Deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning and collaborative construction method

Abstract

The invention belongs to the field of deep foundation pit intelligent supporting systems, and particularly relates to a deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning and a collaborative construction method.The deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning comprises an oblique beam grid supporting structure composed of two-way crossed steel oblique beams, and the outer layer of each steel oblique beam is a Q355B steel pipe with the thickness not smaller than 12 mm; the inclined beam is filled with ultra-high performance concrete with the compressive strength not smaller than 150 MPa, the inclination angle of the inclined beam ranges from 25 degrees to 60 degrees, and an MEMS micro-electro-mechanical system sensor is pre-buried at a node; and the digital twin platform integrates the geological BIM model and the LSTM neural network prediction model. The bending rigidity is improved through the oblique beam grid supporting structure and the concrete filled steel tube composite beam, the inclination angle ranges from 25 degrees to 60 degrees, force flow transmission is optimized, and micro-strain-level stress monitoring is achieved through the MEMS sensors embedded in the nodes. A digital twin platform and an LSTM model are fused with 12-dimensional parameters, the 72-hour deformation prediction error is smaller than or equal to 1.5 mm, and compared with a traditional experience algorithm, the precision is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of deep foundation pit intelligent support system, and particularly relates to a deep foundation pit intelligent support system based on inclined beam grid and digital twinning and a collaborative construction method. BACKGROUND

[0002] The current application of BIM technology in deep foundation pit engineering has the following core problems:

[0003] Low modeling efficiency: traditional modeling relies on manual operation, such as pile-by-pile arrangement, and the geological model has insufficient precision, with a soil layer error of ±30%, and complex component modeling takes too long.

[0004] Poor data collaboration: monitoring data (displacement, stress) is disconnected from the BIM model, with a warning delay of >6 hours; design changes require manual review, with a single cycle taking 3-5 days

[0005] Lack of dynamic regulation: the model cannot predict risks in real time, such as soil creep, and can only be remedied afterwards; there is a lack of closed-loop linkage with control equipment (such as hydraulic servo systems). SUMMARY

[0006] In order to overcome the shortcomings of the prior art, the present application provides a deep foundation pit intelligent support system based on inclined beam grid and digital twinning and a collaborative construction method, which has solved the problems of low efficiency of traditional BIM modeling, data islandization, weak risk prediction, and the urgent need for parameterized design + real-time data fusion.

[0007] One embodiment of the present application discloses a deep foundation pit intelligent support system, comprising:

[0008] An inclined beam grid support structure composed of bidirectional intersecting steel inclined beams, the outer layer of the steel inclined beams is Q355B steel pipes with a thickness not less than 12 mm, and the inner filling is ultra-high performance concrete with a compressive strength not less than 150 MPa, the inclination angle of the inclined beams ranges from 25 degrees to 60 degrees, and MEMS micro-electromechanical sensors are pre-buried at the nodes;

[0009] A digital twinning platform integrating a geological BIM model and an LSTM neural network prediction model, the input layer of the LSTM model includes 12-dimensional parameters such as inclined beam stress, underground water level, and soil creep coefficient, and the output is a prediction value of foundation pit deformation within 72 hours;

[0010] A distributed monitoring network uses a diameter of 0.5 mm armored optical cable embedded in the support pile crown beam to form a strain and temperature sensing array with a full length of 1.5 km, a strain measurement range of ±5000 micro-strain, and a temperature resolution of 0.1 degrees Celsius;

[0011] A modular assembly unit including a prefabricated steel-concrete support and a hydraulic mortise and tenon interface, the support is provided with a damping cavity with a damping ratio not less than 0.15, and the interface has a bearing capacity not less than 500 kN.

[0012] In one embodiment, the nodes of the inclined beam grid support structure are connected by six-way flanges, with a bolt pre-tightening force of not less than 200 kN, and the grid spacing is dynamically adjusted according to the geological shear modulus: in soft soil areas with a shear modulus of less than 20 MPa, the spacing is increased to 3 m; in hard rock areas with a shear modulus of more than 50 MPa, the spacing is relaxed to 8 m; and the adjustment algorithm is based on genetic algorithm optimization of topological layout.

