Method for controlling concrete surface finishing effect under working condition of inverted arch trestle

Abstract

The invention relates to the technical field of concrete construction, in particular to a method for controlling the concrete surface finishing effect under the working condition of an inverted arch trestle, which comprises the following steps of: S1, adjusting the supporting stability and the working space layout of the trestle according to the characteristics of high-altitude tunnel construction equipment and the working condition of an inverted arch; high-precision operation is achieved by researching and developing the intelligent surface finishing robot adaptive to the working condition of the high-altitude trestle and a parameter correction mechanism, the temperature control problem of concrete surface finishing in the high and cold environment is solved through the constructed intelligent temperature control platform, and dynamic optimization of technological parameters is achieved by means of intelligent monitoring and durability prediction. Therefore, the quality stability, the equipment environment adaptability and the construction intelligence level of the concrete surface finishing under the working condition of the high-altitude inverted arch trestle are improved, the surface defect rate is remarkably reduced, and the structural durability is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete construction, in particular to a method for controlling concrete finishing effect under inverted arch trestle working condition. BACKGROUND

[0002] As the core load-bearing part of the tunnel structure, the concrete construction quality of the inverted arch directly determines the overall stability and durability of the tunnel. In the construction of the inverted arch of the tunnel, the inverted arch trestle is an indispensable key facility, which is a temporary load-bearing platform specially designed for inverted arch concrete pouring operation. It is usually built by section steel or steel structure, spanning the inverted arch operation area at the bottom of the tunnel, forming a channel for the passage of construction vehicles and equipment, ensuring the smoothness of the transportation line in the tunnel, and reserving operation space for inverted arch concrete pouring, vibrating, finishing and other operations under the trestle, providing a safe and continuous working surface for inverted arch construction, effectively solving the problems of low efficiency and transportation interference caused by the traditional "excavation and pouring one section at a time, waiting for solidification" mode, and is especially suitable for rapid construction of long and large tunnels. However, in the existing technology, in the tunnel engineering in high-altitude and high-cold regions, the concrete finishing control under the working condition of the inverted arch trestle has the problems of poor adaptability of construction equipment, great influence of low temperature on the quality of concrete finishing, difficult timely monitoring and control of surface defects and durability problems.

[0003] Based on this, the present application provides a method for controlling the concrete finishing effect under the working condition of the inverted arch trestle to solve the above-mentioned technical problems. SUMMARY

[0004] The purpose of the present application is to provide a method for controlling the concrete finishing effect under the working condition of the inverted arch trestle. The intelligent finishing robot and parameter correction mechanism adapted to the high-altitude trestle working condition are developed to realize high-precision operation, and the intelligent temperature control platform is constructed to solve the temperature control problem of concrete finishing in high-cold environment. With the aid of intelligent monitoring and durability prediction, the process parameters are dynamically optimized, thereby improving the quality stability of concrete finishing under the working condition of high-altitude inverted arch trestle, the environmental adaptability of equipment, and the intelligent level of construction, significantly reducing the surface defect rate and prolonging the structural durability.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] The present application provides a method for controlling the concrete finishing effect under the working condition of the inverted arch trestle, comprising the following steps:

[0007] S1: adjusting the trestle support stability and operation space layout according to the characteristics of high-altitude tunnel construction equipment and the working condition of the inverted arch;

[0008] S2: dynamically regulating the pouring thickness, slump and material uniformity in combination with the influence of high-cold and high-altitude environment on the initial setting time and fluidity of concrete;

[0009] S3: Based on the drilling and blasting method tunnel equipment coordination control logic and S1 adjusted stack bridge space parameters, develop highland type intelligent surface collecting robot adapting to stack bridge working condition and establish altitude-equipment parameter correction database, through supercharged power system and GNSS / IMU automatic navigation to carry out high-precision curved surface leveling;

[0010] S4: Embed the temperature control system in the intelligent surface collecting robot and stack bridge auxiliary structure of S3, and construct an intelligent operation platform integrating the functions of "dynamic leveling, infrared radiation heating and micro-environmental insulation";

[0011] S5: According to the characteristics of the working space below and around the stack bridge, use unmanned aerial vehicle infrared AI crack warning system and distributed optical fiber sensing technology to intelligently identify and monitor the real-time damage of concrete surface micro-cracks, predict durability based on damage theory and dynamically optimize process parameters.

