Fabricated retaining wall stability analysis system and method based on cloud computing

Abstract

The invention relates to the technical field of engineering management, and discloses a fabricated retaining wall stability analysis system and method based on cloud computing, and the system comprises a multi-dimensional data module and an intelligent evaluation module. According to the fabricated retaining wall stability analysis system and method based on cloud computing, construction data of all fabricated retaining walls and environment monitoring data of all time points are obtained through a multi-dimensional data module, a data set is formed through classification, and an intelligent evaluation module analyzes the bearing coefficient of each group of fabricated retaining walls; according to the method, a deformation coefficient and a lateral thrusting coefficient are generated according to the deformation degree and the compression state of each group of fabricated retaining walls, whether the retaining walls have lateral instability risks or not is quantitatively evaluated, the influence of environmental factors on the long-term cumulative effect of wall stability is comprehensively considered, an influence coefficient is generated, and the multi-dimensional analysis precision is high; the intelligent evaluation module judges the bearing grade of the fabricated retaining wall and the structural strength and durability of the fabricated retaining wall, outputs corresponding judgment results, evaluates the current situation and predicts future changes, and is high in safety.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering management, and specifically to a prefabricated retaining wall stability analysis system and method based on cloud computing. Background Art

[0002] A prefabricated retaining wall is a retaining structure completed through factory prefabrication and on-site assembly. Its main functions are to support soil, control landslides, maintain slope stability, and prevent soil erosion. Due to advantages such as fast construction, controllable costs, and stable quality, prefabricated retaining walls are widely used in fields such as highways, railways, water conservancy, ports, and municipal engineering. A prefabricated retaining wall is assembled from prefabricated components, and common components include retaining wall panels, foundation units, connectors, and reinforcement units, etc. Commonly used materials include reinforced concrete, prestressed concrete, and ecological materials. In some projects, steel or plastic reinforcement materials are also used in combination to enhance performance. When the components are produced in the factory, they are made through standardized processes, ensuring precise dimensions and quality consistency, which is convenient for efficient on-site installation. At the same time, the shape and specifications of the components can be flexibly adjusted according to engineering requirements to adapt to different topographical conditions and load requirements. By factory-producing prefabricated components, the amount of in-situ cast concrete work is reduced, the construction period is shortened, and the dependence on environmental and climatic conditions is also reduced. The prefabricated components adopt standardized designs and can be reused or recycled, reducing resource waste. Prefabricated retaining walls need to bear complex load conditions, including earth pressure, seismic force, wind force, and water flow pressure, etc. If the stability is insufficient, it may cause the wall to slip, overturn, or be damaged, leading to soil instability and even disasters such as landslides and collapses, causing serious damage to the project and the surrounding environment. Especially in high slopes, large-volume retaining walls, or important infrastructure, stability is crucial.

[0003] Currently, traditional prefabricated retaining wall stability analysis systems usually use real-time monitoring data to evaluate the current state of the retaining wall. When predicting the change trend of the retaining wall structure, they often ignore the comprehensive impact of long-term cumulative factors on the wall structure, lack precise early warning management capabilities, and cannot effectively predict potential safety risks. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a prefabricated retaining wall stability analysis system and method based on cloud computing, which have the advantages of high accuracy in multi-dimensional analysis and high safety in intelligent early warning, and solve the problem that traditional prefabricated retaining wall stability analysis systems often ignore the comprehensive impact of long-term cumulative factors on the wall structure, thus lacking precise early warning management capabilities.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: A prefabricated retaining wall stability analysis system based on cloud computing, including a multi-dimensional data module and an intelligent evaluation module;

[0008] The multi-dimensional data module is composed of a construction data unit and an environmental data unit. The construction data unit collects a construction data set through a network-connected monitoring device. The construction data set includes the construction data of all prefabricated retaining walls. The environmental data unit collects an environmental data set through a network-connected monitoring device. The environmental data set includes the environmental monitoring data at all time points;

[0009] The intelligent evaluation module is composed of a quality analysis unit, a monitoring and evaluation unit, and a warning management unit. The quality analysis unit analyzes the bearing coefficient Czx of each group of prefabricated retaining walls according to the construction data set. The monitoring and evaluation unit sets a monitoring period zQ with a fixed duration, and then combines the construction data set and the environmental data set to analyze the deformation degree and compression state of each group of prefabricated retaining walls, and generates corresponding deformation coefficients Bxx and lateral thrust coefficients Ctx. The warning management unit analyzes the influence degree of various environmental factors on the prefabricated retaining walls according to the environmental data set, and generates corresponding influence coefficients Yxx. The warning management unit sets bearing thresholds CZY, deformation thresholds BXY, lateral thrust thresholds CTY, and influence thresholds YXY within a fixed range, and then combines the bearing coefficient Czx, deformation coefficient Bxx, lateral thrust coefficient Ctx, and influence coefficient Yxx to judge the bearing grade of the prefabricated retaining wall, as well as the structural strength and durability of the prefabricated retaining wall, and outputs corresponding judgment results.

