contributing to improved accuracy of the present invention's solution.

[0008] Optionally, generating a feasible secondary utilization design scheme based on the physical characteristics of the construction waste and the structural characteristics of the later-stage components of the project includes the following steps: A model for generating recycled waste components is constructed. Based on this model, reconfigurable components are generated based on the physical characteristics of the construction waste. These reconfigurable components are then screened using the structural characteristics of the post-construction components to obtain a feasible recycling design. By generating reconfigurable components and then screening them based on actual conditions to form a design, the present invention improves the comprehensiveness of the design.

[0009] Optionally, the construction of the waste secondary utilization component generation model includes the following steps: Successful cases of waste recycling at construction sites are obtained; a collaborative filtering algorithm and a content-based recommendation algorithm are integrated to construct a waste recycling component generation model framework, which is then trained using these successful cases to obtain a waste recycling component generation model. This invention, based on the collaborative filtering algorithm and content-based recommendation algorithm component model and trained using successful cases, facilitates the rapid generation of reconfigurable components.

[0010] Optionally, the method of screening the reconfigurable components by utilizing the structural characteristics of the late-stage components to obtain a feasible secondary utilization design scheme comprises the following steps: The present invention first classifies components according to their complexity and then screens the reconfigurable components based on the classification criteria. This facilitates the precise screening of reconfigurable components based on their structural characteristics, ensuring the feasibility of the solution while maximizing the value of waste materials, providing an economical and environmentally friendly solution for post-construction construction.

[0011] Optionally, the construction of the waste secondary utilization scheme evaluation model includes the following steps: A first feasibility index is calculated based on the waste utilization rate and waste value; a second feasibility index is obtained based on the compatibility of the reused components in the waste reuse scheme with the later-stage components of the project; and a third feasibility index is obtained based on the matching degree between the construction site and the reused components. Combining the first, second, and third feasibility indices, a waste reuse scheme evaluation model is constructed. This invention establishes evaluation indicators from multiple dimensions, which facilitates the accurate evaluation of waste reuse schemes.

[0012] Optionally, the step of constructing a waste secondary utilization scheme evaluation model by combining the first feasibility index, the second feasibility index, and the third feasibility index comprises the following steps: Based on a scaling method, a judgment matrix is ​​constructed based on the importance of the first, second, and third feasibility indices. Weight coefficients are obtained using this judgment matrix. A waste recycling scheme evaluation model is derived by combining these weight coefficients with the first, second, and third feasibility indices. This present invention uses an expert scaling method to calculate the weight coefficients for each evaluation indicator, converting qualitative judgments into quantitative values, further facilitating the objectivity and operability of scheme comparisons and decision-making analyses.

[0013] In a second aspect, in order to efficiently execute the method for generating a design scheme for the secondary utilization of construction waste provided by the present invention, the present invention also provides a system for generating a design scheme for the secondary utilization of construction waste, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to call the program instructions to execute the method for generating a design scheme for the secondary utilization of construction waste as described in the first aspect of the present invention. The system for generating a design scheme for the secondary utilization of construction waste provided by the present invention has a compact structure and stable performance, and is capable of stably executing the method for generating a design scheme for the secondary utilization of construction waste provided by the present invention, further enhancing the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a method for generating a design scheme for secondary utilization of construction waste provided by an embodiment of the present invention; Figure 2 This is a framework diagram of a system for generating a design scheme for secondary utilization of construction waste provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0016] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0017] See also Figure 1 In order to solve the above problems, the present invention provides a method for generating a design scheme for secondary utilization of construction waste, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Acquire image data of a construction waste area, and identify physical features of construction waste based on the image data of the construction waste area.

[0018] Specifically, the step S1 of acquiring the image data of the construction waste area and identifying the physical characteristics of the construction waste according to the image data of the construction waste area includes the following steps: S11 . Deploy image acquisition equipment, and collect image data of the construction waste area through the image acquisition equipment.