[0012] In S1, according to the characteristics of high-altitude tunnel construction equipment and inverted arch working condition, adjust the support stability of stack bridge and the layout of working space, the specific steps are as follows:

[0013] S1.1: Analyze the performance parameter changes of construction equipment in high-altitude environment, and determine the load demand of stack bridge support system;

[0014] S1.2: According to the inverted arch construction load characteristics, adjust the support spacing and reinforcement method of stack bridge;

[0015] S1.3: Combined with the characteristics of inverted arch curve and construction process, reasonably layout the working platform and passing space.

[0016] In S2, combined with the influence of high-cold and high-altitude environment on the initial setting time and fluidity of concrete, dynamically control the pouring thickness, slump and material uniformity, the specific steps are as follows:

[0017] S2.1: Analyze the influence of temperature, humidity and other factors in high-cold and high-altitude environment on the initial setting time and fluidity of concrete;

[0018] S2.2: According to the results of analyzing the influence of environment, dynamically adjust the key parameters of concrete pouring thickness and slump for different construction stages, and optimize the material uniformity;

[0019] S2.3: Use sensor technology and real-time monitoring system to track the working performance indicators of concrete initial setting time and fluidity, and quickly adjust the construction scheme on site through timely feedback data.

[0020] S3.1: Based on the drilling and blasting method tunnel equipment coordination control logic and S1 adjusted stack bridge space parameters, design and develop intelligent surface collecting robots suitable for plateau environment and stack bridge working conditions;

[0021] S3.1: Based on the drilling and blasting method tunnel equipment coordination control logic and S1 adjusted stack bridge space parameters, design and develop intelligent surface collecting robots suitable for plateau environment and stack bridge working conditions;

[0022] S3.2: Based on the influence of environmental factors such as air density and temperature at different altitudes on equipment performance, build a corresponding equipment parameter correction database;

[0023] S3.3: Integrate the supercharged power system in the intelligent surface collecting robot;

[0024] S3.4: Use the global navigation satellite system combined with the inertial measurement unit to perform accurate position information and attitude control.

[0025] The specific steps of S3.2 are as follows:

[0026] S3.2.1: Collect environmental parameters at different altitudes, including atmospheric pressure, air density, environmental temperature and oxygen content;

[0027] S3.2.2: Simultaneously test the output efficiency of the power system, the hydraulic response speed and the motor power attenuation data of the plateau type intelligent surface collecting robot under the above environment;

[0028] S3.2.3: Establish a mapping relationship model between environmental parameters and equipment performance attenuation, and store it as a multi-dimensional lookup table or function expression;

[0029] S3.2.4: Integrate the mapping relationship into the device control system to real-time correct the power output, walking speed and surface collecting operation parameters.

[0030] In S4, the temperature control system is embedded in the intelligent surface collecting robot of S3 and the stack bridge auxiliary structure, and an intelligent operation platform integrating "dynamic leveling, infrared radiation heating and micro-environmental insulation" functions is constructed, and the specific steps are as follows:

[0031] S4.1: Embed the infrared radiation heating module and insulation structure in the S3 intelligent surface collecting robot body and stack bridge auxiliary structure;

[0032] S4.2: In the process of intelligent surface collecting robot marching, the infrared radiation heating system is started simultaneously, so that the concrete surface receives uniform heat radiation while leveling operation;

[0033] S4.3: Set up a liftable or telescopic flexible thermal insulation cabin in the robot working area to form a local closed micro-environment;

[0034] S4.4: Link the temperature control system with the movement, leveling and monitoring modules of the robot through the edge controller, and automatically start and stop heating and adjust the power according to the concrete temperature feedback.

[0035] The specific steps of S4.4 are:

[0036] S4.4.1: The edge controller receives the concrete surface temperature value T and the robot movement state parameters, including the travel speed V and the leveling mechanism pressure F, from the monitoring module in real time;

[0037] S4.4.2: Set the temperature threshold: when T < 5℃, automatically start the infrared radiation heating system, and adjust the initial power according to P0 = 1.2 × (5-T)kW; when T ≥ 15℃, automatically turn off the heating system;

[0038] S4.4.3: If the temperature T fluctuates in the range of 5-15℃ during heating, the edge controller adjusts the robot travel speed: when T < 10℃, reduce V by 10% to prolong the heating time; when T ≥ 10℃, restore V to the set value;

[0039] S4.4.4: Synchronize the leveling mechanism pressure F with the temperature feedback: when T < 8℃, increase the leveling mechanism pressure F by 5%, otherwise keep the reference pressure.

