A BIM-based intelligent layout method for tower clusters and walkways in large-scale public building projects

Abstract

This application provides a BIM-based intelligent layout method for tower cranes and trestles in large-scale public building projects, relating to the field of building structure construction technology. The method includes: system construction and data preparation; employing optimization algorithms to arrange tower cranes and trestles, modeling the tower crane-trestle layout problem as a mixed integer programming problem coupled with facility site selection and path planning; using a two-stage heuristic algorithm to arrange tower cranes and trestles, obtaining a Pareto optimal solution set; and simulating and verifying the tower crane and trestle layout schemes included in the Pareto optimal solution set and outputting the solution. This application constructs a tower crane-trestle collaborative layout model, aiming to minimize the total material transportation cost, total time, and total risk, simultaneously optimizing the location, model, and quantity of tower cranes and the path of trestles in three-dimensional space, forming an integrated construction logistics solution.

Description

Technical Field

[0001] This application relates to the field of building structure construction technology, and in particular to a BIM-based intelligent layout method for tower clusters and walkways in large public building projects. Background Technology

[0002] Currently, in the construction of ultra-large-scale single buildings or buildings with complex spatial structures, the efficiency of organizing horizontal and vertical material transportation is a key factor restricting the construction period and cost. Traditional construction organization design usually separates the tower crane layout from the planning of temporary roads / trestle on site.

[0003] When planning logistics systems, tower crane placement relies on experience, focusing on coverage and lifting capacity, but failing to fully consider efficient horizontal transport paths from material sources to the tower crane hooks; construction walkways / temporary roads are often planned only for connectivity, without being coordinated and optimized with the tower crane's operating radius and work efficiency.

[0004] This often leads to the following problems in actual construction: the area covered by the tower crane boom is underutilized due to excessively long horizontal transport distances; the central area of ​​the site becomes a logistics blind spot due to insufficient tower crane coverage, forcing the use of extremely inefficient multi-level transfers; and the trestle fails to effectively serve the tower crane's optimal material picking point, resulting in vehicle queues and secondary handling. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a BIM-based intelligent layout method for tower cranes and trestles in large public construction projects. By constructing a collaborative layout model of tower cranes and trestles, with the goal of minimizing the total cost, time, and risk of material transportation, the method simultaneously optimizes the position, model, and quantity of tower cranes and the path of trestles in three-dimensional space, forming an integrated construction logistics solution.

[0006] This application provides a BIM-based intelligent layout method for tower clusters and walkways in large-scale public building projects, the method comprising: S1: System construction and data preparation, including: S11: Building the three-layer architecture of the system: data layer, algorithm service layer, and application interaction layer; S12: Inputting BIM model, tower crane performance database, trestle component library, construction schedule plan, logistics parameters, and optimization rule base, and preprocessing the input data; S2: Optimize the placement of tower cranes and trestles using an optimization algorithm to obtain a Pareto optimal solution set, including: S21: Modeling the tower crane-trestle placement problem as a mixed integer programming problem coupled with facility location and path planning; S22: Using a two-stage heuristic algorithm to place tower cranes and trestles; S221: Selecting initial positioning points for tower cranes near each key area of ​​the construction zone, and selecting the minimum satisfactory model from the tower crane model database based on the material weight and location information of that key area; S222: Starting from the site entrance, using the minimum spanning tree algorithm to generate an initial trestle path skeleton connecting the site entrance and each key tower crane, with the goal of serving the key tower cranes located in the key areas; S223: Using a multi-objective genetic algorithm to optimize the initial positioning points and minimum satisfactory models of the tower cranes in step S221 and the initial trestle path skeleton in step S222, obtaining a Pareto optimal solution set. Within this solution set, the tower crane and trestle placement scheme corresponding to each solution cannot be simultaneously surpassed by other solutions in terms of cost, time, and risk. S3: Simulate and verify the tower crane and trestle layout schemes included in the Pareto optimal solution set and output the schemes, including: S31: Select a preset number of preferred schemes from the Pareto solution set and perform high-fidelity 4D logistics simulation verification; S32: Output the final intelligent layout results of the tower crane and trestle, including: tower crane-trestle collaborative layout general drawing, tower crane special scheme, trestle special construction drawing, construction logistics organization scheme, engineering quantity and cost budget list, 4D simulation video and analysis report.

[0007] In one possible implementation, in step S12, The tower crane performance database includes: basic parameters, the efficient working radius range of each tower crane model, the single cycle operation time of each tower crane model at each working radius, the total converted cost of installation and dismantling of each tower crane model, and the constraints on the maximum free end height, minimum number of attachments, and attachment point spacing for each tower crane model. The stacker component library includes: standard units, support systems, connecting components, and for each stacker component, its component ID, three-dimensional geometric dimensions, load-bearing capacity, unit length / weight cost, and estimated installation time. The optimization rule base includes: an optimization objective function (minimizing total cost and safety risk index while maximizing logistics efficiency); tower crane layout safety and performance constraints (including minimum horizontal distance constraints between tower cranes, minimum vertical height difference constraints between tower cranes, and mandatory area coverage constraints); trestle geometry and safety constraints (including maximum longitudinal slope constraints of trestle, minimum planar turning radius constraints of trestle, and minimum clearance constraints between trestle and existing structures); and tower crane-trestle collaborative operation constraints (including trestle end and tower crane high-efficiency zone coordination constraints, and trestle traffic capacity constraints). In step S12, the input data is preprocessed, including: The BIM model is parsed, components are identified and attributes are attached, and the construction area is divided into multiple grids; The construction schedule is broken down into sub-projects and associated with BIM model components or construction areas to form a 4D model, which is used to drive changes in material requirements at each stage.

[0008] In one possible implementation, step S21: Modeling the tower crane-trestle layout problem as a mixed-integer programming problem coupled with facility location-path planning, including: S211: Define the decision variable: whether it is at the candidate point i Arrangement model is k Is the tower crane in position? m and n Build plank roads between them and select the plank road type. p From the supply point s To the point of demand d Material flow distribution; S212: Construct a mathematical model for an optimization objective function that minimizes the weighted sum of total cost, total time, and total risk; wherein, a mathematical model for the total cost is constructed based on tower crane cost, trestle cost, and transport vehicle cost; a mathematical model for the total time is constructed based on the estimated transport time of each material using a function of total flow vector and trestle network structure variables; and a mathematical model for the total risk is constructed based on tower crane collision risk index, trestle traffic flow conflict point risk index, and structural risk index. S213: Set the safety and performance constraints of the tower crane layout, the geometric and safety constraints of the trestle, and the constraints of the tower crane-trestle collaborative operation in step S12 as constraints of the mathematical model of the optimization objective function.

[0009] In one possible implementation, step S221: Selecting preliminary positioning points for the tower crane near each key area of ​​the construction area, and selecting the minimum acceptable model from the tower crane model database based on the material weight and location information of that key area, including: S2211: Decompose the sub-projects from the construction schedule, and associate the sub-projects with BIM model components or construction areas to form a 4D model; S2212: Based on the 4D model, calculate the logistics urgency index of each grid in the construction area according to the material demand of each grid in each construction stage and the distance between the grid and the material supply point. Based on the logistics urgency index of each grid in the construction area, select multiple key areas from the construction area. S2213: For each critical area, the weighted centroid of that critical area is used as the initial candidate point for the tower crane's positioning; S2214: The maximum single-piece weight requirement, the farthest service distance requirement, and the comprehensive lifting intensity reflecting continuous operation requirements of this critical area are determined as the lifting demand characteristics; S2215: Based on the aforementioned hoisting requirements, determine the lifting capacity constraints, coverage radius constraints, work efficiency constraints, and work intensity adaptation constraints of the tower crane, and select the smallest model that meets the tower crane constraints from the tower crane model database. S2216: With the goal of avoiding permanent structures and existing facilities and reserving space for the connection of the trestle, the preliminary positioning candidate points determined in step S2213 are fine-tuned within the allowable adjustment range. The final position after fine-tuning satisfies all tower crane constraints in step S2215. The preliminary positioning points and minimum satisfying models of the key tower cranes in each key area are output.

