Building construction method based on MIC

Abstract

The invention relates to the technical field of building construction, in particular to an MIC-based building construction method, which comprises the following steps: S1: based on a building information model; s2, embedding a self-repairing material layer and a fireproof layer in each module, and generating a block chain displacement identification code for each module; s3, planning a module transportation path according to the real-time traffic data; s4, simulating a foundation condition through a digital twin system, adjusting parameters of a magnetic type interface, and projecting a positioning datum line by using an AR system; s5, the module is grabbed through the assembly robot; s6, assembling data are collected through an Internet of Things sensor, the digital twin model is updated, and the node completion degree is verified based on a block chain intelligent contract; s7, the built-in flexible solar thin film of the module is unfolded, and an inter-module energy sharing network is established; and S8, predicting the service life of the module through a digital twin system, and starting an enzyme catalytic decomposition program for the degradable module. The assembly precision and the resource utilization rate are remarkably improved, and building industrialization is promoted to be upgraded to be intelligent and low-carbon.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to a building construction method based on MIC. Background Art

[0002] MiC building (Modular Integrated Construction), also known as modular integrated building, is a new construction method that disassembles a building into independent modules, completes construction processes such as structure, decoration, water and electricity, and equipment pipelines in a factory, and then quickly combines and assembles them into an overall building through reliable connection technologies at the construction site.

[0003] With the development of social economy and the progress of science and technology, MIC building technology has been significantly developed both at home and abroad. Through the method of factory prefabrication and on-site assembly, MIC buildings have achieved rapid construction, quality improvement, and enhanced sustainability in the construction industry. Abroad, MIC buildings have become an ideal choice for addressing emergency needs, post-disaster reconstruction, and large-scale events, and have shown broad application prospects in fields such as residential, office, and commercial facilities. In China, as the core technology in the 4.0 era of prefabricated buildings, MIC buildings are driving the upgrade of the construction industry from construction to manufacturing, forming a green building industry.

[0004] However, the current MIC building technology is not yet fully mature, especially in terms of module division, seismic design, and assembly precision control, there are technical bottlenecks. (1) In terms of modularization: Since a perfect standardization and normalization system has not been established in the field of MIC buildings, it is difficult to achieve compatibility and interchangeability between modules produced by different manufacturers. For example, mismatched interfaces and insufficient assembly precision between modules all affect the quality and performance of the overall building. Although progress has been made in factory prefabrication of MIC buildings, module division still faces inefficiency problems, resulting in time waste during the assembly process. (2) In terms of seismic performance: The research and application of MIC buildings in seismic performance are not yet sufficient, especially in earthquake-prone areas. Therefore, while ensuring assembly precision, improving the seismic performance of buildings is a major challenge currently faced. (3) In terms of assembly precision: Due to the complex construction site environment and the different management levels at the construction site, some construction units lack construction management experience in MIC buildings, which will directly affect problems such as error accumulation and resource waste during the construction of MIC buildings, resulting in a decline in the quality of the overall building.

[0005] Therefore, although MIC building technology has achieved certain achievements at the current stage, it still faces many problems and challenges. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention specifically adopts the following technical solutions.

[0007] Design a MIC-based building construction method, which specifically includes the following steps:

[0008] S1: Based on the building information model, generate a module division plan through a multi-objective collaborative optimization algorithm, and preset an adaptive seismic structure at the module connection nodes;

[0009] S2: Embed a self-healing material layer and a fireproof layer in the module, and generate a blockchain displacement identification code for each module;

[0010] S3: Plan the module transportation route according to real-time traffic data;

[0011] S4: Simulate the foundation conditions through the digital twin system, adjust the magnetic adsorption interface parameters, and use the AR system to project the positioning reference line;

[0012] S5: Use the assembly robot to grab the module, position it with the magnetic adsorption interface, trigger the self-healing material to compensate for the assembly error, and synchronously calibrate the seismic damping parameters;

[0013] S6: Collect assembly data through the Internet of Things sensors and update the digital twin model, and verify the node completion degree based on the blockchain smart contract;

[0014] S7: Unfold the flexible solar thin film built into the module and establish an energy sharing network between the modules;

[0015] S8: Predict the module life through the digital twin system and start the enzyme-catalyzed decomposition program for the degradable module.