[0040] In S5, according to the characteristics of the working space under and around the stack bridge, the unmanned aerial vehicle infrared AI crack warning system and distributed optical fiber sensing technology are used to intelligently identify and monitor the micro-cracks on the concrete surface in real time, predict the durability based on damage theory and dynamically optimize the process parameters. The specific steps are as follows:

[0041] S5.1: According to the characteristics of the limited working space under and around the stack bridge, reasonably arrange the unmanned aerial vehicle inspection path and distributed optical fiber sensing network;

[0042] S5.2: Use unmanned aerial vehicles equipped with infrared thermal imagers for regular inspection, and analyze the collected images using artificial intelligence algorithms to automatically identify early micro-cracks and temperature anomalies on the concrete surface;

[0043] S5.3: Through the pre-embedded or surface-pasted distributed optical fiber sensors, continuously monitor the internal temperature field and strain field changes of the concrete, and accurately capture the development process of microstructure damage caused by freeze-thaw cycles;

[0044] S5.4: Based on the structural damage theory and monitoring data, establish a durability evolution model to predict the long-term performance degradation trend of concrete, and feed back the results to the construction control system to dynamically adjust the process parameters of subsequent finishing, curing.

[0045] The specific steps of S5.2 are:

[0046] S5.2.1: Set the unmanned aerial vehicle inspection parameters: for the space under the trestle and the surrounding space, use a flight height of 5-10 m, a cruising speed of 0.1 m / s, set the infrared thermal imager sampling frequency to 2 Hz, and the visible light camera and infrared thermal imager collect images synchronously;

[0047] S5.2.2: Preprocess the collected infrared images: including image denoising, temperature field calibration, and image stitching;

[0048] S5.2.3: Use an improved U-Net deep learning model to analyze the preprocessed images: the input is a visible-infrared fusion image, identify microcracks with a width of ≥0.2 mm through a multi-scale feature extraction module, and mark abnormal areas with a temperature difference of ≥5℃ through temperature gradient analysis;

[0049] S5.2.4: Verify the confidence of the identification results: when the crack identification confidence is ≥90% and the temperature anomaly area coincidence degree of 3 consecutive images is ≥80%, it is determined as valid identification results and the coordinate information is stored.

[0050] The specific steps of S5.4 are:

[0051] S5.4.1: Collect the internal temperature field, strain field, and microcrack development data of the concrete monitored by the distributed optical fiber sensor, input the monitoring data into the preset durability evolution model, and the durability evolution model uses a freeze-thaw damage function table:

[0052]

[0053] In the formula, D is the current freeze-thaw damage degree, E n The dynamic elastic modulus after the nth freeze-thaw cycle, E0 is the initial dynamic elastic modulus, and β is a material-related parameter. When the predicted D is >0.4 or the remaining life is less than 80% of the design life, the system automatically improves the curing and insulation levels and the finishing and compaction process parameters of the subsequent paragraphs;

[0054] S5.4.2: Predict the remaining life of concrete and the degradation trend of key performance indicators, and trigger an early warning when the predicted value is lower than the set threshold of the design benchmark;

[0055] S5.4.3: Feed back the warning signal and optimization suggestions to the construction control system, and automatically adjust the finishing density, number of troweling passes, and curing and insulation time of the subsequent operation section.

[0056] Compared with the prior art, the present application has the beneficial effects that:

[0057] The present application realizes high-precision operation by developing an intelligent finishing robot and a parameter correction mechanism suitable for high-altitude trestle working conditions, and solves the temperature control problem of concrete finishing in high-cold environments by constructing an intelligent temperature control platform. With the aid of intelligent monitoring and durability prediction, dynamic optimization of process parameters is realized, thereby improving the quality stability of concrete finishing, the environmental adaptability of equipment, and the intelligent level of construction under high-altitude inverted arch trestle working conditions, significantly reducing the surface defect rate and prolonging the structural durability. BRIEF DESCRIPTION OF DRAWINGS

[0058] Fig. 1 The flowchart of the method for controlling the effect of concrete finishing under inverted arch trestle working conditions according to the present application.