[0010] In one possible implementation, step S222: Starting from the site entrance and aiming to serve the key tower cranes located in the critical area, a preliminary stack path skeleton connecting the site entrance and each key tower crane is generated using the minimum spanning tree algorithm, including: S2221: Construct an undirected graph, taking the site entrance, the initial positioning points of all key tower cranes, and a set of optional intermediate turning points for bypassing obstacles as vertices of the undirected graph, and determining the Euclidean distance between two vertices as edge weights. S2222: The minimum spanning tree algorithm is used to determine the minimum spanning tree that satisfies the obstacle avoidance constraint, the connectivity guarantee constraint that connects any two tower cranes or connects any tower crane to the site entrance, and minimizes the sum of edge weights in the undirected graph, as the initial stackway path skeleton.

[0011] In one possible implementation, step S223: A multi-objective genetic algorithm is used to optimize the initial positioning point of the tower crane in step S221, the minimum required model, and the initial stacker path skeleton in step S222, to obtain a Pareto optimal solution set, including: S2231: Using the initial positioning point and minimum required model of the tower crane in step S221, the initial stack path skeleton in step S222, and the flow allocation value as the initial population, the initial population is encoded into a chromosome. S2232: Decode the chromosome into a crane and trestle layout scheme, and evaluate the fitness of the crane and trestle layout scheme; S2233: The algorithm employs genetic operations including tournament selection, simulated binary crossover, and polynomial mutation. It uses the fitness values ​​of the tower crane and trestle layout schemes to iteratively evolve the population and outputs a set of Pareto optimal solutions.

[0012] In one possible implementation, step S2232: decoding the chromosome into a crane and trestle layout scheme and evaluating the fitness of the crane and trestle layout scheme includes: S22321: Perform a rapid geometric and safety rule check on the tower crane and trestle layout scheme. If the scheme violates any (important) constraint, assign a preset low fitness value to the tower crane and trestle layout scheme. S22322: Based on the tower crane model and quantity, trestle type and total length, evaluate the total static cost of the tower crane and trestle layout scheme; S22323: Based on the decoded network of the tower crane and trestle layout scheme and the flow allocation gene in the chromosome, run the logistics simulation model to simulate the material transportation on a typical construction day, evaluate the efficiency of the tower crane and trestle layout scheme during operation, and output key performance indicators, including total daily transportation time, tower crane utilization rate, and saturation of key sections of the trestle. Based on the key performance indicators, evaluate the total dynamic time cost of the tower crane and trestle layout scheme. S22324: The sum of the total static cost and the total dynamic time cost is determined as the final total cost of the tower crane and trestle layout scheme; S22325: After normalizing the total cost, total time, and total risk, synthesize the fitness value of the tower crane and trestle layout scheme according to the target weight.

[0013] In one possible implementation, step S22322: Based on the tower crane model and quantity, the trestle type and total length, evaluate the total static cost of the tower crane and trestle layout scheme, including: S223221: According to model k Calculate the total installation cost, total dismantling cost, daily rental fee, and estimated total rental days of the tower crane, as well as the model and quantity of the tower crane in the tower crane and trestle layout plan, and calculate the relevant costs of the tower crane; S223222: According to type p Calculate the unit length construction cost of the trestle, the additional cost of the connection construction at the nodes, and the type and length of the trestle in the tower crane and trestle layout scheme, and calculate the relevant costs of the trestle; S223223: According to model k The cost of the foundation and supporting facilities is calculated based on the area occupied by the tower crane foundation or outriggers, the average unit price of site hardening and foundation treatment, the fixed estimated cost of other temporary supporting facilities, and the model and quantity of the tower cranes in the tower crane and trestle layout plan. S223224: The sum of the costs related to the tower crane, the costs related to the trestle, and the costs of the foundation and supporting facilities is determined as the total static cost.

[0014] In one possible implementation, step S22323: Based on the decoded network of the tower crane and trestle layout scheme and the flow allocation genes in the chromosome, run a logistics simulation model to simulate material transportation on a typical construction day, evaluate the efficiency of the tower crane and trestle layout scheme during operation, and output key performance indicators, including total daily transportation time, tower crane utilization rate, and saturation of key trestle sections. Based on the key performance indicators, evaluate the total dynamic time cost of the tower crane and trestle layout scheme, including: S223231: Input the simulation cycle, material requirements, vehicle parameters, and tower crane parameters into the logistics simulation model; S223232: Run the simulation logic of the logistics simulation model to estimate the vehicle transportation time based on the length of the walkway from the supply point to the tower crane, the vehicle's full-load speed, the vehicle's empty-load speed, the average loading time, and the vehicle's average queuing waiting time. S223233: According to model k The tower crane in the range r The service rate of the tower crane is calculated based on the single cycle operation time. The material arrival rate of the tower crane is calculated based on the daily material transportation plan, single vehicle load capacity, and typical workday length. The average queuing time in front of the tower crane is calculated based on the service rate and material arrival rate. S223234: Calculate the total daily transportation time based on the daily material transportation plan, single vehicle load capacity, vehicle transportation time, and average queuing time in front of the tower crane; S223235: Calculate the average utilization rate of tower cranes based on service rate and material arrival rate; S223236: Calculate the saturation of the key cross-section of the trestle based on the daily material transportation plan, the load capacity of a single vehicle, and the design passage capacity of the arc segment through which the material passes. S223237: Calculate the total dynamic time cost based on the total daily transportation time, the total number of construction days of the plan, the weighted average hourly cost of transportation vehicles, the average utilization rate of the tower crane group, and the average hourly rental fee of the tower crane group.

[0015] In one possible implementation, step S31: Selecting a preset number of preferred solutions from the Pareto solution set and performing high-fidelity 4D logistics simulation verification, including: S311: Based on the precise coordinates and parameters of any tower crane and trestle layout scheme in the Pareto solution set, automatically place parametric tower crane families and trestle standard section families in the BIM model to finely reconstruct the BIM model of the construction facilities. S312: Import the BIM model of the construction facilities into the simulation software and integrate the simulation environment; S313: Import construction schedule and material requirements plan, define the operation logic of vehicle and tower crane operators, run simulation, and simulate the construction process with a preset construction period; S314: Simulate and output the analysis chart of the tower crane and trestle layout scheme, provide feedback and fine-tune the logistics bottlenecks in the analysis chart, and simulate and verify the fine-tuned scheme again.

[0016] This application provides a BIM-based intelligent layout method for tower cranes and trestles in large-scale public building projects. Building upon traditional tower crane layout methods, it introduces construction trestles as a key decision variable. By constructing a collaborative layout model of "tower cranes and trestles," and aiming to minimize the total material transportation cost / time, it simultaneously optimizes the location, model, and quantity of tower cranes, as well as the path of trestles, in three-dimensional space, forming an integrated construction logistics solution. This solution is particularly suitable for large-scale public facility construction projects such as airports.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 One of the flowcharts of the intelligent layout method for large-scale public building projects based on BIM and the walkway provided in the embodiments of this application is shown. Figure 2 The second flowchart illustrates the intelligent layout method for large-scale public building projects based on BIM, including tower clusters and walkways, provided in an embodiment of this application. Figure 3 The third flowchart illustrates the intelligent layout method for large-scale public building projects based on BIM, including tower clusters and walkways, provided in this application embodiment. Figure 4 The fourth flowchart illustrates the intelligent layout method for large-scale public building projects based on BIM, including tower clusters and walkways, provided in this application embodiment. Figure 5 The fifth flowchart illustrates the intelligent layout method for large-scale public building projects based on BIM, including tower clusters and walkways, provided in this application embodiment. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] Currently, in the construction of ultra-large-scale single buildings (such as large transportation hubs and industrial plants) or building projects with complex spatial structures (such as stadiums and convention centers), the efficiency of organizing horizontal and vertical material transportation is a key factor restricting the construction period and cost. Traditional construction organization design usually separates the tower crane layout from the planning of temporary roads / walkways on site.

[0022] When planning logistics systems, tower crane placement relies on experience, focusing on coverage and lifting capacity, but failing to fully consider efficient horizontal transport paths from material sources to the tower crane hooks; construction walkways / temporary roads are often planned only for connectivity, without being coordinated and optimized with the tower crane's operating radius and work efficiency.

[0023] This often leads to the following problems in actual construction: the area covered by the tower crane boom is underutilized due to excessively long horizontal transport distances; the central area of ​​the site becomes a logistics blind spot due to insufficient tower crane coverage, forcing the use of extremely inefficient multi-level transfers; and the trestle fails to effectively serve the tower crane's optimal material picking point, resulting in vehicle queues and secondary handling.

[0024] Existing research on tower crane layout based on BIM (Building Information Modeling) has introduced algorithm optimization, but none of them have optimized the "tower crane-stairway" as a coupled system as a whole, resulting in inherent defects in logistics efficiency in the generated solutions.