[0013] In one embodiment, the digital twin platform updates the model every 5 minutes, triggers a level 3 warning when the predicted deformation exceeds 0.15% of the foundation pit depth, and dynamically adjusts the inclined beam axial force through a hydraulic servo system with a control accuracy of plus or minus 5 kN, and the adjustment formula is:

[0014]

[0015] where F i is the adjustment force of the i-th node, K ij is the node stiffness matrix, δ j is the measured displacement, α and β are the geological and construction coefficients, γ is the unit weight of the soil, and A i is the influence area.

[0016] In one embodiment, the LSTM neural network prediction model has 64 neurons in the hidden layer, uses a Sigmoid activation function, and has a training set error of plus or minus 0.8 mm; the model dynamically corrects the weight parameters based on real-time monitoring data during the excavation of the foundation pit.

[0017] In one embodiment, the prefabricated steel-concrete support of the modular assembly unit is embedded with an RFID chip, which records the carbon emission factor of steel at 1.2 kg of CO2 per kg and the carbon emission factor of concrete at 280 kg of CO2 per m3, and the data is synchronized to the digital twin platform to generate a carbon neutral report.

[0018] One embodiment of the present application discloses a green collaborative construction method for a super-large-area deep foundation pit, comprising the following steps:

[0019] Construction support piles and crown beams are installed using a static pressure pile driver to install inclined beam supports, with construction noise not greater than 68 dB during the day and not greater than 55 dB at night;

[0020] The inclined beam grid is dynamically installed, initially tensioned to 50% of the design axial force, increased to 80% when excavated to a depth of 50%, adjusted to 100% after the bottom plate is poured, and simultaneously released with a redundancy of about 80%;

[0021] The space-time effect coefficient λ is calculated through the digital twin platform, and when the λ value exceeds 0.6, the top-down method is switched, and the force transmission belt in the edge area is preferentially constructed, the force transmission belt is embedded with a 12 mm thick water stop steel plate and connected to the lower end of the inclined beam by a 10.9 grade high-strength friction type bolt.

[0022] In one embodiment, the inclined beam is removed by a laser-guided plasma cutting process with a cutting width of no more than 3 mm, and a synchronous spraying of a nano-silica dust suppressant with a coverage rate of no less than 95%. The removal sequence is performed according to a stress release curve generated by a digital twin platform.

[0023] In one embodiment, the waste concrete is processed by a vertical impact crusher with a crushing capacity of no less than 50 tons per hour, and the recycled aggregate is used to replace the original material of the bottom cushion layer at a mixing amount of 40%, with the aggregate particle size controlled within 5 to 20 mm.

[0024] In one embodiment, the emergency response adopts a UAV cluster inspection equipped with a thermal imager with a resolution of 640x512 pixels to identify cracks, with a response time of less than 10 minutes, and data is transmitted in real time to the digital twin platform through the LPWAN network.

[0025] In one embodiment, the precipitation adopts a vacuum deep well and electro-osmosis composite process, with a well point spacing of 8 meters, a water level control accuracy of plus or minus 0.3 meters, and an electro-osmosis voltage gradient of 1.5 volts per centimeter.

[0026] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0027] 1. Real-time acquisition of support structure stress and displacement data, automatic triggering of early warning through prediction algorithm, significantly reducing response time, and dynamic adjustment of support shaft force by support structure hydraulic self-adaptive module, significantly improving anti-deformation ability.

[0028] 2. Inclined support and self-stable design, such as front support pile and rear pull rod, forming an anti-overturning moment, effectively controlling the horizontal displacement and uplift of the foundation pit.

[0029] 3. Mortise and tenon type connection panel realizes rapid assembly, and the construction speed reaches the traditional process.