[0019] In the embodiment, the first step is to deploy the acquisition equipment. High-definition fixed cameras are strategically placed around and on top of the waste storage area. The cameras should have a resolution of at least 4K and include autofocus and optical image stabilization. They should be installed at a height that allows for a complete view of the entire waste area, typically 3-5 meters. Each camera's coverage should overlap with adjacent cameras by 15%-20% to avoid blind spots. Furthermore, the cameras should be equipped with infrared fill lights to ensure clear images at night or in low-light environments.

[0020] At other construction sites without fixed waste areas, mobile inspection robots can be deployed for regular image capture. Equipped with a 360-degree rotating camera and depth sensor, they travel along pre-set waste inspection routes at a speed of 0.5-1 meters per second, capturing an image every meter. Additionally, drones are deployed for aerial photography 2-3 times daily, maintaining a flight altitude of 50-80 meters. These drones use a combination of vertical and 45-degree angled shots to capture the overall distribution and stacking of waste.

[0021] Furthermore, a local data storage server is set up at the construction site. The collected image data is first stored locally on the server in real time and simultaneously backed up to a cloud database via wireless networks. The stored image data is named in a standardized format: "Device Type - Acquisition Time - Acquisition Location Number," for example, "Fixed Camera - 202507110830 - F01," to facilitate subsequent data search and management.

[0022] S12: performing data preprocessing on the image data to obtain image data in a standard format.

[0023] Specifically, the data preprocessing of the image data includes the following steps: First, image denoising is performed on the image data.

[0024] Gaussian filtering was used to remove Gaussian noise from the captured images, with a filter kernel size of 3×3 or 5×5 depending on the noise intensity. Salt and pepper noise was processed using a median filter with a window size of 3×3. Wavelet transform denoising was used to perform multi-scale image decomposition. High-frequency coefficients, where noise is most concentrated, were thresholded to retain useful image information and further improve image quality.

[0025] Secondly, image enhancement processing is performed on the image data.

[0026] Histogram equalization is used to enhance image contrast, making the waste features more distinct. For images with uneven lighting, the CLAHE (Contrast-Limited Adaptive Histogram Equalization) algorithm is used to avoid overly bright or dark areas and enhance image detail. The image is sharpened using the Laplacian or Sobel operator to highlight the edges and contours of the waste, paving the way for subsequent feature extraction.

[0027] Finally, image segmentation processing is performed on the image data.

[0028] A threshold-based segmentation algorithm is used to separate the waste from the background by determining an appropriate threshold based on the grayscale difference between the waste and the background. For images with less pronounced grayscale differences, a region-growing-based segmentation algorithm is used. Starting from a seed point, pixels with similar characteristics are gradually merged to form a complete waste region. Morphological processing, including erosion and dilation operations, is performed on the segmented image to remove small noisy areas and fill holes in the waste region, making the waste outline clearer and more complete.

[0029] S13: constructing a physical feature extraction model, and combining the physical feature extraction model with the standard format image data to obtain the physical features of the construction waste.

[0030] Specifically, the construction of the physical feature extraction model includes the following steps: First, the sparrow algorithm is improved by a dynamic adaptive weight algorithm.

[0031] In the sparrow algorithm, the finder is the individual with the best fitness value in the population. It is responsible for exploring potential optimal areas in the search space and providing foraging directions for the entire group. Traditional sparrow algorithms suffer from weak global search capabilities, poor local development, and a tendency to fall into local optima, resulting in insufficient search accuracy. By introducing a dynamic adaptive weighting algorithm, the finder's position update strategy is modified, and the search range is dynamically adjusted, ensuring that the algorithm covers a wider solution space and avoids falling into local optima.