[0025] To address the aforementioned issues, this application provides a BIM-based intelligent layout method for tower cranes and trestle systems in large-scale public construction projects. Building upon traditional tower crane layout methods, it introduces construction trestle systems as a key decision variable. By constructing a collaborative layout model of "tower cranes and trestle systems," and aiming to minimize the total material transportation cost / time, it simultaneously optimizes the location, model, and quantity of tower cranes, as well as the path of the trestle systems, in three-dimensional space, forming an integrated construction logistics solution.

[0026] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] See Figure 1 As shown, Figure 1 One of the flowcharts for a BIM-based intelligent layout method for large-scale public building projects and walkways, provided in this application embodiment, includes the following steps: S1: System construction and data preparation; S2: Use an optimization algorithm to arrange tower cranes and trestles to obtain the Pareto optimal solution set; S3: Simulate and verify the layout schemes of tower cranes and trestles included in the Pareto optimal solution set and output the scheme.

[0028] See Figure 2 As shown, Figure 2 The second flowchart of the intelligent layout method for large-scale public building projects based on BIM, provided in the embodiments of this application, includes the following steps: S11: Construct a three-layer architecture for the system: data layer, algorithm service layer, and application interaction layer; S12: Input the BIM model, tower crane performance database, trestle component library, construction schedule, logistics parameters, and optimization rule library, and preprocess the input data.

[0029] See Figure 3 As shown, Figure 3 The third flowchart of the intelligent layout method for large-scale public building projects based on BIM, provided for the embodiments of this application, includes the following steps: S21: Model the tower crane-trestle layout problem as a mixed integer programming problem coupled with facility location selection and path planning; S22: A two-stage heuristic algorithm is used to deploy tower cranes and trestle walkways.

[0030] See Figure 4 As shown, Figure 4 The fourth flowchart of the intelligent layout method for large-scale public building projects based on BIM, provided in the embodiments of this application, includes the following steps: S31: Select a preset number of optimal solutions from the Pareto solution set and perform high-fidelity 4D logistics simulation verification; S32: Output the final intelligent layout results of the tower crane and trestle system, including: overall layout plan of tower crane-trestle collaborative layout, tower crane special plan, trestle special construction drawing, construction logistics organization plan, engineering quantity and cost budget list, 4D simulation video and analysis report.

[0031] See Figure 5 As shown, Figure 5 The fourth flowchart of the intelligent layout method for large-scale public building projects based on BIM, provided in the embodiments of this application, includes the following steps: S221: Select preliminary positioning points for tower cranes near each key area of ​​the construction area, and select the minimum compliant model from the tower crane model database based on the material weight and location information of that key area; S222: Starting from the site entrance, with the goal of serving the key tower cranes located in the critical area, use the minimum spanning tree algorithm to generate a preliminary stack path skeleton connecting the site entrance and each key tower crane. S223: Using a multi-objective genetic algorithm, the initial positioning point and minimum required model of the tower crane in step S221 and the initial trestle path skeleton in step S222 are optimized to obtain a Pareto optimal solution set. The tower crane and trestle layout scheme corresponding to each solution in this solution set cannot be surpassed by other solutions in terms of cost, time and risk.

[0032] Part 1: System Construction and Data Preparation (Implementation Step S1) 1.1 System Architecture (Implementation Step S11) This system adopts a three-tier architecture: Data layer: Stores all input data, including BIM models, tower crane performance databases, trestle component libraries, construction schedules, and optimization rule bases. It employs a combination of a relational database (MySQL) and a file system (for storing model files).

[0033] Algorithm Service Layer: Deploys core optimization algorithm modules, a geometric calculation engine, and a logistics simulation core. Developed in Python, it integrates algorithm libraries such as Google OR-Tools and NetworkX, and provides services through a RESTful API.

[0034] Application Interaction Layer: Provides a graphical interface for web-based or desktop plugins (such as the Revit plugin) for users to import data, set parameters, start optimization, view 3D results and simulation animations.

[0035] 1.2 Detailed data input and preprocessing (implementation step S12) 1.2.1 Deep processing of BIM model: Identify structural components in the BIM model and calculate their theoretical weight based on their geometric dimensions and material types; divide the ground projection of the construction area in the BIM model into grid cells of a preset size, and assign attributes to the grid cells including the construction stage to which they belong, the main material requirements of that stage, and whether it is an obstacle zone.

[0036] The model needs to integrate architecture, structure, and site layout disciplines, with an accuracy of LOD300 (component level).

[0037] Automatic component identification and attribute attachment: The system automatically identifies major structural components (columns, beams, slabs, and walls) by parsing the BIM model and calculates their theoretical weight based on their geometric dimensions and material type (read from attributes or according to national standard default values). For critical precast components (such as large steel structures and precast wall panels), the weight attributes must be manually verified.

[0038] Construction area gridding: The entire construction area is projected onto the ground and divided into 1m×1m grids. Each grid cell is assigned attributes: the construction stage it belongs to, the main material requirements (tons) for that stage, and whether it is an obstacle zone (permanent buildings, foundation pits, existing facilities).

[0039] 1.2.2 Tower Crane Performance Database: In addition to basic parameters (such as lifting torque, maximum lifting capacity, lifting height, lifting speed, slewing speed, etc.), the following must be included: ① High-efficiency working range: such as the range where the rated lifting capacity reaches more than 80% of the maximum value, which is the key to cost calculation.

[0040] Formula for calculating the efficient working range:

[0041] Parameter definition: : Index of tower crane models.

[0042] :model The efficient working range of a tower crane is a set of continuous or discrete range intervals.

[0043] : The actual working radius of the tower crane (unit: meters).

[0044] :model The minimum working radius of the tower crane (unit: meters).

[0045] :model The maximum working radius of the tower crane (unit: meters).

[0046] :model The tower crane in the range Rated lifting capacity (unit: tons) at the location. This is regarding the radius. The function or discrete data points are usually defined by the tower crane performance table (lifting capacity-amplitude curve).

[0047] :model The maximum rated lifting capacity of the tower crane (usually at the minimum radius, in tons).

[0048] The high-efficiency working threshold coefficient is a preset percentage value (e.g., =0.8), indicating that the high-efficiency working area requires a lifting capacity of not less than 80% of the maximum lifting capacity.

[0049] ② Time curves for each cycle of operation: including the time for hook lifting, slewing, hook lowering, and empty return, which are the basis for calculating hoisting efficiency.

[0050] Formulas for calculating the cycle time of each order:

[0051] Parameter definition: :model The tower crane in the range At this point, a vertical drop is completed. The total time required for a single cycle of hoisting operations (in seconds).

[0052] Lifting / lowering time. Depends on the lifting speed with or without the load. and vertical movement height (Unit: meter). Usually approximated as... .

[0053] Turning time. Depends on the model. The tower crane in the range angular velocity at the point (Unit: degrees / second, or radians / second) and the required angle of rotation. (Unit: degrees, or radians). Usually approximated as... Angular velocity of rotation It may vary depending on the amplitude and the weight being lifted.

[0054] Material unloading and unhooking time. A relatively fixed value, including time for fine-tuning alignment, worker unhooking, etc. (unit: seconds).

[0055] : Empty hook return time. This typically includes the time for the empty hook to lift / lower and rotate back to the starting point, and can be modeled as... Its speed may differ from the load.

[0056] Note: The calculation formula for the cycle time of each range implicitly includes the range. The potential impact on lifting speed and slewing speed, parameters This indicates that different models have different performance curves.

[0057] ③ Installation and dismantling plan and timeline: Formula for calculating installation and dismantling costs:

[0058] Parameter definition: :model The total converted cost of tower crane installation and dismantling (unit: yuan, or other currency unit).

[0059] : The hourly rate for installation and dismantling work teams (including special operation personnel) (unit: yuan / person·day or yuan / team·day).

[0060] : Number of people in the installation and dismantling work team or the number of standard work teams (unit: people or work teams).

[0061] Rental unit price of auxiliary machinery (such as truck cranes) (unit: yuan / unit·day).

[0062] :model The standard installation period for tower cranes (unit: days).

[0063] :model The standard dismantling period for tower cranes (unit: days).

[0064] ④ Attachment point requirements and spacing: affect the spatial relationship with the main structure.

[0065] Key determination formula: and

[0066] The spacing between attachment points must meet the following requirements:

[0067] Parameter definition: :model The maximum free end height of the tower crane (the height from the last attachment point or the top surface of the foundation to the lower hinge point of the jib, in meters).