[0030] 4. Mortise and tenon connection design cancels on-site welding, and the support installation speed is faster than the traditional steel sheet pile.

[0031] 5. Real-time adjustment of pressure sensor to the inclined support shaft force, reducing 30% of the redundant support amount, while improving the anti-heave ability in soft soil area.

[0032] 6. Recyclable steel components, self-stable support inclined support steel pipe can be disassembled and recycled, reducing the amount of concrete.

[0033] 7. Shock absorption and noise reduction design, the vibration decibel value of the LTW system is greatly reduced compared with the traditional method, reducing the noise during night construction.

[0034] 8. Distributed optical fiber sensing network is implanted in the enclosure structure crown beam, real-time monitoring of deformation within 1.5 kilometers, and the early warning response time is shortened from hours to minutes.

[0035] 9. The inclined supports and the front support piles form an anti-overturning moment, improving the accuracy of horizontal displacement control in the foundation pit.

[0036] 10. Open excavation reduces mechanical fuel consumption and lowers the overall carbon emission reduction intensity. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0038] Fig. 1 This is a flowchart of the inclined beam grid support closed-loop control system of the present invention;

[0039] Fig. 2 This is a flowchart of the geological-monitoring data fusion decision-making process of the present invention;

[0040] Fig. 3 This is a closed-loop flowchart of green demolition and resource recycling in this invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0043] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments.

[0044] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments. Figs. 1-3 Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments.

[0045] The inclined beam grid support structure is composed of bidirectional intersecting steel inclined beams, the outer layer of the steel inclined beam is a Q355B steel pipe with a thickness not less than 12 mm, and the inner filling is super high performance concrete with a compressive strength not less than 150 MPa, the inclination angle of the inclined beam ranges from 25 degrees to 60 degrees, and a MEMS micro-electro-mechanical sensor is pre-buried at the node;

[0046] The digital twin platform integrates a geological BIM model and an LSTM neural network prediction model, the input layer of the LSTM model contains 12-dimensional parameters such as inclined beam stress, underground water level and soil creep coefficient, and the output is a prediction value of pit deformation within 72 hours;

[0047] The distributed monitoring network adopts a diameter of 0.5 mm armored optical cable embedded in the support pile crown beam to form a strain and temperature sensing array with a full length of 1.5 km, the strain measurement range is ±5000 με, and the temperature resolution is 0.1 degrees Celsius;

[0048] The modular assembly unit includes a prefabricated steel-concrete support and a hydraulic mortise and tenon interface, the support is provided with a damping cavity with a damping ratio not less than 0.15, and the interface has a bearing capacity not less than 500 kN.

[0049] In the embodiment of the application, the inclined beam grid support structure, the steel pipe concrete composite beam (Q355B steel pipe + 150 MPa UHPC) improves the bending stiffness, the inclination angle of 25 degrees to 60 degrees optimizes the force flow transmission, and the pre-buried MEMS sensor at the node realizes micro-strain level stress monitoring;

[0050] The digital twin platform, the LSTM model fuses 12-dimensional parameters (such as soil creep coefficient), the 72-hour deformation prediction error is ≤1.5 mm, and the accuracy is improved compared with traditional empirical algorithms;

[0051] The distributed monitoring network, the 1.5 km armored optical cable sensing array realizes ±5000 με large range strain monitoring, and the temperature compensation algorithm eliminates environmental interference;

[0052] The modular assembly unit, the hydraulic mortise and tenon interface has a bearing capacity of 500 kN (equivalent to 50 tons), and the damping cavity (damping ratio ≥0.15) reduces the construction vibration transmission.

[0053] In one embodiment, the node of the inclined beam grid support structure adopts a six-way flange plate connection, the bolt pretightening force is not less than 200 kN, and the grid spacing is dynamically adjusted according to the geological shear modulus: the soft soil area with a shear modulus less than 20 MPa is encrypted to 3 m, the hard rock area with a shear modulus greater than 50 MPa is relaxed to 8 m, and the adjustment algorithm is based on genetic algorithm optimization topology layout.