[0059] Fig. 2 The intelligent finishing robot control flowchart in the method for controlling the effect of concrete finishing under inverted arch trestle working conditions according to the present application.

[0060] Fig. 3 The concrete crack intelligent identification flowchart in the method for controlling the effect of concrete finishing under inverted arch trestle working conditions according to the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0062] Embodiment:

[0063] As Figs. 1-3As shown, the embodiment provides a method for controlling concrete finishing effect under inverted arch trestle working condition, comprising the following steps: S1: adjusting the trestle support stability and operation space layout according to the characteristics of high-altitude tunnel construction equipment and inverted arch working condition; S2: dynamically regulating the pouring thickness, slump and material uniformity in combination with the influence of high-cold and high-altitude environment on the initial setting time and fluidity of concrete; S3: based on the drilling and blasting method tunnel equipment coordinated control logic and the trestle space parameters adjusted in S1, developing a plateau type intelligent finishing robot adapted to the trestle working condition and establishing an altitude-equipment parameter correction database, and performing high-precision curved surface leveling through a pressurized power system and GNSS / IMU automatic navigation; S4: embedding the temperature control system in the intelligent finishing robot in S3 and the trestle auxiliary structure, and constructing an intelligent operation platform integrating the functions of "dynamic leveling, infrared radiation heating and micro-environmental heat preservation"; S5: according to the characteristics of the operation space below and around the trestle, using an unmanned aerial vehicle infrared AI crack warning system and distributed optical fiber sensing technology to intelligently identify and monitor the real-time freeze-thaw damage of the concrete surface micro-cracks, and dynamically optimizing the process parameters based on the damage theory to predict the durability.

[0064] In S1, the trestle support stability and operation space layout are adjusted according to the characteristics of high-altitude tunnel construction equipment and inverted arch working condition, and the specific steps are as follows: S1.1: analyzing the performance parameter changes of construction equipment in high-altitude environment, and determining the load demand of the trestle support system; S1.2: adjusting the trestle support spacing and reinforcement method according to the inverted arch construction load characteristics; S1.3: reasonably arranging the operation platform and passing space in combination with the inverted arch curved surface characteristics and construction process.

[0065] Further, it needs to be explained that the core influence of high-altitude environment (above 3000m altitude) on construction equipment includes: engine power attenuation: every 1000m increase, the power decreases by 8%~10% (for example, a 200kW device in plain area actually outputs about 144~160kW at 4000m altitude); equipment self-weight change: due to cold-proof and supercharging modification, the equipment self-weight increases by 5%~8% (for example, the conventional finishing machinery is 3t, and the plateau type is increased to 3.15~3.24t); operation range reduction: hydraulic system response delay, resulting in a 3%~5% reduction in mechanical arm operation radius.

[0066] S2 combines the influence of high-cold high-altitude environment on the initial setting time and fluidity of concrete, dynamically controls the pouring thickness, slump and material uniformity, and the specific steps are as follows: S2.1: analyze the influence of temperature and humidity factors in high-cold high-altitude environment on the initial setting time and fluidity of concrete; S2.2: according to the results of analyzing the influence of environment, dynamically adjust the key parameters of pouring thickness and slump of concrete for different construction stages, and optimize the material uniformity; S2.3: use sensor technology and real-time monitoring system to track the working performance indexes of initial setting time and fluidity of concrete, and quickly adjust the construction scheme on site through timely feedback data.

[0067] Further, it needs to be explained that the dynamic adjustment parameters in different construction stages are divided into three stages of "bottom pouring-middle leveling-top finishing", and the parameter differences of each stage are as follows: bottom layer (thickness 40-50 cm): slump 160-180 mm, material is pushed by "layer-by-layer pushing method" (pushing speed 0.5 m / min), and vibration compaction is ensured; middle layer (thickness 20-30 cm): slump 180-200 mm, material point spacing 1.2-1.5 m, "honeycomb-shaped point distribution" is adopted to avoid honeycomb; top layer (thickness 10-15 cm): slump 200-220 mm, initial leveling is carried out immediately after material distribution to avoid surface sanding caused by fast initial setting.