[0068] :model The maximum allowable attachment spacing (in meters) for tower cranes is determined by the tower crane design specifications.

[0069] Minimum number of attachment lanes required (unit: lanes).

[0070] Total installation height required for the tower crane (measured from the top of the foundation, unit: meters).

[0071] :model The independent lifting height (or initial installation free height, unit: meters) of the tower crane.

[0072] : The function to round up.

[0073] Actual design spacing between attachment points (unit: meters).

[0074] :model The minimum allowable attachment spacing (in meters) for tower cranes is determined by structural strength and stability.

[0075] 1.2.3 Stacker / Temporary Road Component Library: Standard units: such as 6m×3m standard segments, 3m×3m widened intersection segments, uphill and downhill sections with standard slope (≤8%), and turning platforms.

[0076] Support system: Parametric families of different forms such as steel pipe column support and truss support, with load-bearing capacity tables at different heights.

[0077] Connecting components: guardrails, lighting, drainage and other ancillary facilities.

[0078] Each component has a unique component ID, three-dimensional geometric dimensions, load-bearing capacity, cost per unit length / weight, and estimated installation time.

[0079] 1.2.4 Logistics Parameters and Constraints: Define the logistics parameters of logistics carriers and materials, the optimization objective function (minimizing total cost and safety risk index while maximizing logistics efficiency), and the constraints on tower crane layout safety and performance, trestle geometry and safety, and tower crane-trestle collaborative operation.

[0080] Transport vehicles: Define the dimensions, full load weight, empty and full load speed, and turning radius of different types of vehicles (such as 8-ton trucks and 3-ton forklifts).

[0081] Construction schedule: Imported in Microsoft Project or P6 format, broken down into sub-projects, and associated with BIM model components or construction areas to form a 4D model, which drives changes in material requirements at each stage.

[0082] Detailed optimization rules: defined through configuration files.

[0083] The embodiments of this application employ a multi-objective optimization method, whose comprehensive optimization objective function F It consists of a weighted average of three sub-objectives:

[0084] Parameter definition: Total cost, including all related costs such as fixed investment, leasing, operation and maintenance of tower cranes and trestle walkways (unit: yuan).

[0085] Logistics efficiency is defined as the product of the total amount of materials lifted (tons) by all tower cranes within the evaluation period and their average transport distance (meters), divided by the total operating time (hours). =Σ(lifting weight × lifting radius) / total time (unit: ton-meter / hour).

[0086] The safety risk index is a dimensionless comprehensive evaluation value. The higher the value, the greater the safety risk of the proposed solution. Its calculation integrates multiple dimensions such as collision probability, structural stability risk, and traffic conflict point density.

[0087] , , : These are the normalized weight coefficients for the three sub-objectives, all of which are greater than 0. The specific values ​​are set by the decision-maker based on the project priority.

[0088] The objective function aims to minimize and , while maximizing .

[0089] The mathematical model for the core constraints is as follows: ① Safety and performance constraints of tower crane layout a) Minimum horizontal distance constraint between tower cranes: Two arbitrary tower cranes TC i and TC j Distance from the center point on the horizontal projection plane Must meet:

[0090] in, and Tower cranes TC i and TC j Independent height or current installation height (unit: meters).

[0091] b) Minimum vertical height difference constraint between tower cranes: If the working areas of the two tower cranes overlap, the height difference between the top surfaces of their standard tower sections Must meet:

[0092] in, The height of a standard section of a single tower crane (unit: meters).

[0093] c) Must have regional coverage constraints: Let the set of critical construction areas that must be covered be . The area jointly covered by all tower cranes in the plan is Then the coverage Must meet:

[0094] in, This indicates the area of ​​the region being calculated.

[0095] ② Stack road geometry and safety constraints a) Maximum longitudinal slope constraint of the walkway: For any segment of the plank road S k Its longitudinal slope i k The maximum value must not be exceeded.

[0096] b) Minimum planar turning radius constraint for the walkway: For any planar curve on the walkway, its centerline turning radius Must meet:

[0097] c) Minimum clearance constraint between the walkway and existing structures: Minimum distance between any part of the walkway (including the railings) and the surface of surrounding permanent or temporary structures. Must meet:

[0098] ③ Constraints on coordinated operation of tower crane and trestle a) Coordination constraints between the end of the walkway and the high-efficiency zone of the tower crane: Set up a plank road T m The coordinates of the final unloading point are P m The tower crane serving this point TC n In amplitude The lifting capacity at the location is Its maximum lifting capacity is Then it must satisfy:

[0099] in, The efficient working threshold coefficient (usually taken as...) =0.7~0.8), to ensure that the end of the walkway is within the efficient working range of the tower crane.

[0100] b) Boardwalk capacity constraints (congestion prevention constraints): At any construction stage t For any node on the stack network v (e.g., intersections, merging points), estimated average vehicle waiting time The threshold must not be exceeded:

[0101] The waiting time It can be obtained through flow analysis based on queuing theory or discrete event simulation models.

[0102] Part Two: Implementation of the Core Optimization Algorithm (Implementation Step S2) 2.1 Problem Modeling (Implementation Step S21) The placement problem is modeled as a mixed integer programming problem coupled with facility location and path planning.

[0103] 2.1.1 Decision Variables (Implementation Step S211): Boolean variables x ik : Whether to deploy a tower crane of model k at candidate point i.

[0104] Boolean variables y mmp : Whether to build a stack between positions m and n, and select type p.

[0105] Continuous variables : The material flow distribution value from supply point s to demand point d.

[0106] 2.1.2 Objective Function (Implementation Step S212): Minimize(α +β +γ ), where α, β, and γ are weighting coefficients set by the decision-maker. Minimize(·) is the minimization function.

[0107] The objective function is to minimize the weighted sum of total cost, total time, and total risk.

[0108] Total cost =Σ(Tower crane fixed cost + Daily operating cost) +Σ(Trestle road construction cost + Maintenance cost) + Transport vehicle cost.

[0109] Total Time =Σ(transport time for each material), estimated using a simulation model based on the predicted flow and route.

[0110] Total Risk Quantitatively assess the risks of the proposed solution, such as the tower crane collision risk index and the number of traffic conflict points on the trestle.

[0111] The mathematical model for total time and total risk is explained below: (1) Total time The mathematical model is expressed as:

[0112] in: Indicates the stage t Transporting unit materials from the supply point s Transport to the point of demand d The estimated transportation time.

[0113] This time is the total flow vector. and stack network structure variables y ={ y mmp The function of}.

[0114] function The determination of this needs to be calculated using an embedded, simplified network traffic simulation model (such as a congestion model based on queuing theory or time-varying shortest path).

[0115] Parameter definition: In the given y Below, from s arrive d The shortest feasible path length (in meters).

[0116] Average speed of a vehicle in a non-congested environment (unit: meters per hour).

[0117] :path The set of all arc segments (m, n) traversed.

[0118] : in the stage t Due to traffic This results in additional congestion delays (in hours) on arc (m,n). This delay can be estimated using a speed-flow relationship model (such as the US National Highway Traffic Safety Administration's BPR function):

[0119] : The length of the arc segment (m,n).

[0120] : 0-1 indicates the variable, when the flow rate is... The value is 1 when using arc segment (m,n), and 0 otherwise.

[0121] The design capacity (in tons per hour) of arc segment (m,n), and the type of walkway built upon it. p Decide.

[0122] , Calibration parameters for congestion delay models (typically) =0.15、 =4).

[0123] (2) Total Risk The mathematical model can be expressed as:

[0124] 1) Tower crane collision risk index :

[0125] Tower crane i and j The horizontal projection distance of the center of the tower (unit: meters).

[0126] The dynamic safety distance threshold (unit: meters) is calculated based on the height of the two objects.

[0127] : Positive part function. When the actual distance When the value is below the safety threshold, this item is positive, resulting in a risk penalty.

[0128] Tower crane i and j The conditional probability of interference occurring within the work cycle can be estimated based on the overlapping area of ​​its turning range and the level of work activity.

[0129] Weighting coefficient for tower crane collision risk.

[0130] 2) Risk index of traffic conflict points on the boardwalk :

[0131] The set of all intersections, merging points, and other nodes in the stack network.

[0132] : in the stage t Through nodes v Estimated total flow (unit: tons / stage).

[0133] :node v The design throughput capacity (in tons per stage) is related to the type and geometry of the stack connecting the node.

[0134] The squared term of the flow-capacity ratio is used to amplify the risk contribution of high-load nodes, and more sensitively reflect the safety risks (such as rear-end collisions and side collisions) caused by congestion.