[0010] Preferably, the expression of the construction data set is {Q1 d 、Q2 d 、Q3 d 、…、Qn d}, where Q1 d to Qn d are the construction data of the first group to the nth group of prefabricated retaining walls respectively. The construction data includes concrete compressive strength, steel bar yield strength, slurry bond strength, welding hardness, wall length, wall height, and wall thickness. d represents the completion time point of each group of prefabricated retaining walls.

[0011] Preferably, the expression of the environmental data set is {BX s 、TY s 、ZP s 、ZF s 、JL s}, where BX s represents the deformation amount, TY s represents the soil pressure, ZP s represents the vibration frequency, ZF s represents the vibration amplitude, and JL srepresents the runoff, and s represents the specific time point for obtaining environmental monitoring data.

[0012] Preferably, the calculation process of the bearing coefficient Czx is as follows:

[0013] Extract the construction data of the i-th precast retaining wall from the construction dataset, and mark the concrete compressive strength of the i-th precast retaining wall as HK i , mark the yield strength of the steel bars of the i-th precast retaining wall as GQ i , mark the slurry bonding strength of the i-th precast retaining wall as JN i , mark the welding hardness of the i-th precast retaining wall as HJ i , mark the wall height of the i-th precast retaining wall as GD i , mark the wall thickness of the i-th precast retaining wall as HD i ;

[0014]

[0015] In the formula, α 1 represents the evaluation weight for the concrete compressive strength, α 2 represents the evaluation weight for the yield strength of the steel bars, α 3 represents the evaluation weight for the slurry bonding strength, α 4 represents the evaluation weight for the welding hardness, represents the ratio of the wall height to the wall thickness, which is used to measure the anti-overturning ability of the i-th precast retaining wall, α 5 represents the evaluation weight for the ratio of the wall height to the wall thickness, α 1 + α 2 + α 3 + α 4 + α 5 = 1, represents obtaining the bearing coefficient Czx of the i-th precast retaining wall according to α 1 , α 2 , α 3 , α 4 and α 5 . i .

[0016] Preferably, the calculation process of the deformation coefficient Bxx is as follows:

[0017] According to the construction dataset, mark the completion time point of the i-th precast retaining wall as i d ;

[0018] According to the environmental dataset, count the environmental monitoring data of the i-th precast retaining wall within the monitoring period zQ, and mark the deformation amount of the i-th precast retaining wall as {bx 1, bx 2 , bx 3 , …, bx e}, bx 1 to bx e are the deformation amounts of the i-th group of prefabricated retaining walls during the first to the e-th monitoring respectively;

[0019]

[0020] In the formula, bx f represents the deformation amount of the i-th group of prefabricated retaining walls at the f-th monitoring during the monitoring period ZQ, and i s represents the specific time point of the environmental monitoring data of the i-th group of prefabricated retaining walls, and i s -i d represents the time difference between the monitoring time point and the completion time point, that is, the completion duration of the i-th group of prefabricated retaining walls, represents the ratio of the deformation amount to the completion duration, that is, the deformation coefficient Bxx of the i-th group of prefabricated retaining walls i .

[0021] Preferably, the calculation process of the lateral thrust coefficient Ctx is as follows:

[0022] According to the construction data set, mark the wall length of the i-th group of prefabricated retaining walls as CD i ;

[0023] According to the environmental data set, count the environmental monitoring data of the i-th group of prefabricated retaining walls during the monitoring period ZQ, and mark the earth pressure received by the i-th group of prefabricated retaining walls as {ty 1 , ty 2 , ty 3 , …, ty u},ty 1 to ty u are the earth pressures received by the i-th group of prefabricated retaining walls during the first to the u-th monitoring respectively;

[0024]

[0025] In the formula, CD i × GD i represents the compression area of the i-th group of prefabricated retaining walls, and ty g represents the earth pressure received by the i-th group of prefabricated retaining walls at the g-th monitoring during the monitoring period ZQ, represents the ratio of the earth pressure to the compression area, that is, the lateral thrust coefficient Ctx of the i-th group of prefabricated retaining walls i .