[0054] In the embodiment of the present application, the six-way flange plate connection, the bolt pretightening force 200 kN guarantees the node shear strength ≥300 MPa, and avoids the residual stress cracks caused by traditional welding;

[0055] The genetic algorithm dynamically adjusts the distance, the soft soil area (the shear modulus is less than 20 MPa) has a spacing of 3 m: increases the support density, and controls the ground settlement to be less than 10 mm; the hard rock area (greater than 50 MPa) has a spacing of 8 m: reduces the amount of steel, and shortens the construction period.

[0056] In one embodiment, the digital twin platform updates the model every 5 minutes, triggers a third-level warning when the predicted deformation exceeds 0.15% of the foundation pit depth, and dynamically adjusts the inclined beam axial force through the hydraulic servo system, with a control accuracy of ±5 kN. The adjustment formula is:

[0057]

[0058] Where, F i is the adjustment force of the i-th node, K ij is the node stiffness matrix, δ j is the measured displacement, α and β are the geological and construction coefficients, γ is the unit weight of soil, and A i is the influence area.

[0059] In the embodiment of the present application, the third-level warning mechanism, when the foundation pit depth is 20 m, the deformation greater than 30 mm triggers a warning, and the response time is shortened;

[0060] The stiffness matrix K ij is related to the node displacement δ j , and dynamically compensates the support force;

[0061] The geological coefficient α (1.2 for soft soil and 0.8 for hard rock) is self-adaptive to soil differences;

[0062] The control accuracy is ±5 kN (equivalent to 0.5 tons), which avoids material waste caused by over-supporting.

[0063] In one embodiment, the LSTM neural network prediction model has 64 neurons in the hidden layer, uses a Sigmoid activation function, and has a training set error of ±0.8 mm. The model dynamically corrects the weight parameters through real-time monitoring data in the foundation pit excavation process.

[0064] In the embodiment of the application, the 64-neuron + sigmoid activation function solves the vanishing gradient problem of processing time series data (such as groundwater level fluctuations), and the training set error is ± 0.8 mm, which is more accurate than the BP neural network;

[0065] Dynamic weight correction, model parameters are updated every 2 hours during excavation, and the correlation R between the prediction result and the actual deformation is 2 ≥ 0.95.

[0066] In one embodiment, the prefabricated steel-concrete support of the modular assembly unit is embedded with an RFID chip, recording the carbon emission factor of steel 1.2 kg of carbon dioxide per kg and the carbon emission factor of concrete 280 kg of carbon dioxide per cubic meter, and synchronizing the data to the digital twin platform to generate a carbon neutral report.

[0067] In this embodiment, the carbon emission of steel is 1.2 kg of carbon dioxide / kg + the carbon emission of concrete is 280 kg of carbon dioxide / m 3 Real-time recording; the digital twin platform generates a carbon neutral report, accurately calculates the emission reduction of recycled steel (1.8 tons of carbon dioxide per ton of emission reduction).

[0068] One embodiment of the application discloses a large-area deep foundation pit green collaborative construction method, comprising the following steps:

[0069] Construction support pile and crown beam, static pressure pile planting machine is used to install inclined beam support, construction noise is not greater than 68 decibels during the day, and not greater than 55 decibels at night;

[0070] Dynamic installation of inclined beam grid, initial tension to 50% of the design axial force, increased to 80% when excavated to 50% depth, adjusted to 100% after bottom pouring, and the redundant constraint is released synchronously;

[0071] Through the digital twin platform, the space-time effect coefficient λ is calculated, when the λ value exceeds 0.6, the top-down method is switched, and the force transmission belt in the edge area is preferentially constructed, the said force transmission belt is embedded with a thickness of 12 mm water stop steel plate and connected with the lower end of the inclined beam through a high-strength friction type bolt of grade 10.9;