[0135] : Weighting coefficient for traffic conflict risk.

[0136] The colon (:) is an indicator function symbol that checks whether the content within the parentheses is affirmative; it returns 1 if affirmative and 0 if negative. Here, it's used to determine the "node". v Is it located from the supply point? s To the point of demand d path superior".

[0137] 3) Structural Risk Index :

[0138] : in the stage t The actual load (including self-weight, vehicle load, and material load) borne by the arc segment (m,n) of the walkway.

[0139] : The structural design load-bearing capacity of type p stack.

[0140] This penalty applies when the actual load exceeds the design bearing capacity. Its weighting coefficient.

[0141] 2.1.3 Constraints (Implementation step S213): It covers all geometric constraints, safety constraints, capacity constraints, and logistics balance constraints defined in 1.2.4.

[0142] 2.2 Algorithm Flow (Two-Phase Heuristic Algorithm) (Implementation Step S22) Due to the large scale and complexity of the problem, it is impractical to solve it using an exact algorithm. Therefore, the following algorithm flow is designed in the embodiments of this application: 2.2.1 Rapid Generation of Initial Scheme (Greedy Algorithm + Rule Reasoning) (Implementation Step S221) Steps S221 and S222 implement the first stage of the two-stage heuristic algorithm, namely, the rapid generation of the initial scheme.

[0143] 2.2.1.1 Import the construction schedule into Microsoft Project or P6 format, break it down into sub-projects, associate it with BIM model components or construction areas to form a 4D model, which is used to drive changes in material requirements at each stage (implementation step S2211).

[0144] 2.2.1.2 Identify core logistics needs: Based on the 4D model, identify the Top N areas with "large material demand and far from the site entrance" throughout the entire construction period, as key areas that must be served by "stairway + tower crane" (implementation step S2212).

[0145] Key region identification model: The construction site is discretized into a set of grids. Each grid In each construction phase There is a definite material requirement. (Unit: tons)

[0146] Define a "logistics urgency index" for each grid. :

[0147] Parameter definition: Indicates average demand intensity. This indicates the distance to the nearest entrance.

[0148] Total number of construction stages.

[0149] : grid g The center coordinate vector ( , ).

[0150] Material supply point s (e.g., the coordinate vector of the main entrance of the site, the storage yard).

[0151] Total number of material supply points.

[0152] Euclidean distance.

[0153] Key area selection: according to Sort the values ​​from largest to smallest and select the first few. N Each grid constitutes a key area set. Each of these key areas It can contain one or more adjacent heights Value grid.

[0154] 2.2.1.3 Preliminary positioning of key tower cranes (implementation step S2213) For each key area Determine the candidate points for the center of the tower crane service area.

[0155] Calculate the critical region The weighted center of mass is used as a preliminary candidate point for the tower crane's positioning:

[0156] 2.2.1.4 Calculate the hoisting demand characteristics of key areas (implementation step S2214) Maximum single-piece weight requirement:

[0157] in, For the network in phase t The weight of the heaviest single component that needs to be hoisted.

[0158] Maximum service distance requirement: based on candidate points Using the circle as the center, calculate the coverage of the critical area. Minimum radius required for all internal grids:

[0159] Overall lifting strength:

[0160] Comprehensive lifting strength This reflects the need for continuous operation, expressed in tons per stage.

[0161] 2.2.1.5 Select the smallest satisfactory model from the database (implementation step S2215) From tower crane model database In the selection process, choose the smallest model that meets all of the following criteria (usually sorted by cost or maximum lifting capacity). :

[0162] Constraints that must be satisfied simultaneously: ① Lifting capacity constraints: ,in Model k In amplitude r The rated lifting capacity below, This is a safety factor (usually 1.1~1.15).

[0163] ②Coverage radius constraint: .in, Model k The maximum working range.

[0164] ③ Work efficiency constraints: Or at least satisfy That is, the working range should be located as close as possible to the high-efficiency zone. A value of 0.6 to 0.7 is acceptable.

[0165] ④ Work intensity adaptation constraints: .in Model k In amplitude The theoretical cycle operation capacity (unit: tons / hour). To ensure that the tower crane's capacity meets the flow requirements, the maximum reasonable utilization rate is preset (e.g., 0.7).

[0166] 2.2.1.6 Fine-tune the final position (implementation step S2216) Taking into account obstacles and subsequent collaborative optimization, the initial location point is... It can be adjusted within a certain range around it. (e.g., radius is) Fine-tuning was performed within the circle to avoid permanent structures, existing facilities, or to reserve space for walkway connections. The adjusted position... All constraints in step S2215 need to be re-verified.

[0167] Output: For each key region R n Output a preliminary layout plan: , ), that is, the final position and the selected minimum satisfactory model.

[0168] Steps S2212-S2216 are used to achieve the placement of critical tower cranes: near each critical area, based on the weight of the materials and the distance, the smallest tower crane of the required model is selected from the database to initially determine the location and ensure that the area is covered.

[0169] 2.2.2 Main Stack Generation (Implementation Step S222) Starting from the site entrance, with the goal of serving these key tower cranes, the "minimum spanning tree" algorithm is used to generate a preliminary stackway path skeleton connecting the site entrance and each key tower crane, avoiding major obstacles.

[0170] The calculation formula and steps of the "minimum spanning tree" algorithm are as follows: 2.2.2.1 Graph Construction (Implementation Step S2221) Define an undirected graph G =( V , E ): Vertex set V : Includes source point v 0 (site entrance), preliminary positioning points of all key tower cranes { v 1, v 2,.... v N}, and a set of optional intermediate turning points (for avoiding obstacles).

[0171] Edge set E Connect any two vertices v i and v j edge e ij The prerequisite is that the line segment does not intersect with any obstacle polygon (geometric collision detection is required).

[0172] edge weight : Defined as the Euclidean distance between two vertices (or the actual path length after considering slope correction):

[0173] in, As vertices v i The coordinates.

[0174] 2.2.2.2 Core Algorithm (Taking Prim's Algorithm as an Example) (Implementation Steps S2222) The goal is to find the minimum spanning tree. T Minimize the total weight .

[0175] initialization: Visited vertex set:U ={ v 0} (starting from the venue entrance); Unvisited vertex set: ; Minimum Priority Queue Q Store all connections U and The edge ( u , v ), key value Sort.

[0176] Iterative process (until) U = V (or all key points are accessed): ①From Q Extract the edge with the smallest weight from the... e min =( u , v ),in u ∈ U , .

[0177] ② Place the vertices v join in U , will the side e min Add to spanning tree T .

[0178] ③ will with v Connected, and the other vertex is at V All edges in ' are added to the priority queue. Q .

[0179] Output: Minimum spanning tree T This is the initial skeleton of the stack path. It is a set of edges: T ={ e ij}

[0180] This set of edges connects the site entrance to all key tower crane points and has the shortest total length.

[0181] Mathematical expression of critical constraints: ① Obstacle avoidance constraint: For candidate edges e ij It must meet the following requirements:

[0182] in It is a collection of obstacles.

[0183] ② Connectivity Guarantee: The algorithm guarantees the undirected graph. GIt is connected (i.e., there is at least one barrier-free path between any two tower crane points or between the tower crane points and the site entrance).

[0184] 2.2.3 Fine-tuning of the collaborative scheme (multi-objective genetic algorithm) (implementation step S223) Step S223 implements the second stage of the two-stage heuristic algorithm, namely, the fine optimization of the collaborative scheme.

[0185] 2.2.3.1 Encoding (Implementation step S2231) The skeleton scheme generated in the first stage, completed in steps S221 and S222, is used as part of the initial population. A chromosome code includes a tower crane part (e.g., positioning point, minimum satisfaction model, etc.), a stackway part (e.g., stackway path skeleton), and a flow allocation part (e.g., flow allocation value, etc.), using a mixed code of real numbers, integers, and permutations.

[0186] 2.2.3.2 Fitness assessment (implementation step S2232) 2.2.3.2.1 Decoding and Feasibility Check (Implementation Step S22321) The chromosomes are decoded into specific tower crane and walkway layouts. First, a rapid geometry and safety rule check is performed, and schemes that violate important constraints (such as tower crane collisions) are directly assigned extremely low fitness.

[0187] The logistics network cost is calculated using steps S22322 and S22323.

[0188] 2.2.3.2.2 Static cost calculation: Calculate the total cost of equipment and facilities based on the tower crane model and quantity, trestle type and total length (implementation step S22322).