[0032] Furthermore, the sparrow algorithm is improved by the dynamic adaptive weight algorithm, satisfying the following formula: in, Indicates the A sparrow in the The position at the iteration, represents the dynamic adaptive weight, , represents the initial value of the weight, represents the final value of the weight, represents the maximum number of iterations, represents the number of iterations, Indicates the A sparrow in the The position at the iteration, represents the control disturbance factor, It represents the warning value, which is a random number in [0,1]. ST represents the safety value, which is a random number in [0.5,1]. Q represents a random number that conforms to the standard normal distribution. L represents a unit vector.

[0033] In some other embodiments, a step length influencing factor may be introduced to mutate the iterative sparrow individuals. Specifically, the step length influencing factor satisfies the following formula: in, represents the step size influencing factor, represents the location parameter of the Laplace distribution probability density function, represents a random number uniformly distributed within (0,1), The step-size influencing factor is introduced to mutate the iterative crossover sparrow individuals, satisfying the following formula: in, Indicates the A sparrow in the The position after mutation at the iteration, Indicates the The position of the optimal sparrow at the iteration.

[0034] Then, optimal hyperparameters of the neural network are obtained according to the improved sparrow algorithm, and a physical feature extraction model is constructed using the optimal hyperparameters.

[0035] Specifically, the hyperparameters of the neural network to be optimized are determined. For the neural network for physical feature extraction of construction waste (such as the feature extraction network of YOLOv8), the key hyperparameters and ranges that need to be optimized are clarified. Then, based on the hyperparameter optimization of the sparrow algorithm, the hyperparameters optimized by the improved sparrow algorithm (such as learning rate 0.002, batch size 16, number of neurons 128, etc.) are substituted into the neural network structure to construct a physical feature extraction model.

[0036] Furthermore, the physical features of the construction waste are obtained by combining the physical feature extraction model and the standard format image data.

[0037] Specifically, the physical feature extraction of construction waste includes size feature extraction and state feature extraction.

[0038] Dimensional feature extraction, including: Length and Width Extraction: For the segmented waste area, the minimum enclosing rectangle algorithm is used to determine the approximate outline of the waste. The length and width of the waste are calculated based on the ratio between image pixels and actual size (pre-calibrated using a calibration plate, e.g., 100 pixels corresponds to 1 cm). For slender waste (such as rebar), the Hough line detection algorithm is used to detect the axis, and the actual length is calculated based on the pixel length of the axis.

[0039] Diameter extraction: For scrap materials with circular cross-sections (such as steel bars), a circle detection algorithm (such as Hough circle detection) is used to identify their cross-sectional contours. The actual diameter is calculated based on the pixel diameter of the contour, and the calculation result is rounded to two decimal places.

[0040] Volume estimation: For regularly shaped waste materials (such as concrete blocks), the volume is calculated based on their length, width, and height (obtained through depth sensors or estimated based on stacking height). For irregularly shaped waste materials, 3D point cloud data processing methods are used to stitch and model the point cloud data obtained by mobile inspection robots and drones to accurately calculate their volume.

[0041] State feature extraction, including: Rust degree assessment: For metal scrap such as steel bars, the color characteristics of the scrap surface in the image are analyzed and the color histogram analysis method is used to calculate the proportion of the rusted area on the scrap surface. Combined with the color depth of the rusted area (e.g., yellow-brown for light rust, reddish-brown for moderate rust, and black for severe rust), the rust degree is divided into four levels: no rust, light rust, moderate rust, and severe rust.

[0042] Integrity Assessment: For waste materials such as concrete blocks, integrity is assessed by detecting cracks and damage on their surfaces. The Canny operator is used to detect edge information in the image. Combined with crack characteristics (such as length, width, and number), integrity is categorized into three levels: intact (no visible cracks or damage), slightly damaged (cracks less than 5 cm long and less than 0.5 cm wide), and severely damaged (cracks greater than 10 cm long or with visible fragmentation).