[0068] S3 is based on the drilling and blasting method tunnel equipment coordination control logic and the adjusted stack bridge space parameters in S1, develops a plateau type intelligent finishing robot suitable for stack bridge working conditions and establishes an altitude-equipment parameter correction database, carries out high-precision curved surface leveling through a supercharged power system and GNSS / IMU automatic navigation, and the specific steps are as follows: S3.1: according to the drilling and blasting method tunnel equipment coordination control logic and the adjusted stack bridge space parameters in S1, design and develop an intelligent finishing robot suitable for plateau environment and stack bridge working conditions; S3.2: based on the influence of environmental factors such as air density and temperature at different altitudes on equipment performance, construct a corresponding equipment parameter correction database; the specific steps are as follows: S3.2.1: collect environmental parameters at different altitudes, including atmospheric pressure, air density, environmental temperature and oxygen content; S3.2.2: simultaneously test the output efficiency of the power system of the plateau type intelligent finishing robot, the hydraulic response speed and the motor power attenuation data under the above environment; S3.2.3: establish a mapping relationship model between environmental parameters and equipment performance attenuation, and store it as a multi-dimensional lookup table or a function expression; S3.2.4: integrate the mapping relationship into the equipment control system to real-time correct the power output, walking speed and finishing operation parameters. S3.3: integrate a supercharged power system in the intelligent finishing robot; S3.4: use global navigation satellite system combined with inertial measurement unit to carry out accurate position information and attitude control.

[0069] Further, it needs to be explained that the design parameters of the plateau type intelligent floor finishing robot adapting to the stack bridge working condition: body size: folding state length ≤2.5m, width ≤1.2m (adapt to the 1.5m wide channel below the stack bridge), after unfolding the mop disc diameter is 1.5-1.8m (cover the width of the inverted arch curved surface); bearing capacity: ≥5t (match the stack bridge support load limit); working temperature range: -20℃ to 40℃ (resistant to high-cold environment). Motor power correction formula: P corr = P 电 0×(1+a×H / b×T) 电0 , wherein P 电 is the corrected target power, P0 is the rated power in plain, H is the altitude, a≈0.06-0.08, T1 is the environmental temperature, b≈0.002. The mapping relationship model of environmental parameters and equipment performance attenuation specific formula is: ① power system output efficiency correction formula: η=η0×(P / P1)×(273+T) / (273+T0), wherein η is the actual efficiency, η0 is the standard state efficiency (take 1), P is the measured air pressure, P1=101.325kPa (standard atmospheric pressure), T is the measured temperature, T0=20℃ (standard temperature). Motor power attenuation formula: P 电 =P 电0 0×(ρ / ρ0), wherein P 电 is the actual power, P 电0 is the standard power, ρ is the measured air density (kg / m 3 ), ρ0=1.225kg / m 3 (standard density).

[0070] In S4, the temperature control system is embedded in the intelligent finishing robot of S3 and the stack bridge auxiliary structure, to build an intelligent operation platform integrating the functions of "dynamic leveling, infrared radiation heating, and micro-environmental insulation". The specific steps are as follows: S4.1: embed the infrared radiation heating module and the insulation structure in the body of the intelligent finishing robot of S3 and the stack bridge auxiliary structure; S4.2: during the movement of the intelligent finishing robot, start the infrared radiation heating system synchronously, so that the concrete surface receives uniform heat radiation while being leveled; S4.3: set up a liftable or telescopic flexible insulation cabin in the operation area of the intelligent finishing robot, to form a locally closed micro-environment; S4.4: link the temperature control system with the movement, leveling, and monitoring modules of the robot through the edge controller, and automatically start and stop the heating and adjust the power according to the feedback of the concrete temperature. The specific steps are as follows: S4.4.1: the edge controller receives the feedback of the concrete surface temperature value T and the robot movement state parameters, including the travel speed V and the leveling mechanism pressure F, in real time; S4.4.2: set the temperature threshold: when T < 5℃, automatically start the infrared radiation heating system, and adjust the initial power according to P0 = 1.2 × (5-T)kW; when T ≥ 15℃, automatically turn off the heating system; S4.4.3: if the temperature fluctuates in the range of 5-15℃ during the heating process, the edge controller adjusts the travel speed of the robot: when T < 10℃, reduce V by 10% to prolong the heating time; when T ≥ 10℃, restore V to the set value; S4.4.4: associate the leveling mechanism pressure F with the temperature feedback: when T < 8℃, increase F by 5%, and vice versa, to maintain the reference pressure.