[0189] This section calculates fixed and variable costs directly related to the physical configuration of the solution, but does not involve dynamic operations.

[0190] 2.2.3.2.2.1 Tower crane related costs C tower (Implementation steps S223221)

[0191] Parameter definition: {0,1}: Decision variable, at position i Arrangement Model k The value is 1 if there is a tower crane, otherwise it is 0.

[0192] :model k Total cost of tower crane installation (including foundation, installation and dismantling machinery, and labor).

[0193] :model k Total cost of dismantling the tower crane.

[0194] :model k Tower crane daily rental fee (shift fee) (unit: yuan / day).

[0195] : The estimated total number of days for tower crane rental (determined based on the total construction period and the attached climbing plan).

[0196] 2.2.3.2.2.2 Costs Related to the Plank Road C trestle (Implementation steps S223222)

[0197] Parameter definition: {0,1}: Decision variables, at node m and n The building type is p The stack length is 1.

[0198] :node m and n The distance between them (unit: meters).

[0199] :type p The construction cost per unit length of the boardwalk (RMB / meter), including materials and labor.

[0200] :type p Additional costs associated with the construction of connecting structures at nodes of the trestle (such as ramps, intersections, and reinforcement of supports).

[0201] 2.2.3.2.2.3 Cost of basic and supporting facilities (Implementation steps S223223)

[0202] Parameter definition: :model k The floor area occupied by the tower crane foundation or outriggers (unit: square meters).

[0203] Average unit price for site hardening and foundation treatment (unit: yuan / square meter).

[0204] The fixed estimated cost of other temporary supporting facilities (such as temporary fencing, drainage, and lighting) during the tower crane installation of the entire project.

[0205] 2.2.3.2.2.4 Total Static Cost Formula: C static = C tower + C trestle + C base (Implementation steps S223224) 2.2.3.2.3 Dynamic logistics simulation (implementation steps S22323) Based on the decoded "tower crane-trestle" network and the flow allocation genes in the chromosome, a simplified, discrete-event-based logistics simulation model is run. This model simulates material transport on a typical construction day (or critical phase): vehicles depart from the supply point as planned, travel along designated trestle lines to the serving tower crane, queue for loading and unloading, and are then lifted by the tower crane to the work site. The simulation outputs metrics such as "total transport time," "tower crane utilization rate," and "trestle critical section saturation."

[0206] This section evaluates the efficiency of the solution during operation through simulation and outputs key performance indicators.

[0207] 2.2.3.2.3.1 Simulation Input and Basic Assumptions (Implementation Steps S223231) Simulation cycle: one typical construction day (unit: T day Hour).

[0208] Material requirements: Daily material transportation plan, i.e., from the supply point s To the point of demand d Daily traffic (Unit: tons / day)

[0209] Vehicle parameters: fleet size N veh Single vehicle load capacity Q veh (Unit: tons), No-load speed v empty Full load speed v load (Unit: meters per minute).

[0210] Tower crane parameters: Model k In amplitude r Single cycle operation time (Unit: minutes / cycle), the calculation method is as described in the aforementioned formula.

[0211] Steps S223232 to S223233 are used to implement the core simulation logic and calculate key indicators. The simulation model can be regarded as a queuing network, and the core is to calculate the average operation time of the system under steady state.

[0212] 2.2.3.2.3.2 Vehicle transportation time T veh ( s , d (Implementation steps S223232)

[0213] From the supply point s To service tower crane i (Responsible for the requirements) d The length of the stack path (in meters).

[0214] Average loading time (unit: minutes).

[0215] The average queuing time at supply points or narrow sections of road can be estimated using the M / M / 1 queuing model. ,in For traffic intensity, For material arrival rate, For service rate (through capacity).

[0216] 2.2.3.2.3.3 Tower crane service time and queuing (implementation steps S223233) No. Service rate of each tower crane: (Unit: cycles / hour).

[0217] No. Material arrival rate of each tower crane: (Unit: train trips / hour), of which For the first A set of demand points for tower crane services.

[0218] Average queuing time in front of tower cranes (in minutes):

[0219] Steps S223234 to S223236 are used to achieve the simulation output indicators.

[0220] 2.2.3.2.3.4 Total daily transportation time (Unit: vehicle-hour) (Implementation steps S223234):

[0221] 2.2.3.2.3.5 Average utilization rate of tower cranes (Implementation steps S223235):

[0222] 2.2.3.2.3.6 Saturation of critical sections of the walkway (implementation step S223236):

[0223] Indicator flow Should arcs be used? a (This is 1).

[0224] Arc segment a Design capacity (unit: train trips / day).

[0225] 2.2.3.2.3.7 Cost / Time Conversion: Convert the total transportation time in the simulation into time cost based on the machine shift unit price (implementation step S223237).

[0226] The dynamic time metrics obtained from the simulation are converted into monetary costs so that they can be added to the static costs.

[0227] Total dynamic time cost formula:

[0228] Parameter definition: : Total daily transportation time output by simulation (unit: vehicle-hour).

[0229] : The total number of construction days to which the plan applies.

[0230] Weighted average hourly cost of transport vehicles (unit: yuan / vehicle·hour), including fuel consumption, driver, and maintenance.

[0231] Average utilization rate of tower crane group .

[0232] The total number of tower cranes in the tower crane group.

[0233] Average hourly rental rate for tower crane clusters Unit: Yuan / unit·hour.

[0234] 2.2.3.2.4 Mathematical model for final total cost: C total =C static + C dynamic (Implementation steps S22324) 2.2.3.2.5 Fitness Value Synthesis: After normalizing the total cost, total time, and total risk, a fitness value is synthesized according to the objective function weight coefficients (implementation step S22325): Fitness = 1 / (α C_norm +β T_norm +γ R_norm); C_norm represents the normalized total cost; T_norm represents the normalized total time; R_norm represents the normalized total risk.

[0235] 2.2.3.3 Genetic operations: The population is iteratively evolved (e.g., 500-1000 generations) using operations such as tournament selection, simulated binary crossover (SBX), and polynomial mutation (implementation step S2233).

[0236] Tournament selection method analysis steps: Objective: To select superior individuals from the current population to participate in reproduction in order to produce the next generation.

[0237] Input: Current population P current Population size M Tournament Scale K (generally K =2 or 3).

[0238] step: ① Initialization: Create an empty mating pool P mating .

[0239] ② Execute the tournament: Repeat the following steps M Repeat this process until the mating pool is full: a. Random sampling: from the current population P current Random selection (with or without replacement) K Individuals constitute a small tournament group.

[0240] b. Determining the winner: In this tournament group, the fitness values ​​of individuals are compared (in multi-objective optimization, this is typically calculated based on non-dominated ranking and crowding). The individual with the best fitness is selected as the winner.

[0241] c. Place in the mating pool: Make a copy of the winning individual and place it in the mating pool. P mating middle.

[0242] ③ Output: The size is also the same. M mating pool P mating This includes the better individuals selected through competition.

[0243] Simulated binary crossover analysis steps: Objective: To explore the solution space by exchanging genes between two parent individuals selected from the mating pool to generate two new offspring individuals.

[0244] Input: Chromosome vectors p1 and p2 (real-number encoding) of two parent individuals, and crossover probability. p c Distribution index (Controlling the degree of similarity between offspring and parents, The larger the size, the closer the offspring are to the parent.

[0245] step: ① Determine whether to cross over: Generate a random number r ∈[0,1]. If r > p c If the condition is not met, the parent generation will be directly copied into the child generation, and the process will end.

[0246] ② Perform crossover operation on each gene locus: For the first gene locus on the chromosome... i One gene (variable), execute: a. Generate a random number u i ∈[0,1].

[0247] b. Calculate the expansion factor β qi :

[0248] c. Generate offspring gene values:

[0249]

[0250] in , It is the genetic value of the father. , It is the gene value of the offspring.

[0251] ③ Boundary handling: Inspection , Check if the variable is within its domain. If it is out of bounds, restrain it at the boundary.

[0252] ④ Output: Two new offspring chromosome vectors c1 and c2.

[0253] Steps for polynomial variation analysis: Objective: To introduce new genetic material by subjecting offspring individuals to low-probability, low-amplitude random perturbations of their genes, thereby maintaining population diversity and avoiding local optima.

[0254] Input: Chromosome vector c of offspring individuals (usually derived from crossover operation), mutation probability p m Distribution index (Control the amplitude of the disturbance, The larger the value, the smaller the disturbance.