[0016] Preferably, the implementation of the multi-objective collaborative optimization algorithm in step S1 includes:

[0017] a) Set the weight coefficients of cost, construction period, and carbon emissions as α = 0.4, β = 0.3, γ = 0.3;

[0018] b) Use the improved NSGA-II algorithm to generate the Pareto optimal solution set, with a population size of 200 and 500 iterations;

[0019] c) Embed a shape memory alloy sleeve at the module connection node, with an initial stiffness set to 1.8×10 6 N / m.

[0020] Preferably, the preparation of the self-healing material in step S2 includes:

[0021] a) Incorporate 2.5 wt% of microcapsule self-healing agent into the concrete, with a capsule diameter of 50 - 200 μm, and the core material is a mixture of epoxy resin and curing agent;

[0022] b) The unique blockchain identification code is generated by the SHA-256 algorithm, and the data structure includes the material hash value and the IPFS quality inspection report address.

[0023] Preferably, the implementation of dynamic logistics scheduling in step S3 includes:

[0024] a) The path planning uses an improved ant colony algorithm, with the pheromone evaporation coefficient ρ = 0.3 and the heuristic factor β = 2;

[0025] b) Piezoelectric ceramic arrays are installed on the transport vehicles, with the power generation per kilometer ≥ 18 Wh, and the electric energy is stored in a 300F graphene supercapacitor.

[0026] Preferably, the adjustment of the magnetic adsorption interface parameters in step S4 includes:

[0027] a) A ground point cloud model is generated by 3D laser scanning, and the registration error with the BIM model is < 3 mm;

[0028] b) The preset magnetic field intensity of the electromagnet array is 0.5 - 1.2 T, and the AR system uses PID control to compensate for the projection error, with the proportional coefficient Kp = 0.8.

[0029] Preferably, the robot collaborative assembly in step S5 includes:

[0030] a) A six-axis robotic arm is equipped with a force / torque sensor, and the impedance control parameters are set to a virtual mass of 5 kg and a damping of 80 N·s / m;

[0031] b) When the module docking deviation > 5 mm, the shape memory alloy is triggered to heat up to 35 °C, with a heating rate of 8 °C / s.

[0032] Preferably, the construction of the energy sharing network in step S7 includes:

[0033] a) CIGS flexible photovoltaic modules are used, with a single-module laying area ≥ 2.5 m 2 , and the conversion efficiency is 19.3%;

[0034] b) A 48V DC microgrid is established, and the control target of the circulating current suppression algorithm is that the circulating current < 3% of the rated current.

[0035] Preferably, the triggering condition of the enzyme-catalyzed decomposition program in step S8 is:

[0036] a) The ambient temperature ≥ 35 °C and the humidity ≥ 70% last for 72 hours;

[0037] b) An LSTM neural network is used to predict the remaining life of the module, with an input feature dimension of 28 and 64 neurons in the hidden layer.

[0038] Preferably, during the assembly process in step S5, when the detected crack width ≥ 0.15 mm, the microcapsules are activated to release the repair agent, and the repair rate ≥ 92%; in the S6 progress monitoring, stress data is collected at 100 Hz through the LoRaWAN sensor network, and the abnormal working condition identification time ≤ 47 seconds.

[0039] Preferably, the magnetic interface allows for a ±5 cm position adjustment between modules, dynamically adapting to foundation settlement and thermal expansion and contraction deformations.