[0072] The inclined beam is removed by using a laser-guided plasma cutting process, the notch width is not greater than 3 mm, a nano-silicon dioxide dust suppressant with a coverage rate of not less than 95% is sprayed synchronously, and the removal sequence is performed according to the stress release curve generated by the digital twin platform;

[0073] The waste concrete is treated by a vertical impact crusher, the crushing capacity is not less than 50 tons per hour, the recycled aggregate is replaced by the original material of the bottom pad at a dosage of 40%, and the aggregate particle size is controlled in the range of 5 to 20 mm;

[0074] Emergency response adopts unmanned aerial vehicle cluster inspection, is equipped with thermal imager of resolution 640*512 pixels to identify crack, response time is less than 10 minutes, and data is returned to digital twin platform in real time through LPWAN network;

[0075] Precipitation adopts vacuum deep well and electro-osmosis composite process, well point spacing is encrypted to 8 meters, water level control precision is plus or minus 0.3 meters, and electro-osmosis voltage gradient is set to 1.5 volts per centimeter.

[0076] In the embodiment of the application, the redundancy constraint is released, the internal force conflict of the structure is reduced, the space-time effect coefficient λ is, and the effect is: when λ is greater than 0.6, the reverse construction method is switched, the edge force transmission belt (water stop steel plate + 10.9 grade bolt) is preferentially constructed, and the differential settlement is controlled to be less than or equal to 5 mm.

[0077] Laser-guided plasma cutting, cut width is less than or equal to 3 mm (traditional oxygen cutting is greater than 10 mm), heat-affected zone is reduced, and the recycling value of the steel pipe is protected.

[0078] Closed-loop control: embodying real-time iteration of "monitoring->prediction->control->feedback";

[0079] Multi-system linkage: showing the data fusion path of the geological model, the monitoring network and the LSTM algorithm;

[0080] Dynamic optimization evidence: verifying the decision basis of the genetic algorithm adjusting the grid spacing in claim 2;

[0081] The present application realizes the following through the technical closed loop of "intelligent sensing->predictive decision->precise execution" and the construction system of "green construction->resource recycling->emergency support":

[0082] Safety improvement: deformation control accuracy reaches millimeter level (LSTM model + hydraulic control);

[0083] Efficiency breakthrough: support installation speed is improved (modular assembly + staged tensioning);

[0084] Low-carbon emission reduction: steel recycling rate + recycled aggregate replacement (carbon tracking RFID + crushing process).

[0085] The device embodiments described above are only schematic, wherein the modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, i.e., can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0086] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform, and of course, the various embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0087] Finally, it should be noted that: the deep foundation pit intelligent support system and collaborative construction method based on inclined beam grid and digital twinning disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; the technical solutions recorded in the foregoing embodiments can still be modified, or

[0088] equivalent replacement of part of the technical features; and these modifications or replacements do not make the technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

[0089] application.

Claims

1. A deep foundation pit intelligent support system, characterized in that, Comprise: inclined beam grid support structure composed of two-way cross steel inclined beam, the outer layer of the steel inclined beam is Q355B steel pipe with a thickness not less than 12 mm, filled with ultra-high performance concrete with a compressive strength not less than 150 MPa, the inclined beam inclination range is 25 degrees to 60 degrees, and MEMS micro-electro-mechanical sensor is embedded at the node; digital twin platform integrating geological BIM model and LSTM neural network prediction model, the input layer of the LSTM model contains 12-dimensional parameters such as inclined beam stress, groundwater level and soil creep coefficient, and the output is the prediction value of foundation pit deformation within 72 hours; distributed monitoring network, using 0.5 mm diameter armored optical cable embedded in support pile crown beam to form a 1.5 km long strain and temperature sensing array, strain measurement range is ±5000 micro-strain, temperature resolution is 0.1℃; modular assembly unit, including prefabricated steel-concrete support and hydraulic mortise and tenon interface, the support is provided with a damping cavity with a damping ratio not less than 0.15, and the interface bearing capacity is not less than 500 kN.