[0255] step: ① Determine whether a mutation occurs at each gene locus: For the first gene locus... i Genes generate random numbers r i ∈[0,1]. If r i > p m If the gene locus does not change, it is directly preserved.

[0256] ② Perform the mutation operation (if) r i ≤ p m ): a. Calculate the normalized perturbation :

[0257] in u i ∈[0,1] is a newly generated random number. , , , These are the lower and upper bounds of the gene.

[0258] b. Apply perturbation to generate the mutated gene values:

[0259] ③ Boundary handling: Ensure exist[ , Within the range.

[0260] ④ Output: The mutated chromosome vector .

[0261] Pareto front analysis: As it is a multi-objective optimization, the algorithm will eventually output a set of "non-dominated solutions," that is, the Pareto optimal solution set. Each solution cannot be surpassed by any other solution simultaneously in terms of cost, time, and risk.

[0262] Part 3: 3D simulation verification and solution output (implementation step S3).

[0263] 3.1 High-fidelity 4D logistics simulation (implementation step S31) Select one or more optimal solutions from the Pareto solution set and perform high-fidelity simulation verification.

[0264] 3.1.1 Model Refinement Reconstruction (Implementation Step S311): In BIM software (such as Revit), based on the precise coordinates and parameters of the scheme, automatically place parametric tower crane families and trestle standard section families to generate a complete construction facility BIM model.

[0265] 3.1.2 Simulation Environment Integration (Implementation Step S312): Import the model into professional construction simulation software (such as Synchro, Fuzor) or a simulation platform built using a game engine (such as Unity).

[0266] 3.1.3 Detailed simulation operation (implementation step S313): Import complete, time-segmented construction schedules and detailed material requirements plans.

[0267] Define the operational logic for vehicle and tower crane operators (e.g., empty vehicle goes to the end of the trestle to wait, fully loaded vehicle goes to the storage yard, tower crane prioritizes the lifting of materials with long waiting times, etc.).

[0268] Run simulations to model construction processes that can take weeks or even months.

[0269] 3.1.4 Bottleneck Identification and Solution Fine-tuning (Implementation Step S314): The simulation will output rich analytical charts: daily work cycle diagram of the tower crane, heat map of traffic flow in each section of the trestle, distribution map of average vehicle waiting time, and material inventory level change chart. Based on these charts, engineers can intuitively identify logistics bottlenecks (e.g., excessively long vehicle queues under tower crane #1 every morning at 9:00 AM), and then perform "feedback-fine-tuning": for example, fine-tuning the tower crane's working hours, adding a temporary junction point on the trestle, or adjusting the supply time of some materials. The fine-tuned solution can then be quickly verified through simulation again.

[0270] 3.2 Automated output of results (implementation step S32) The system automatically generates a complete set of deliverables: Overall layout plan of "tower crane-trestle": CAD format, including all coordinates, elevations, and model labels.

[0271] Tower crane specific plan: including foundation positioning diagram, attachment point detail diagram, climbing plan, and anti-collision measures (the plan automatically calculates the interference area of ​​each tower crane and formulates a staggered jacking plan).

[0272] Specialized construction drawings for the boardwalk include: plan layout, elevation, calculation sheets for the supporting structure, and detailed construction drawings of important nodes (such as connections with the structure and turns).

[0273] Construction logistics organization plan: It specifies in detail the transportation routes, vehicles used, departure frequency, tower crane division of labor, and emergency dispatch plan for each construction stage and various materials in text and chart form.

[0274] Work Quantity and Cost Budget List: Automatically calculates the work quantity of all tower cranes, trestle and ancillary facilities in the plan, and generates a budget based on the unit price in the database.

[0275] 4D Simulation Videos and Analysis Reports: Videos that intuitively demonstrate the operation of the solution, along with simulation analysis reports containing all KPI data.

[0276] This application provides a BIM-based intelligent layout method for tower cranes and trestles in large public building projects. This method, based on BIM and intelligent algorithms, enables the coordinated and automatic layout of tower cranes and trestles. Compared to previous separate, manual layout methods that relied on human experience, this method improves in terms of scientific rigor, economy, safety, and feasibility, specifically in the following three aspects: ① From “fragmented planning” to “collaborative optimization of logistics systems”.

[0277] In previous methods, tower crane placement and trestle planning were typically completed independently by different personnel or at different stages, resulting in a significant disconnect between the two. Tower cranes primarily cover buildings, while trestle systems connect sites, often leading to contradictions such as "tower cranes cover the area but materials cannot be transported in" or "trestle systems are built but tower cranes cannot reach them," creating logistical silos. This application's embodiment treats tower cranes as vertical transfer nodes and trestle systems as horizontal transport arteries, constructing them into a unified construction logistics network. Through intelligent algorithms, its spatial location, quantity specifications, and service relationships are simultaneously optimized, ensuring seamless and efficient integration of "horizontal transport - vertical hoisting" from the source, fundamentally solving the material transportation challenges of large or deep-land projects.

[0278] ②From “trial and error based on experience” to “quantitative optimization and simulation rehearsal”.

[0279] Traditional solutions rely on engineers' experience-based calculations and two-dimensional imagination, making it difficult to quantify and compare the merits of different solutions and to foresee real bottlenecks during dynamic construction. This application's embodiments establish a multi-objective optimization model encompassing cost, time, and safety, enabling automated exploration and quantitative comparison of a vast number of solutions in three-dimensional space. This allows for the discovery of more economical combinations that transcend the limitations of experience (such as replacing a large-tonnage tower crane with a combination of a small-tonnage tower crane and a trestle). More importantly, through high-fidelity 4D logistics simulation, the entire process of vehicle traffic, tower crane operations, and material turnover can be pre-simulated, accurately revealing and optimizing dynamic bottlenecks such as traffic congestion and equipment waiting times. This allows the solution to undergo "stress testing" before construction begins, significantly improving its reliability and first-time success rate.

[0280] ③ From “passive risk avoidance” to “proactive intrinsic safety design”.

[0281] Manual layout control of safety is delayed and localized, relying heavily on post-event collision checks and qualitative safety briefings, making it difficult to comprehensively assess complex risks such as dynamic interference between multiple tower cranes and conflicts between walkways and structures. This application's embodiment quantifies safety rules (such as tower crane anti-collision distances, safe distances between walkways and foundation pits, and vehicle turning space) into hard constraints for the algorithm, ensuring that each generated solution automatically meets safety baselines. During optimization, the system proactively calculates the tower crane rotation envelope and the walkway vehicle clearance, actively avoiding most spatial conflict risks from the design stage. This achieves an intrinsic safety improvement from "passive inspection and correction" to "proactive design assurance," significantly reducing on-site safety hazards and the probability of work stoppages for rectification.

[0282] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A BIM-based intelligent layout method for tower clusters and walkways in large-scale public building projects, characterized in that, The method includes: S1: System construction and data preparation, including: S11: Building the three-layer architecture of the system: data layer, algorithm service layer, and application interaction layer; S12: Inputting BIM model, tower crane performance database, trestle component library, construction schedule plan, logistics parameters, and optimization rule base, and preprocessing the input data; S2: Optimize the placement of tower cranes and trestles using an optimization algorithm to obtain a Pareto optimal solution set, including: S21: Modeling the tower crane-trestle placement problem as a mixed integer programming problem coupled with facility location and path planning; S22: Using a two-stage heuristic algorithm to place tower cranes and trestles; S221: Selecting initial positioning points for tower cranes near each critical area of ​​the construction zone, and selecting the minimum satisfactory model from the tower crane model database based on the material weight and location information of that critical area; S222: Starting from the site entrance, using the minimum spanning tree algorithm to generate an initial trestle path skeleton connecting the site entrance and each critical tower crane, with the goal of serving the critical tower cranes located in the critical areas; S223: Using a multi-objective genetic algorithm to optimize the initial positioning points and minimum satisfactory models of the tower cranes in step S221 and the initial trestle path skeleton in step S222, to obtain a Pareto optimal solution set; S3: Simulate and verify the tower crane and trestle layout schemes included in the Pareto optimal solution set and output the schemes, including: S31: Select a preset number of preferred schemes from the Pareto solution set and perform high-fidelity 4D logistics simulation verification; S32: Output the final intelligent layout results of the tower crane and trestle, including: tower crane-trestle collaborative layout general drawing, tower crane special scheme, trestle special construction drawing, construction logistics organization scheme, engineering quantity and cost budget list, 4D simulation video and analysis report.

2. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 1, is characterized in that... In step S12, The tower crane performance database includes: basic parameters, the efficient working radius range of each tower crane model, the single cycle operation time of each tower crane model at each working radius, the total converted cost of installation and dismantling of each tower crane model, and the constraints on the maximum free end height, minimum number of attachments, and attachment point spacing for each tower crane model. The stacker component library includes: standard units, support systems, connecting components, and for each stacker component, its component ID, three-dimensional geometric dimensions, load-bearing capacity, unit length / weight cost, and estimated installation time. The optimization rule base includes: optimization objective function, tower crane layout safety and performance constraints, trestle geometry and safety constraints, and tower crane-trestle collaborative operation constraints; In step S12, the input data is preprocessed, including: The BIM model is parsed, components are identified and attributes are attached, and the construction area is divided into multiple grids; The construction schedule is broken down into sub-projects and associated with BIM model components or construction areas to form a 4D model, which is used to drive changes in material requirements at each stage.

3. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 2, is characterized in that... Step S21 specifically includes: S211: Define the decision variable: whether it is at the candidate point i Arrangement model is k Is the tower crane in position? m and n Build plank roads between them and select the plank road type. p From the supply point s To the point of demand d Material flow distribution; S212: Construct a mathematical model for an optimization objective function that minimizes the weighted sum of total cost, total time, and total risk; wherein, a mathematical model for the total cost is constructed based on tower crane cost, trestle cost, and transport vehicle cost; a mathematical model for the total time is constructed based on the estimated transport time of each material using a function of total flow vector and trestle network structure variables; and a mathematical model for the total risk is constructed based on tower crane collision risk index, trestle traffic flow conflict point risk index, and structural risk index. S213: Set the safety and performance constraints of the tower crane layout, the geometric and safety constraints of the trestle, and the constraints of the tower crane-trestle collaborative operation in step S12 as constraints of the mathematical model of the optimization objective function.

4. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 1, is characterized in that... Step S221 specifically includes: S2211: Decompose the sub-projects from the construction schedule, and associate the sub-projects with BIM model components or construction areas to form a 4D model; S2212: Based on the 4D model, calculate the logistics urgency index of each grid in the construction area according to the material demand of each grid in each construction stage and the distance between the grid and the material supply point. Based on the logistics urgency index of each grid in the construction area, select multiple key areas from the construction area. S2213: For each critical area, the weighted centroid of that critical area is used as the initial candidate point for the tower crane's positioning; S2214: The maximum single-piece weight requirement, the farthest service distance requirement, and the comprehensive lifting intensity reflecting continuous operation requirements of this critical area are determined as the lifting demand characteristics; S2215: Based on the aforementioned hoisting requirements, determine the lifting capacity constraints, coverage radius constraints, work efficiency constraints, and work intensity adaptation constraints of the tower crane, and select the smallest model that meets the tower crane constraints from the tower crane model database. S2216: With the goal of avoiding permanent structures and existing facilities and reserving space for the connection of the trestle, the preliminary positioning candidate points determined in step S2213 are fine-tuned within the allowable adjustment range. The final position after fine-tuning satisfies all tower crane constraints in step S2215. The preliminary positioning points and minimum satisfying models of the key tower cranes in each key area are output.

5. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 1, is characterized in that... Step S222 specifically includes: S2221: Construct an undirected graph, taking the site entrance, the initial positioning points of all key tower cranes, and a set of intermediate turning points for bypassing obstacles as vertices of the undirected graph, and determining the Euclidean distance between two vertices as edge weights. S2222: The minimum spanning tree algorithm is used to determine the minimum spanning tree that satisfies the obstacle avoidance constraint, the connectivity guarantee constraint that connects any two tower cranes or connects any tower crane to the site entrance, and minimizes the sum of edge weights in the undirected graph, as the initial stackway path skeleton.

6. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 1, is characterized in that... Step S223 specifically includes: S2231: Using the initial positioning point and minimum required model of the tower crane in step S221, the initial stack path skeleton in step S222, and the flow allocation value as the initial population, the initial population is encoded into a chromosome. S2232: Decode the chromosome into a crane and trestle layout scheme, and evaluate the fitness of the crane and trestle layout scheme; S2233: Using genetic operations including tournament selection, simulated binary crossover, and polynomial mutation, the population is iteratively evolved using the fitness values ​​of the tower crane and trestle layout schemes, and a set of Pareto optimal solutions is output.

7. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 6, is characterized in that... Step S2232 specifically includes: S22321: Perform a rapid geometric and safety rule check on the tower crane and trestle layout scheme. If the scheme violates any constraint, assign a preset low fitness value to the tower crane and trestle layout scheme. S22322: Based on the tower crane model and quantity, trestle type and total length, evaluate the total static cost of the tower crane and trestle layout scheme; S22323: Based on the decoded network of the tower crane and trestle layout scheme and the flow allocation gene in the chromosome, run the logistics simulation model to simulate the material transportation on a typical construction day, evaluate the efficiency of the tower crane and trestle layout scheme during operation, and output key performance indicators, including total daily transportation time, tower crane utilization rate, and saturation of key sections of the trestle. Based on the key performance indicators, evaluate the total dynamic time cost of the tower crane and trestle layout scheme. S22324: The sum of the total static cost and the total dynamic time cost is determined as the final total cost of the tower crane and trestle layout scheme; S22325: After normalizing the total cost, total time, and total risk, synthesize the fitness value of the tower crane and trestle layout scheme according to the target weight.

8. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 7, is characterized in that... Step S22322 specifically includes: S223221: According to model k Calculate the total installation cost, total dismantling cost, daily rental fee, and estimated total rental days of the tower crane, as well as the model and quantity of the tower crane in the tower crane and trestle layout plan, and calculate the relevant costs of the tower crane; S223222: According to type p Calculate the relevant costs of the trestle by including the construction cost per unit length of the trestle, the additional cost of the connection construction at the nodes, and the type and length of the trestle in the tower crane and trestle layout scheme. S223223: According to model k The cost of the foundation and supporting facilities is calculated based on the area occupied by the tower crane foundation or outriggers, the average unit price of site hardening and foundation treatment, the fixed estimated cost of temporary supporting facilities, and the model and quantity of the tower cranes in the tower crane and trestle layout plan. S223224: The sum of the costs related to the tower crane, the costs related to the trestle, and the costs of the foundation and supporting facilities is determined as the total static cost.

9. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 7, is characterized in that... Step S22323 specifically includes: S223231: Input the simulation cycle, material requirements, vehicle parameters, and tower crane parameters into the logistics simulation model; S223232: Run the simulation logic of the logistics simulation model to estimate the vehicle transportation time based on the length of the walkway from the supply point to the tower crane, the vehicle's full-load speed, the vehicle's empty-load speed, the average loading time, and the vehicle's average queuing waiting time. S223233: According to model k The tower crane in the range r The service rate of the tower crane is calculated based on the single cycle operation time. The material arrival rate of the tower crane is calculated based on the daily material transportation plan, single vehicle load capacity, and typical workday length. The average queuing time in front of the tower crane is calculated based on the service rate and material arrival rate. S223234: Calculate the total daily transportation time based on the daily material transportation plan, single vehicle load capacity, vehicle transportation time, and average queuing time in front of the tower crane; S223235: Calculate the average utilization rate of tower cranes based on service rate and material arrival rate; S223236: Calculate the saturation of the key cross-section of the trestle based on the daily material transportation plan, the load capacity of a single vehicle, and the design passage capacity of the arc segment through which the material passes. S223237: Calculate the total dynamic time cost based on the total daily transportation time, the total number of construction days of the plan, the weighted average hourly cost of transportation vehicles, the average utilization rate of the tower crane group, and the average hourly rental fee of the tower crane group.

10. The intelligent layout method for large-scale public building projects based on BIM, as described in claim 1, is characterized in that... Step S31 specifically includes: S311: Based on the precise coordinates and parameters of any tower crane and trestle layout scheme in the Pareto solution set, automatically place parametric tower crane families and trestle standard section families in the BIM model to finely reconstruct the BIM model of the construction facilities. S312: Import the BIM model of the construction facilities into the simulation software and integrate the simulation environment; S313: Import construction schedule and material requirements plan, define the operation logic of vehicle and tower crane operators, run simulation, and simulate the construction process with a preset construction period; S314: Simulate and output the analysis chart of the tower crane and trestle layout scheme, provide feedback and fine-tune the logistics bottlenecks in the analysis chart, and simulate and verify the fine-tuned scheme again.

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