[0040] The beneficial effects of the present invention are as follows:

[0041] 1. Based on the multi-objective collaborative optimization algorithm and the building information model, the present invention generates a module division scheme, optimizes the module size and transportation path, significantly improves the module standardization degree and transportation efficiency, and reduces the risk of interface mismatch between modules. Through the blockchain unique identification code and the standardized magnetic interface design, the interchangeability and traceability management of modules are enhanced, and material waste and construction delays are significantly reduced. The dynamic logistics scheduling combined with the energy self-sufficiency technology effectively reduces transportation energy consumption and carbon emissions, and promotes the development of the green industrialized construction mode.

[0042] 2. The pre-embedded adaptive seismic structure (such as a shape memory alloy sleeve) and the self-healing material layer of the module connection nodes of the present invention act synergistically, significantly enhancing the overall seismic capacity and structural ductility of the building, and effectively coping with the deformations and damages under seismic loads. The real-time monitoring system combined with the self-healing mechanism quickly responds to cracks and stress anomalies, avoids the impact of cumulative damage on the building safety, and extends the service life of the building.

[0043] 3. By combining the magnetic interface with the augmented reality (AR) positioning technology, the present invention realizes the rapid and accurate positioning of modules, significantly reducing the manual calibration workload and assembly error. The collaborative operation of assembly robots and the digital twin real-time feedback system ensure the high efficiency and controllability of the construction process, significantly shortening the overall construction period and improving the construction quality. The blockchain smart contract automatically verifies the completion degree of construction nodes, reduces human intervention and contract disputes, and promotes the transparency and automation of the construction process.

[0044] 4. Through the module-built renewable energy system and the microgrid technology, the present invention reduces the dependence on traditional energy during the construction process and improves the energy self-sufficiency ability during the building operation stage. The intelligent decomposition mechanism and life prediction technology of the degradable module realize the recycling of building materials and the low-environmental-impact recovery, significantly reducing construction waste and resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is the flowchart of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0046] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0047] Embodiment 1:

[0048] A building construction method based on MIC. Taking the assembly of a multi-story residential building as an example, the implementation object is a 6-story steel structure residential building with a building area of 3200 m 2 , and the seismic fortification intensity is 8 degrees.

[0049] The specific implementation steps are as follows:

[0050] S1: Based on the building information model, generate a module division plan through a multi-objective collaborative optimization algorithm, and preset an adaptive seismic structure at the module connection nodes;

[0051] The implementation of the multi-objective collaborative optimization algorithm includes:

[0052] a) Set the weight coefficients of cost, construction period, and carbon emission as α = 0.4, β = 0.3, γ = 0.3;

[0053] b) Use the improved NSGA-II algorithm to generate a Pareto optimal solution set, with a population size of 200 and 500 iterations;

[0054] c) Embed a shape memory alloy sleeve at the module connection node, and set the initial stiffness to 1.8×10 6 N / m.

[0055] Specifically, after importing the BIM model, use the improved NSGA-II algorithm to generate a module division plan: input parameters: column grid spacing 6m×6m, floor height 3m, weight coefficients α = 0.4 / β = 0.3 / γ = 0.3; algorithm operation: population size 200, iteration 500 times, output module size 4.8m×2.4m×3m; seismic structure: embed a Ni-Ti shape memory alloy sleeve (phase change temperature 35°C, initial stiffness 1.8×10 6 N / m) at the beam-column node;

[0056] S2: Embed a self-healing material layer and a fireproof layer in the module, and generate a blockchain displacement identification code for each module;

[0057] The preparation of the self-healing material includes:

[0058] a) Incorporate 2.5 wt% of microcapsule self-healing agent into the concrete, with the capsule diameter of 50 - 200 μm and the core material being a mixture of epoxy resin and curing agent;

[0059] During the casting of the specific module wall: Incorporate 2.5 wt% of microcapsule self-healing agent (urea-formaldehyde resin shell, core materials epoxy resin + polysulfide rubber);

[0060] Install an aerogel fireproof layer (density 180 kg / m 3 , thermal conductivity 0.023 W / m·K);

[0061] b) The blockchain unique identification code is generated through the SHA-256 algorithm, and the data structure includes the material hash value and the IPFS quality inspection report address.