[0043] Material feature extraction: The material of the waste is analyzed through the texture characteristics of the image. For example, wood has a unique grain pattern, and concrete has a rough surface texture. A gray-level co-occurrence matrix algorithm is used to extract texture feature parameters (such as contrast, energy, and entropy) from the image. This is then combined with a database of texture features from different materials to determine the material type of the waste.

[0044] S2. Combine the engineering BIM model and engineering progress to extract the structural characteristics of the later components of the project.

[0045] In this embodiment, based on the project's construction phases, the BIM model specifies the timeframe for the later stages of the project (e.g., from the completion of the main structure to final acceptance). The BIM software's filtering function then filters out all components involved in this phase. The filtering criteria can be set to include components with a planned construction timeframe falling within the later stages of the project, or components belonging to a construction zone that falls within the later stages of construction.

[0046] Furthermore, the parametric nature of the BIM model can be leveraged to derive component geometric parameters such as length, width, height, cross-sectional shape, and dimensions. For complex components (such as irregularly shaped prefabricated parts), 3D coordinate point cloud data can be derived from the 3D view of the BIM model, providing detailed geometric information for subsequent structural feature analysis.

[0047] Furthermore, the structural features of components in the later stages of the project are extracted.

[0048] For linear components (such as beams, columns, and steel bar skeletons), extract features such as axis length, cross-sectional dimensions (such as width × height for beams and side length for columns), and span. For steel bar skeletons, extract the spacing of the main bars, the diameter and spacing of the stirrups, and the overall length and height of the skeleton.

[0049] For planar components (such as slabs and walls), features such as planar dimensions (length × width), thickness, slope (such as inclined slabs), and boundary contour shape are extracted. For example, a floor slab constructed later has planar dimensions of 6m × 3m, a thickness of 120mm, and a rectangular boundary.

[0050] For special-shaped components, their surface curvature, volume, surface area and other features are extracted through the three-dimensional mesh data of the BIM model, and the coordinates of the key control points of the components are recorded at the same time to accurately describe their complex geometric shapes.

[0051] Extract the mechanical performance parameters of the components from the results of the structural analysis software associated with the BIM model (such as SAP2000, YJK), including the design bearing capacity (compressive, tensile, shear strength), deflection limit, crack width limit, etc. For example, the design compressive strength of a prefabricated skeleton is 30 MPa, and the deflection limit is L / 250 (L is the skeleton span).

[0052] Analyze the stress form of the component (such as tension, compression, bending, shear), and extract the corresponding stress characteristic parameters. For example, beam components mainly bear bending internal forces, and the bending moment design value and shear force design value need to be extracted; column components mainly bear axial compression, and the axial compression design value needs to be extracted.

[0053] Extract the material composition and performance parameters of the component, such as the strength grade of the steel bar (HRB400, HPB300, etc.), diameter, strength grade of the concrete (C30, C40, etc.), moisture content of the wood, compressive strength, etc. For composite components (such as reinforced concrete composite beams), the characteristic parameters of each component material need to be extracted respectively.

[0054] Extract the connection mode and connection node characteristics of the component, such as the connection between the prefabricated component and the cast-in-place part using reserved steel bar lap joint, which needs to extract the lap joint length and steel bar diameter; the bolt connection between components needs to extract the bolt type, number, spacing, etc.

[0055] S3, according to the physical characteristics of the construction waste and the structural characteristics of the late-stage component of the project, generate a feasible secondary utilization design scheme.

[0056] In the embodiment, the generation of a feasible secondary utilization design scheme according to the physical characteristics of the construction waste and the structural characteristics of the late-stage component of the project includes the following steps: S31, construct a waste secondary utilization component generation model.

[0057] Specifically, the construction of a waste secondary utilization component generation model includes the following steps: First, obtain successful cases of construction site waste secondary utilization.

[0058] Next, fuse the collaborative filtering algorithm and the content-based recommendation algorithm to construct a waste secondary utilization component generation model framework, and train it using the successful cases to obtain a waste secondary utilization component generation model.