[0071] Further, it should be noted that the flexible insulation cabin adopts a fireproof canvas + steel framework structure (unfolding size 3m × 2m × 1.5m), which is lifted by a hydraulic rod, covers the operation area when it is folded, and the temperature inside the cabin is 10-15℃ higher than the ambient temperature.

[0072] S5.1: According to the characteristics of the limited operation space under and around the trestle, the unmanned aerial vehicle inspection path and the distributed optical fiber sensing network are reasonably arranged; S5.2: The unmanned aerial vehicle equipped with an infrared thermal imager is used for regular inspection, and the collected images are analyzed combined with artificial intelligence algorithm to automatically identify early microcracks and temperature abnormal areas on the concrete surface; the specific steps are as follows: S5.2.1: Set the unmanned aerial vehicle inspection parameters: for the space under and around the trestle, the flight height is 5-10 m, the cruising speed is 0.1 m / s, the infrared thermal imager sampling frequency is set to 2 Hz, and the visible light camera and the infrared thermal imager collect images synchronously; S5.2.2: Preprocess the collected infrared images: including image denoising, temperature field calibration, and image stitching; S5.2.3: Use the improved U-Net deep learning model to analyze the preprocessed images: the input is the visible light-infrared fusion image, the microcracks with a width of ≥0.2 mm are identified through the multi-scale feature extraction module, and the abnormal areas with a temperature difference of ≥5℃ are marked through temperature gradient analysis; S5.2.4: Verify the confidence of the identification results: when the crack identification confidence is ≥90% and the continuous 3-frame image coincidence degree of the temperature abnormal area is ≥80%, it is determined as valid identification result and the coordinate information is stored. S5.3: Through the pre-embedded or surface-pasted distributed optical fiber sensor, the internal temperature field and strain field changes of the concrete are continuously monitored, and the microstructure damage development process caused by freeze-thaw cycles is accurately captured; S5.4: Based on the structural damage theory and the monitoring data, a durability evolution model is established to predict the long-term performance degradation trend of the concrete, and the results are fed back to the construction control system to dynamically adjust the process parameters of subsequent finishing and curing. The specific steps are as follows: S5.4.1: Collect the internal temperature field, strain field and microcrack development data of the concrete monitored by the distributed optical fiber sensor, and input the monitoring data into the preset durability evolution model, and the durability evolution model is expressed by a freeze-thaw damage function:

[0073]

[0074] wherein D is the current freeze-thaw damage degree, E nThe dynamic elastic modulus after the nth freeze-thaw cycle, E0 is the initial dynamic elastic modulus, and β is a material-related parameter. When the prediction D>0.4 or the remaining service life is less than 80% of the design service life, the system automatically improves the maintenance and insulation level and the surface compaction process parameters of the subsequent paragraphs; S5.4.2: predicting the remaining service life of the concrete and the degradation trend of the key performance indicators, and triggering an early warning when the predicted value is lower than the set threshold of the design benchmark; S5.4.3: feeding back the early warning signal and optimization suggestions to the construction control system, and automatically adjusting the surface density, the number of troweling passes and the maintenance and insulation time length of the subsequent operation section.

[0075] Further, it needs to be explained that the specific method of infrared image preprocessing is as follows: image denoising: using median filter algorithm (window size 3x3) to remove salt and pepper noise in the infrared image; temperature field calibration: taking the measured value of the embedded temperature sensor as the benchmark to correct the infrared image by gray-scale-temperature mapping (error controlled within ±1℃); image stitching: based on SIFT feature point matching, the adjacent image overlapping area (≥30%) is spliced to generate a panoramic image (stitching error ≤2 pixels).