[0026] Preferably, the calculation process of the influence coefficient Yxx is as follows:

[0027] Extract the environmental monitoring data of the i-th group of prefabricated retaining walls according to the environmental data set, and mark the vibration frequency of the environment where the i-th group of prefabricated retaining walls is located as ZP i Mark the vibration amplitude of the environment where the i-th group of prefabricated retaining walls is located as ZF i Mark the runoff of the environment where the i-th group of prefabricated retaining walls is located as JL i ;

[0028] Yxx i =β 1 ×ZP i +β 2 ×ZF i +β 3 ×JL i +β 4 ×(i s -i d )

[0029] In the formula, β 1 represents the evaluation weight for the vibration frequency, β 2 represents the evaluation weight for the vibration amplitude, β 3 represents the evaluation weight for the runoff, β 4 represents the evaluation weight for the construction duration, β 1 +β 2 +β 3 +β 4 =1, β 1 ×ZP i +β 2 ×ZF i +β 3 ×JL i +β 4 ×(i s -i d ) represents the influence coefficient Yxx of the environment where the i-th group of prefabricated retaining walls is located obtained according to the weights of β 1 , β 2 , β 3 and β 4 . i .

[0030] Preferably, when the bearing coefficient Czx is lower than the bearing threshold CZY, the prefabricated retaining wall is of the third bearing grade; when the bearing coefficient Czx is included in the bearing threshold CZY, the prefabricated retaining wall is of the second bearing grade; when the bearing coefficient Czx is higher than the bearing threshold CZY, the prefabricated retaining wall is of the first bearing grade. The stability of the prefabricated retaining wall of the first bearing grade is higher than that of the second bearing grade, and the stability of the prefabricated retaining wall of the second bearing grade is higher than that of the third bearing grade.

[0031] Preferably, within the monitoring period zQ, when the deformation coefficient Bxx exceeds the deformation threshold BXY or the lateral thrust coefficient Ctx exceeds the lateral thrust threshold CTY, it indicates that the structural strength of the prefabricated retaining wall has been damaged. When the influence coefficient Yxx exceeds the influence threshold YXY, it indicates that the durability of the prefabricated retaining wall has been damaged.

[0032] A method for analyzing the stability of a prefabricated retaining wall based on cloud computing includes the following steps:

[0033] Step 1: Connect to the monitoring device through the network to obtain the construction data of all prefabricated retaining walls and the environmental monitoring data at all time points, and classify and form a construction data set and an environmental data set;

[0034] Step 2: According to the construction data set and the environmental data set, analyze the bearing coefficient Cz of each group of prefabricated retaining walls x , as well as the deformation degree and compression state of each group of prefabricated retaining walls, and generate the corresponding deformation coefficient Bx x and the lateral thrust coefficient Ct x ;

[0035] Step 3: According to the environmental data set, analyze the influence degree of various environmental factors on the prefabricated retaining wall, and generate the corresponding influence coefficient Yx x ;

[0036] Step 4: Set the bearing threshold CZY, deformation threshold BXY, lateral thrust threshold CTY and influence threshold YXY within a fixed range, and then combine the bearing coefficient Cz x , deformation coefficient Bx x , lateral thrust coefficient Ct x and influence coefficient Yx x to judge the bearing grade of the prefabricated retaining wall, as well as the structural strength and durability of the prefabricated retaining wall, and output the corresponding judgment results.

[0037] Compared with the prior art, the present invention provides a system and method for analyzing the stability of a prefabricated retaining wall based on cloud computing, and has the following beneficial effects:

[0038] 1. The present invention connects to the monitoring device through the multi-dimensional data module network to obtain the construction data of all prefabricated retaining walls and the environmental monitoring data at all time points, and classifies and forms a construction data set and an environmental data set. The intelligent evaluation module analyzes the bearing coefficient Cz of each group of prefabricated retaining walls according to the construction data set and the environmental data set x , as well as the deformation degree and compression state of each group of prefabricated retaining walls, and generates the corresponding deformation coefficient Bx x and the lateral thrust coefficient Ct x, perform weighted analysis on each material in the wall structure, and combine cloud computing technology to precisely manage all prefabricated retaining walls, quantitatively evaluate whether there is a risk of lateral instability of the retaining wall, and then analyze the influence degree of various environmental factors on the prefabricated retaining wall to generate the corresponding influence coefficient Yx x , establish a unified evaluation system to comprehensively consider the influence of environmental factors on the long-term cumulative effect of wall stability, and the multi-dimensional analysis has high accuracy.