[0062] Specific blockchain ID generation:

[0063] Use the SHA-256 algorithm to generate the identification code "MIC-2023-08A-0157";

[0064] Data storage structure:

[0065]

[0066] S3: Plan the transportation route according to the real-time traffic data;

[0067] The realization of dynamic logistics scheduling includes:

[0068] a) The path planning adopts an improved ant colony algorithm, the pheromone evaporation coefficient ρ = 0.3, and the heuristic factor β = 2; Path planning:

[0069] Adopt an improved ant colony algorithm (ρ = 0.3, β = 2), and input the real-time traffic data (update frequency 1 time / minute);

[0070] Output the optimal path: The transportation distance is optimized from 82 km to 74 km, and the expected fuel consumption is reduced by 18%;

[0071] b) Install a piezoelectric ceramic array on the transport vehicle, with a power generation of ≥18 Wh per kilometer, and the electric energy is stored in a 300F graphene supercapacitor.

[0072] Energy harvesting:

[0073] Install a PZT-5H piezoelectric ceramic array (size 50 mm × 30 mm × 0.5 mm) on the vehicle;

[0074] Measured power generation: Generate 1.8 kWh of electricity per 100 kilometers for in-vehicle GPS and temperature and humidity sensors;

[0075] S4: Simulate the foundation conditions through the digital twin system, adjust the parameters of the magnetic adsorption interface, and use the AR system to project the positioning reference line;

[0076] The adjustment of the magnetic adsorption interface parameters includes:

[0077] a) Generate a ground-based point cloud model through 3D laser scanning, with a registration error with the BIM model < 3 mm;

[0078] Foundation treatment:

[0079] Generate a point cloud model through 3D laser scanning (accuracy ±1 mm), and finite element analysis shows a maximum settlement difference of 7.2 mm / 10 m;

[0080] Adjust the magnetic field strength of the magnetic adsorption interface to 0.8 T (corresponding current 12 A ±0.1 A);

[0081] b) The preset magnetic field strength of the electromagnet array is 0.5 - 1.2 T, and the AR system uses PID control to compensate for projection errors, with a proportional coefficient Kp = 0.8.

[0082] AR positioning:

[0083] Use the HoloLens 2 device to project a reference line, with PID control parameters (Kp = 0.8, Ki = 0.05,

[0084] Kd = 0.2); Measured positioning error: ±0.7 mm on the X-axis, ±0.6 mm on the Y-axis;

[0085] S5: The assembly robot grabs the module, uses a magnetic adsorption interface for positioning, activates the self-healing material to compensate for assembly errors, and synchronously calibrates the seismic damping parameters; when the crack width ≥0.15 mm is detected, the microcapsules are activated to release the repair agent, and the repair rate ≥92%; In the S6 progress monitoring, stress data is collected at 100 Hz through the LoRaWAN sensor network, and the abnormal working condition identification time ≤47 seconds.

[0086] The magnetic adsorption interface allows a ±5 cm position adjustment between modules, dynamically adapting to foundation settlement and thermal expansion and contraction deformation.

[0087] Robot collaborative assembly includes:

[0088] a) A six-axis robotic arm is equipped with a force / torque sensor, and the impedance control parameters are set to a virtual mass of 5 kg and a damping of 80 N·s / m;

[0089] Assembly process:

[0090] The KUKA KR 1000 robot (repeat positioning accuracy ±0.03 mm) grabs a 4.8-ton module;

[0091] Impedance control parameters: M = 5 kg, B = 80 N·s / m, K = 1200 N / m;

[0092] b) When the module docking deviation >5 mm, trigger the shape memory alloy to heat up to 35 °C, with a heating rate of 8 °C / s.