[0059] A hybrid recommendation model that integrates collaborative filtering and content-based recommendation algorithms is adopted. The collaborative filtering algorithm can make recommendations based on the construction team's preference information for plans in historical cases, and the content-based recommendation algorithm can make recommendations based on the matching degree between waste characteristics and construction scene characteristics and secondary utilization plan characteristics. The fusion of the two algorithms can improve the accuracy and diversity of recommendations.

[0060] A training dataset is constructed using waste material characteristics and historical case studies as input and corresponding reuse scenarios as output. A gradient descent algorithm is used to optimize model parameters, minimizing the model's prediction error through multiple iterations of training. During training, cross-validation is used to evaluate model performance and adjust model parameters accordingly.

[0061] S32. Based on the waste secondary utilization component generation model, reconfigurable components are obtained according to the physical characteristics of the construction waste, and the structural characteristics of the later-stage components of the project are used to screen the reconfigurable components to obtain a feasible secondary utilization design scheme.

[0062] Specifically, the method of screening the reconfigurable components by utilizing the structural characteristics of the late-stage components to obtain a feasible secondary utilization design scheme includes the following steps: First, the late-stage components of the project are classified according to their structural characteristics.

[0063] Based on the structural form and connection method of the components, the components in the later stage of the project are divided into three complexity levels: simple, medium and complex, providing a basis for the subsequent screening of reconfigurable components: Simple components: have a single structural form (such as a lintel frame with a rectangular cross-section), no complex nodes, and are mainly composed of linear materials (such as steel bars).

[0064] Medium components: contain a small number of special-shaped structures (such as L-shaped prefabricated panel frames) and have 2-3 types of connection nodes (such as welding + bolt connections).

[0065] Complex components: have special-shaped cross-sections and multi-directional force nodes (such as space grid skeletons), requiring high-precision processing and assembly.

[0066] Then, based on the classification results, set the component screening criteria.

[0067] For simple components, structural dimensions can be adjusted by combining scrap materials (e.g., a ±5% deviation in the overall length of a skeleton can be accommodated by splicing scrap materials). Core load-bearing components (such as main reinforcement) can be provided by a single type of scrap material (e.g., rebar scrap), eliminating the need for complex combinations of multiple scrap materials. Connections are simple, allowing for the joining of scrap materials to one another and to new materials through conventional processing techniques (e.g., welding and tying).

[0068] For medium-sized components, special-shaped structures can be created by cutting and bending scrap (for example, the corners of an L-shaped frame can be formed by bending whole steel bar scrap). The performance differences between different scrap types can be compensated through structural design (for example, using high-strength scrap in some areas to compensate for the lack of low-strength scrap in other areas).

[0069] For complex components, only when the scrap material has excellent performance (such as high precision and high integrity) and the processing equipment meets the requirements will it be included in the scope of reconfiguration, usually as an alternative.

[0070] Finally, the component screening criteria are used to screen the reconfigurable components, and the feasibility of the screening results is verified to obtain a feasible secondary utilization design scheme.

[0071] For simple components, the physical characteristics of a single type of scrap are directly matched to the structural characteristics of the component. For example, a 6.2m long, 12mm diameter, uncorroded rebar scrap can be used directly as the main reinforcement for a 6m long precast lintel frame. The scrap meets the required length (there is still some excess after deducting the lap length), the diameter is consistent with the design, and the corrosion level meets the standard, so it can be used directly as the main reinforcement.

[0072] For medium-sized components, a combination of various scrap materials can be used to meet structural requirements. For example, an L-shaped precast panel frame requires 4m-long main reinforcement on the long side and 2.5m on the short side. Three rebar scraps, 2.2m and 1.8m in length, can be welded together to form a 4m-long main reinforcement (the welds are positioned away from the areas bearing the greatest stress). A 2.5m section of the 2.6m-long scrap can then be used as the short-side main reinforcement. This combination meets the L-shaped frame's size and load requirements.