[0076] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0077] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for controlling the finishing effect of concrete in the inverted arch trestle working condition, characterized in that, The method comprises the following steps: S1: adjusting the support stability of the trestle and the layout of the operation space according to the characteristics of high-altitude tunnel construction equipment and the working conditions of inverted arches; S2: dynamically regulating the pouring thickness, slump and material uniformity by combining the influence of high-cold and high-altitude environment on the initial setting time and fluidity of concrete; S3: based on the coordinated control logic of drill-and-blast tunnel equipment and the adjusted trestle space parameters in S1, developing a highland-type intelligent surface collecting robot adapted to the working conditions of the trestle and establishing an altitude-equipment parameter correction database, and performing high-precision curved surface leveling through a supercharged power system and GNSS / IMU automatic navigation; S4: embedding the temperature control system in the intelligent surface collecting robot in S3 and the trestle auxiliary structure, and constructing an intelligent operation platform integrating the functions of "dynamic leveling, infrared radiation heating and micro-environmental heat preservation"; S5: for the characteristics of the operation space below and around the trestle, using an unmanned aerial vehicle infrared AI crack warning system and distributed optical fiber sensing technology to intelligently identify and monitor the real-time freezing and thawing damage of micro-cracks on the concrete surface, and dynamically optimizing the process parameters based on the damage theory to predict durability.

2. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 1, characterized in that, In S1, the support stability of the trestle and the layout of the operation space are adjusted according to the characteristics of high-altitude tunnel construction equipment and the working conditions of inverted arches, and the specific steps are as follows: S1.1: analyze the performance parameter changes of construction equipment in high-altitude environment, and determine the load demand of the trestle support system; S1.2: adjust the trestle support spacing and reinforcement method according to the inverted arch construction load characteristics; S1.3: reasonably layout the operation platform and traffic space in combination with the inverted arch curved surface characteristics and construction process.

3. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 1, characterized in that, In S2, the pouring thickness, slump and material uniformity are dynamically regulated by combining the influence of high-cold and high-altitude environment on the initial setting time and fluidity of concrete, and the specific steps are as follows: S2.1: analyze the influence of temperature and humidity factors in high-cold and high-altitude environment on the initial setting time and fluidity of concrete; S2.2: according to the results of analyzing the environmental influence, dynamically adjust the key parameters of concrete pouring thickness and slump for different construction stages, and optimize the material uniformity; S2.3: use sensor technology and real-time monitoring system to track the working performance indicators of the initial setting time and fluidity of concrete, and quickly adjust the construction scheme on site through timely feedback data.

4. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 1, characterized in that, In S3, based on the coordinated control logic of drill-and-blast tunnel equipment and the adjusted trestle space parameters in S1, a highland-type intelligent surface collecting robot adapted to the working conditions of the trestle is developed and an altitude-equipment parameter correction database is established, and high-precision curved surface leveling is performed through a supercharged power system and GNSS / IMU automatic navigation, and the specific steps are as follows: S3.1: according to the coordinated control logic of drill-and-blast tunnel equipment and the adjusted trestle space parameters in S1, design and develop an intelligent surface collecting robot suitable for highland environment and trestle working conditions; S3.2: based on the influence of air density and temperature environmental factors at different altitudes on equipment performance, construct a corresponding equipment parameter correction database; S3.3: integrate a supercharged power system in the intelligent surface collecting robot; S3.4: use global navigation satellite system combined with inertial measurement unit for accurate position information and attitude control.

5. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 4, characterized in that, The specific steps of S3.2 are as follows: S3.2.1: Collect environmental parameters at different altitudes, including atmospheric pressure, air density, ambient temperature and oxygen content; S3.2.2: Simultaneously test the output efficiency of the power system, the hydraulic response speed and the motor power attenuation data of the plateau-type intelligent surface collecting robot in the above environment; S3.2.3: Establish a mapping relationship model between environmental parameters and equipment performance attenuation, and store it as a multi-dimensional lookup table or function expression; S3.2.4: Integrate the mapping relationship into the device control system to real-time correct the power output, walking speed and surface collecting operation parameters.

6. The method for controlling the concrete finishing effect in the inverted arch trestle work condition according to claim 1, characterized in that, In S4, the temperature control system is embedded in the intelligent surface collecting robot and the stack bridge auxiliary structure of S3, to build an intelligent operation platform integrating "dynamic leveling, infrared radiation heating and micro-environmental insulation" functions. The specific steps are as follows: S4.1: Embed the infrared radiation heating module and insulation structure in the S3 intelligent surface collecting robot body and stack bridge auxiliary structure; S4.2: During the intelligent surface collecting robot's travel, start the infrared radiation heating system simultaneously to make the concrete surface receive uniform heat radiation while leveling; S4.3: Set up a liftable or telescopic flexible insulation cabin in the intelligent surface collecting robot's working area to form a locally closed micro-environment; S4.4: Link the temperature control system with the robot's movement, leveling and monitoring modules through the edge controller, and automatically start and stop heating and adjust the power according to the concrete temperature feedback.

7. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 6, characterized in that, The specific steps of S4.4 are as follows: S4.4.1: The edge controller receives the concrete surface temperature value T and the robot's movement state parameters, including travel speed V and leveling mechanism pressure F, from the monitoring module in real time; S4.4.2: Set the temperature threshold: when T < 5℃, automatically start the infrared radiation heating system, and adjust the initial power according to P0 = 1.2 × (5-T)kW; when T ≥ 15℃, automatically turn off the heating system; S4.4.3: If the temperature fluctuates in the range of 5-15℃ during heating, the edge controller adjusts the robot's travel speed: when T < 10℃, reduce V by 10% to prolong the heating time; when T ≥ 10℃, restore V to the set value; S4.4.4: Simultaneously associate the leveling mechanism pressure F with the temperature feedback: when T < 8℃, increase F by 5%, otherwise keep the baseline pressure.

8. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 1, characterized in that, In S5, according to the characteristics of the working space under and around the stack bridge, the unmanned aerial vehicle infrared AI crack warning system and distributed optical fiber sensing technology are used to intelligently identify and monitor the micro-cracks on the concrete surface in real time. Based on the damage theory, the durability is predicted and the process parameters are dynamically optimized. The specific steps are as follows: S5.1: According to the characteristics of the limited working space under and around the stack bridge, reasonably arrange the unmanned aerial vehicle inspection path and distributed optical fiber sensing network; S5.2: Use the unmanned aerial vehicle equipped with an infrared thermal imager for regular inspection, and analyze the collected images using artificial intelligence algorithms to automatically identify early micro-cracks and temperature anomalies on the concrete surface; S5.3: Continuously monitor the temperature field and strain field changes inside the concrete by pre-embedded or surface-pasted distributed optical fiber sensors, and accurately capture the development process of microstructure damage caused by freeze-thaw cycles; S5.4: Establish a durability evolution model based on structural damage theory and monitoring data to predict the long-term performance degradation trend of concrete, and feed the results back to the construction control system to dynamically adjust the process parameters of subsequent finishing, curing.

9. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 8, characterized in that, The specific steps of S5.2 are: S5.2.1: Set the unmanned aerial vehicle inspection parameters: for the space under and around the stack bridge, use a flight height of 5-10 m, a cruising speed of 0.1 m / s, and set the infrared thermal imager sampling frequency to 2 Hz. The visible light camera and infrared thermal imager collect images synchronously; S5.2.2: Preprocess the collected infrared images: including image denoising, temperature field calibration, and image stitching; S5.2.3: Use an improved U-Net deep learning model to analyze the preprocessed images: the input is a visible-infrared fusion image, identify microcracks with a width of ≥0.2 mm through a multi-scale feature extraction module, and mark abnormal areas with a temperature difference of ≥5°C through temperature gradient analysis; S5.2.4: Verify the confidence of the identification results: when the crack identification confidence is ≥90% and the temperature anomaly area has a 3-frame image coincidence degree of ≥80%, it is determined as an effective identification result and the coordinate information is stored.

10. The method for controlling the concrete finishing effect in the inverted arch trestle working condition according to claim 8, characterized in that, The specific steps of S5.4 are: S5.4.1: Collect the internal temperature field, strain field, and microcrack development data of the concrete monitored by the distributed optical fiber sensors, and input the monitoring data into the pre-set durability evolution model. The durability evolution model is expressed by a freeze-thaw damage function: In the formula, D is the current freeze-thaw damage degree, E n The dynamic elastic modulus after the nth freeze-thaw cycle, E0 is the initial dynamic elastic modulus, and β is a material-related parameter. When it is predicted that D>0.4 or the remaining service life is less than 80% of the design service life, the system automatically improves the maintenance and insulation level and the surface compaction process parameters of the subsequent paragraphs. S5.4.2: Predict the remaining life of the concrete and the degradation trend of key performance indicators. When the predicted value is lower than the set threshold of the design benchmark, an early warning is triggered; S5.4.3: Feed the warning signal and optimization suggestions back to the construction control system to automatically adjust the finishing density, number of troweling passes, and curing and insulation duration of the subsequent work sections.

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