[0093] Error compensation:

[0094] After detecting a 3.8 mm horizontal deviation, trigger the shape memory alloy to heat up to 35 °C (heating rate 8 °C / s);

[0095] Calculation of self-healing agent release amount: Q = π × (0.15 mm) 2 × 5 mm × (1 - e^(-0.15 × 60)) = 0.032 mL;

[0096] S6: Collect assembly data through Internet of Things sensors and update the digital twin model, and verify the node completion degree based on the blockchain smart contract;

[0097] a) Data collection

[0098] LoRaWAN sensor (center frequency 868 MHz) collects stress data at 100 Hz

[0099] Automatic verification conditions for blockchain smart contract:

[0100] Solidity Copy Code

[0101] 1 require(stressData < 25 MPa, "Stress exceeds the standard");

[0102] 2 require(alignmentError <= 5 mm, "Positioning deviation is too large");

[0103] b) Exception handling

[0104] When the stress value of a certain node is detected to be 28 MPa, the system triggers an alarm and pauses the assembly within 47 seconds;

[0105] S7: Unfold the flexible solar thin film built into the module and establish an energy sharing network between modules;

[0106] The construction of the energy sharing network includes:

[0107] a) Adopt CIGS flexible photovoltaic modules, with a single-module laying area ≥ 2.5 m 2 , and the conversion efficiency is 19.3%;

[0108] Photovoltaic deployment

[0109] Unfold the CIGS flexible solar thin film (single module 2.6 m 2 , and the conversion efficiency is 19.3%);

[0110] The MPPT algorithm tracks the maximum power point (voltage scanning step size 0.5 V);

[0111] b) Establish a 48V DC microgrid, and the control target of the circulating current suppression algorithm is that the circulating current < 3% of the rated current.

[0112] Microgrid operation

[0113] Establish a 48V DC network, and the circulating current suppression algorithm controls the circulating current at 2.1% (< 3% target value); the first-day power generation record: 62 kWh (meeting 65% of the electricity demand for daytime construction);

[0114] S8: Predict the module life through the digital twin system, and start the enzymatic catalytic decomposition program for the degradable module.

[0115] The triggering conditions for the enzymatic catalytic decomposition program are:

[0116] a) The environmental temperature ≥ 35°C and the humidity ≥ 70% for 72 hours continuously;

[0117] b) Use the LSTM neural network to predict the remaining life of the module, with an input feature dimension of 28 and 64 neurons in the hidden layer.

[0118] Life prediction

[0119] The LSTM model (input 28-dimensional features, 64 neurons in the hidden layer) predicts that the remaining life of the module is 12.3 years (error ± 0.8 years);

[0120] Temporary enclosure decomposition

[0121] Triggering conditions: environmental temperature 36°C / humidity 75% for 72 hours continuously

[0122] Detection after 30 days: the decomposition rate of the biobased material is 98.2%, and the heavy metal residue < 0.01 ppm

[0123] Experimental data and effect verification

[0124] Index Measured value Traditional method Achievement degree of technical effect Single-module assembly time 7 minutes and 28 seconds 22 minutes and 15 seconds Efficiency increased by 300% Overall construction period 38 days 107 days Shortened by 63% Maximum inter-story drift angle 1 / 356 1 / 280 Seismic performance improved by 27% Construction waste generation <![CDATA[11.7kg / m 2 > <![CDATA[73kg / m 2 > Reduced by 84%

[0125] Example 2

[0126] A building construction method based on MIC, which is different from Example 1 in that it is applied in an industrial plant construction project. The maximum size of the module is expanded to 12m × 4m × 3.5m, and the magnetic field strength of the magnetic adsorption interface is increased to 1.2T.

[0127] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A building construction method based on MIC, characterized in that, Specifically, it includes the following steps: S1: Based on the Building Information Model, generate a module division plan through a multi-objective collaborative optimization algorithm, and preset an adaptive seismic structure at the module connection nodes; S2: Embed a self-healing material layer and a fireproof layer in the module, and generate a blockchain displacement identification code for each module; S3: Plan the module transportation route according to real-time traffic data; S4: Simulate the foundation conditions through a digital twin system, adjust the parameters of the magnetic adsorption interface, and use the AR system to project the positioning reference line; S5: Use an assembly robot to grab the module, position it with a magnetic adsorption interface, trigger the self-healing material to compensate for the assembly error, and synchronously calibrate the seismic damping parameters; S6: Collect assembly data through Internet of Things sensors and update the digital twin model, and verify the node completion degree based on the blockchain smart contract; S7: Unfold the flexible solar thin film built into the module and establish an energy sharing network between modules; S8: Predict the module life through the digital twin system and initiate the enzyme-catalyzed decomposition process for degradable modules.