[0073] When there's a slight deviation between the scrap's physical characteristics and the component's structural characteristics, the problem can be addressed by adjusting the structural design parameters. For example, if the rebar diameter is 1mm smaller than the design value, the number of rebars can be increased to maintain the same total cross-sectional area (e.g., if the original design used two φ12 rebars, change to three φ10 rebars, keeping the cross-sectional area deviation within 3%). If the deviation is significant (e.g., a diameter deviation exceeding 2mm), the component can be reassembled or replaced with a different type of scrap.

[0074] In other embodiments, a large number of component design templates and parametric design tools can be configured to select appropriate templates for adjustment and optimization based on different types and sizes of scrap. For example, reconstructing rebar scraps into a prefabricated component skeleton can automatically calculate the skeleton's structural dimensions, rebar layout, and connection methods based on parameters such as the rebar's length and diameter, ensuring that the design meets the component's mechanical properties and usage requirements.

[0075] S4. Construct a waste material secondary utilization scheme evaluation model, and use the waste material secondary utilization scheme evaluation model to evaluate the secondary utilization design scheme to obtain the optimal secondary utilization design scheme.

[0076] In the embodiment, the construction of the waste secondary utilization scheme evaluation model includes the following steps: S41. Calculate a first feasibility index based on the waste utilization rate and the waste value.

[0077] Specifically, the first feasibility index is calculated based on the waste utilization rate and the waste value, satisfying the following formula: in, represents the first feasible index, Indicates the The utilization rate of waste materials, Indicates the The original value of the waste S42. Obtain a second feasibility index based on the compatibility between the secondary utilization components in the waste secondary utilization plan and the later-stage components of the project.

[0078] Specifically, the degree of matching between the component's geometric dimensions (length, width, height, interface dimensions, etc.) and the later components is measured, and the score is calculated based on the ratio of the actual installation deviation to the allowable deviation: 100 points for a deviation ≤ 20%, 60 points for a deviation of 20% < ≤ 50%, and 0 points for a deviation > 50%.

[0079] Evaluate the coordination of mechanical parameters such as bearing capacity and stiffness between the two (such as the ratio of the bearing capacity of the secondary component to the demand of the later component): a ratio in the range of 0.9-1.1 is 100 points, 0.8-0.9 or 1.1-1.2 is 60 points, and outside this range is 0 points.

[0080] Furthermore, according to the compatibility between the secondary utilization components in the waste secondary utilization scheme and the later-stage components of the project, a second feasibility index is obtained, which satisfies the following formula: in, represents the second feasible index, represents the normalization function, The weight coefficients representing the matching and coordination, Indicates the The matching degree of the secondary utilization components, Indicates the The coordination of secondary utilization components.

[0081] By quantifying adaptability, we can avoid the subsequent construction obstructions or structural hazards caused by secondary reused components being "usable but unsuitable", and provide an accurate assessment of the engineering practicality of the plan.

[0082] S43. Obtain a third feasibility index based on the matching degree between the construction site and the secondary reused component.

[0083] Specifically, the consideration is whether the construction site space (such as stacking and processing areas) can accommodate the storage and processing needs of secondary components. If the space is fully satisfied, it will be 100 points; if partial adjustment is required (such as compressing other areas), it will be 60 points; and if the space is seriously insufficient, it will be 0 points.

[0084] Assess whether existing construction equipment and technology are compatible with the installation / processing (such as cutting, welding, hoisting, etc.) of secondary-use components. Full process compatibility is scored as 100 points, the need for minor equipment upgrades or process optimization is scored as 60 points, and the need for large-scale transformation is scored as 0 points.

[0085] Analyze the impact of the processing and installation process of secondary recycled components on the construction period of the original project. If the construction period is not affected or is shortened by ≤5%, it will be 100 points; if the construction period is extended by 5%-10%, it will be 60 points; if the construction period is extended by >10%, it will be 0 points.