2. The intelligent support system for deep foundation pit according to claim 1, characterized in that, The node of the inclined beam grid support structure adopts six-way flange plate connection, the bolt pre-tightening force is not less than 200 kN, and the grid spacing is dynamically adjusted according to the geological shear modulus: the soft soil area with a shear modulus less than 20 MPa is encrypted to 3 meters, and the hard rock area with a shear modulus greater than 50 MPa is relaxed to 8 meters, and the adjustment algorithm is based on genetic algorithm to optimize the topological layout.

3. The intelligent support system for deep foundation pit according to claim 1, characterized in that, The digital twin platform updates the model every 5 minutes, triggers a three-level warning when the predicted deformation exceeds 0.15% of the foundation pit depth, and dynamically adjusts the inclined beam axial force through the hydraulic servo system, with a control accuracy of ±5 kN, and the adjustment formula is: Where F i is the adjustment force of the ith node, K ij is the stiffness matrix of the node, δ j is the measured displacement, α and β are the geological and construction coefficients, γ is the soil bulk density, and A i is the influence area.

4. The intelligent support system for deep foundation pit according to claim 3, characterized in that, The hidden layer of the LSTM neural network prediction model is provided with 64 neurons, adopts Sigmoid activation function, and the training set error is ±0.8 mm, and the model dynamically corrects the weight parameters through real-time monitoring data in the foundation pit excavation process.

5. The intelligent bracing system for deep foundation pits according to claim 1, wherein, The prefabricated steel-concrete support of the modular assembly unit is embedded with an RFID chip, recording the carbon emission factor of steel 1.2 kg of CO2 per kg and the carbon emission factor of concrete 280 kg of CO2 per cubic meter, and the data is synchronized to the digital twin platform to generate a carbon neutral report.

6. A green collaborative construction method for a super-large-area deep foundation pit, characterized in that, Comprise the following steps: Construction support pile and crown beam, using static pressure pile installation machine to install inclined beam support, construction noise is not greater than 68 dB during the day and not greater than 55 dB at night; Dynamically install the inclined beam grid, initially tensioned to 50% of the design axial force, increased to 80% when excavated to 50% depth, adjusted to 100% after bottom pouring, and released redundant constraints simultaneously; Switch to top-down method when the space-time effect coefficient λ calculated by the digital twin platform exceeds 0.6, and preferentially construct the force transmission belt in the edge area, the force transmission belt is embedded with a thickness of 12 mm water stop steel plate and connected with the lower end of the inclined beam through 10.9 grade high-strength friction type bolts.

7. The green collaborative construction method for a super-large-area deep foundation pit according to claim 6, characterized in that, The inclined beam is removed by laser-guided plasma cutting process, the cutting width is not greater than 3 mm, and the synchronous spraying coverage is not less than 95% of the nano-silicon dioxide dust suppressant, and the removal sequence is performed according to the stress release curve generated by the digital twin platform.

8. The green collaborative construction method for a super-large-area deep foundation pit according to claim 6, characterized in that, The waste concrete is processed by vertical impact crusher with a crushing capacity of not less than 50 tons per hour, and the recycled aggregate is used to replace the original material of the bottom cushion layer at a mixing amount of 40%, and the particle size of the aggregate is controlled within 5 to 20 mm.

9. The green collaborative construction method for a super-large-area deep foundation pit according to claim 6, characterized in that, The emergency response adopts unmanned aerial vehicle cluster inspection, and is equipped with a thermal imager with a resolution of 640×512 pixels to identify cracks, and the response time is less than 10 minutes. The data is transmitted in real time to the digital twin platform through the LPWAN network.

10. The green collaborative construction method for a super-large-area deep foundation pit according to claim 6, characterized in that, The precipitation adopts a vacuum deep well and electro-osmosis composite process, the well point spacing is encrypted to 8 meters, the water level control accuracy is plus or minus 0.3 meters, and the electro-osmosis voltage gradient is set to 1.5 volts per centimeter.

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