2. The MIC-based building construction method according to claim 1, wherein: The implementation of the multi-objective collaborative optimization algorithm in step S1 includes: a) Set the weight coefficients of cost, construction period, and carbon emission as α = 0.4, β = 0.3, γ = 0.3; b) Use the improved NSGA-II algorithm to generate the Pareto optimal solution set, with a population size of 200 and 500 iteration times; c) Embedded shape memory alloy sleeves at the module connection nodes, with the initial stiffness set to 1.8×10 6 N / m.

3. The MIC-based building construction method according to claim 1, characterized in that: The preparation of the self-healing material in step S2 includes: a) Incorporate 2.5 wt% of microcapsule self-healing agent into the concrete, with a capsule diameter of 50 - 200 μm and the core material being a mixture of epoxy resin and curing agent; b) The blockchain unique identification code is generated through the SHA-256 algorithm, and the data structure includes the material hash value and the IPFS quality inspection report address.

4. The MIC-based building construction method according to claim 1, wherein: The realization of dynamic logistics scheduling in step S3 includes: a) The path planning adopts the improved ant colony algorithm, with the pheromone evaporation coefficient ρ = 0.3 and the heuristic factor β = 2; b) The transport vehicle is installed with a piezoelectric ceramic array, with a power generation of ≥18 Wh per kilometer, and the electric energy is stored in a 300F graphene supercapacitor.

5. The MIC-based building construction method according to claim 1, characterized in that: The adjustment of the magnetic adsorption interface parameters in step S4 includes: a) Generate a ground point cloud model through three-dimensional laser scanning, with a registration error with the BIM model <3 mm; b) The preset magnetic field intensity of the electromagnet array is 0.5 - 1.2 T, and the AR system uses PID control to compensate for the projection error, with the proportionality coefficient Kp = 0.

8.

6. The MIC-based building construction method according to claim 1, characterized in that: The robot collaborative assembly in step S5 includes: a) The six-axis robotic arm is equipped with a force / torque sensor, and the impedance control parameters are set as a virtual mass of 5 kg and a damping of 80 N·s / m; b) When the module docking deviation >5 mm, trigger the shape memory alloy to heat up to 35°C, with a heating rate of 8°C / s.

7. The MIC-based building construction method according to claim 1, wherein: The construction of the energy sharing network in step S7 includes: a) Adopt CIGS flexible photovoltaic modules, with a single-module laying area ≥ 2.5 m 2 , and a conversion efficiency of 19.3%; b) Establish a 48V DC microgrid, and the control target of the circulating current suppression algorithm is that the circulating current <3% of the rated current.

8. The MIC-based building construction method according to claim 1, characterized in that: The triggering conditions of the enzyme-catalyzed decomposition process in step S8 are: a) The environmental temperature ≥35°C and humidity ≥70% last for 72 hours; b) Use the LSTM neural network to predict the remaining life of the module, with an input feature dimension of 28 and 64 hidden layer neurons.

9. The MIC-based building construction method according to claim 1, characterized in that: During the assembly process of step S5, when the detected crack width ≥ 0.15 mm, the microcapsules are activated to release the repair agent, and the repair rate ≥ 92%; in the progress monitoring of S6, stress data is collected at 100 Hz through the LoRaWAN sensor network, and the abnormal working condition identification time ≤ 47 seconds.

10. The MIC-based building construction method according to claim 1, characterized in that: The magnetic interface allows for a ±5 cm position adjustment between modules, dynamically adapting to foundation settlement and thermal expansion and contraction deformation.

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