[0086] Specifically, according to the matching degree between the construction site and the secondary reused component, a third feasibility index is obtained, which satisfies the following formula: in, represents the third feasible index, Respectively represent the weight coefficients of the impact of consumption capacity, processing capacity and construction period, Indicates the The absorption capacity of secondary utilization components, Indicates the The processing capability of secondary utilization components, Indicates the The impact of secondary utilization components on the construction period.

[0087] S44. Construct a waste secondary utilization program evaluation model by combining the first feasibility index, the second feasibility index, and the third feasibility index.

[0088] Specifically, first, a judgment matrix is constructed based on the scale method according to the importance of the first feasible index, the second feasible index and the third feasible index. The judgment matrix is constructed by pairwise comparison of the importance of each index by a plurality of relevant experts using the 1-9 scale method. In constructing the judgment matrix, the experts need to focus on the differences in the influence of different indexes in the whole life cycle of the project. For example, in a project with tight schedule, the importance of schedule adaptation rate and equipment utilization efficiency may be significantly improved, thereby affecting the element value of the judgment matrix. In the process of constructing the judgment matrix, the experts need to systematically analyze the relative importance between indexes in combination with the characteristics of the project, for example, municipal engineering has higher requirements for environmental indexes, which may increase the importance ratio of environmental indexes and economic indexes. At the same time, in order to ensure the rationality of the judgment matrix, cross-checking and opinion coordination of expert scoring are required to avoid the influence of extreme values on the accuracy of the weight.

[0089] Then, the weight coefficient is obtained by using the judgment matrix. Specifically, the weight of each index is calculated by the eigenvalue method, and the consistency of the judgment matrix is checked (CR<0.1 is passed).

[0090] Finally, the weight coefficient, the first feasible index, the second feasible index and the third feasible index are combined to obtain a waste secondary utilization scheme evaluation model.

[0091] Further, the waste secondary utilization scheme evaluation model is used to evaluate the secondary utilization design scheme to obtain an optimal secondary utilization design scheme.

[0092] Please refer to Figure 2 In the embodiments, in order to efficiently execute the construction waste secondary utilization design scheme generation method provided by the present application, the present application further provides a construction waste secondary utilization design scheme generation system, which comprises an input device, an output device, a processor and a memory, the input device, the output device, the processor and the memory are connected with each other, the memory contains program instructions for the steps of the construction waste secondary utilization design scheme generation method. The construction waste secondary utilization design scheme generation system of the present application has compact structure and stable performance, can stably execute the construction waste secondary utilization design scheme generation method of the present application, and further improves the overall applicability and practical application ability of the present application.

[0093] In an embodiment, the processor may be a central processing unit (CPU), which may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The input device may be used to obtain data information. The output device may be used to output the results obtained by storing the program instructions contained in the computer program in the memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0094] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0095] The embodiment further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for generating a design scheme for secondary utilization of construction waste are implemented.

[0096] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0097] In summary, the present invention solves the problems of low recycling rate, lack of systematic design scheme generation method, poor scheme matching and feasibility in the existing secondary utilization process of construction waste by identifying waste materials and automatically generating secondary utilization schemes. It effectively improves the utilization rate of construction materials, further helps to reduce environmental pollution and promote the green and sustainable development of the construction industry.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope described in the present invention.

Claims

1. A method for generating a design scheme for secondary utilization of construction waste, characterized in that: The following steps are involved: Acquiring image data of a construction waste area, and identifying physical characteristics of construction waste based on the image data of the construction waste area; Combine the engineering BIM model and engineering progress to extract the structural characteristics of the components in the later stages of the project; Generate a feasible secondary utilization design plan based on the physical characteristics of the construction waste and the structural characteristics of the later-stage components of the project; A waste material secondary utilization scheme evaluation model is constructed, and the secondary utilization design scheme is evaluated using the waste material secondary utilization scheme evaluation model to obtain the optimal secondary utilization design scheme.

2. The method for generating a design scheme for secondary utilization of construction waste according to claim 1, characterized in that: The step of acquiring image data of the construction waste area and identifying physical characteristics of the construction waste based on the image data of the construction waste area includes the following steps: deploying image acquisition equipment to collect image data of the construction waste area; performing data preprocessing on the image data to obtain image data in a standard format; A physical feature extraction model is constructed, and the physical features of the construction waste are obtained by combining the physical feature extraction model with the standard format image data.

3. The method for generating a design scheme for secondary utilization of construction waste according to claim 2, characterized in that: The data preprocessing of the image data comprises the following steps: performing image denoising processing on the image data; performing image enhancement processing on the image data; Perform image segmentation processing on the image data.

4. The method for generating a design scheme for secondary utilization of construction waste according to claim 2, characterized in that: The construction of the physical feature extraction model includes the following steps: Improve the sparrow algorithm through dynamic adaptive weight algorithm; The optimal hyperparameters of the neural network are obtained according to the improved sparrow algorithm, and a physical feature extraction model is constructed using the optimal hyperparameters.

5. The method for generating a design scheme for secondary utilization of construction waste according to claim 1, characterized in that: Generating a feasible secondary utilization design scheme based on the physical characteristics of the construction waste and the structural characteristics of the later-stage components of the project includes the following steps: Construct a model for generating components using secondary waste materials; Based on the waste secondary utilization component generation model, reconfigurable components are obtained according to the physical characteristics of the construction waste, and the structural characteristics of the late-stage components of the project are used to screen the reconfigurable components to obtain a feasible secondary utilization design scheme.

6. The method for generating a design scheme for secondary utilization of construction waste according to claim 5, characterized in that: The construction of the waste secondary utilization component generation model includes the following steps: Obtain successful cases of secondary utilization of construction site waste; The collaborative filtering algorithm and the content-based recommendation algorithm are integrated to construct a waste recycling component generation model framework, and the successful case is used for training to obtain the waste recycling component generation model.

7. The method for generating a design scheme for secondary utilization of construction waste according to claim 5, characterized in that: The method of screening the reconfigurable components by utilizing the structural characteristics of the late-stage components to obtain a feasible secondary utilization design scheme comprises the following steps: Classifying the late-stage components of the project according to their structural characteristics; Based on the classification results, set component screening criteria; The reconfigurable components are screened using the component screening criteria, and the feasibility of the screening results is verified to obtain a feasible secondary utilization design scheme.

8. The method for generating a design scheme for secondary utilization of construction waste according to claim 1, characterized in that: The construction of the waste secondary utilization program evaluation model includes the following steps: Calculate the first feasible index based on the waste utilization rate and waste value; Obtaining a second feasibility index according to the compatibility of the recycled components in the waste recycling plan with the later-stage components of the project; Obtaining a third feasibility index based on a matching degree between the construction site and the secondary reused component; Combining the first feasibility index, the second feasibility index and the third feasibility index, a waste secondary utilization program evaluation model is constructed.

9. The method for generating a design scheme for secondary utilization of construction waste according to claim 8, characterized in that: The step of combining the first feasibility index, the second feasibility index, and the third feasibility index to construct a waste secondary utilization scheme evaluation model comprises the following steps: Based on the scaling method, a judgment matrix is ​​constructed according to the importance of the first feasibility index, the second feasibility index, and the third feasibility index; Using the judgment matrix, obtaining a weight coefficient; The weight coefficient, the first feasibility index, the second feasibility index and the third feasibility index are combined to obtain a waste secondary utilization program evaluation model.

10. A system for generating a design scheme for secondary utilization of construction waste, characterized in that: The construction waste secondary utilization design scheme generation system includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the construction waste secondary utilization design scheme generation method described in any one of claims 1 to 9.

Images