[0039] 2. The present invention sets the bearing threshold CZY, deformation threshold BXY, lateral thrust threshold CTY, and influence threshold YXY within a fixed range through the intelligent evaluation module, and then combines the bearing coefficient Czx, deformation coefficient Bxx, lateral thrust coefficient Ctx, and influence coefficient Yxx to judge the bearing grade of the prefabricated retaining wall, as well as the structural strength and durability of the prefabricated retaining wall. The stability of the prefabricated retaining wall with the first bearing grade is higher than that of the second bearing grade, and the stability of the prefabricated retaining wall with the second bearing grade is higher than that of the third bearing grade. During the monitoring period ZQ, when the deformation coefficient Bxx exceeds the deformation threshold BXY or the lateral thrust coefficient Ctx exceeds the lateral thrust threshold CTY, it indicates that the structural strength of the prefabricated retaining wall has been damaged. When the influence coefficient Yxx exceeds the influence threshold YXY, it indicates that the durability of the prefabricated retaining wall has been damaged. It can not only evaluate the current situation of the retaining wall, but also predict the future stability change, issue an early warning in advance, reduce risks, and help extend the service life of the structure. The intelligent early warning has high safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of the system flow of the present invention;

[0041] Figure 2 is a diagram of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] Since the traditional prefabricated retaining wall stability analysis system usually evaluates the current state of the retaining wall based on real-time monitoring data, when predicting the structural change trend of the retaining wall, it often ignores the comprehensive influence of long-term cumulative factors on the wall structure, lacks accurate early warning management ability, and cannot effectively predict potential safety risks. Therefore, a prefabricated retaining wall stability analysis system and method based on cloud computing are provided. Please refer to Figure 1 , a prefabricated retaining wall stability analysis system based on cloud computing, including a multi-dimensional data module and an intelligent evaluation module;

[0044] The multi-dimensional data module consists of a construction data unit and an environmental data unit. The construction data unit collects a construction data set through a network-connected monitoring device. The construction data set includes the construction data of all precast retaining walls. The expression of the construction data set is {Q1 d , Q2 d , Q3 d , …, Qn d}, where Q1 d to Qn d are the construction data of the first group to the nth group of precast retaining walls respectively. The construction data includes concrete compressive strength, steel yield strength, slurry bond strength, welding hardness, wall length, wall height, and wall thickness. d represents the completion time point of each group of precast retaining walls. Accurately monitoring the construction quality of each group of precast retaining walls helps to quickly judge the bearing capacity of walls with different qualities in the follow-up;

[0045] The environmental data unit collects an environmental data set through a network-connected monitoring device. The environmental data set includes environmental monitoring data at all time points. The expression of the environmental data set is {BX s , TY s , ZP s , ZF s , JL s}, where BX s represents the deformation amount, TY s represents the earth pressure, ZP s represents the vibration frequency, ZF s represents the vibration amplitude, JL s represents the runoff. s represents the specific time point for obtaining environmental monitoring data. Long-term monitoring of the potential impact of the external environment on the stability of the retaining wall provides multi-dimensional data support for subsequent evaluation;

[0046] The intelligent evaluation module consists of a quality analysis unit, a monitoring and evaluation unit, and a warning management unit. The quality analysis unit analyzes the bearing coefficient Czx of each group of precast retaining walls according to the construction data set. The calculation process is as follows:

[0047] Extract the construction data of the ith group of precast retaining walls in the construction data set, and mark the concrete compressive strength of the ith group of precast retaining walls as HK i , mark the steel yield strength of the ith group of precast retaining walls as GQ i , mark the slurry bond strength of the ith group of precast retaining walls as J『N i , mark the welding hardness of the ith group of precast retaining walls as HJ i , mark the wall height of the ith group of precast retaining walls as GD i , and mark the wall thickness of the ith group of precast retaining walls as HD i ;

[0048]

[0049] In the formula, α 1 represents the evaluation weight for the compressive strength of concrete, α 2 represents the evaluation weight for the yield strength of steel bars, α 3 represents the evaluation weight for the bonding strength of the slurry, α 4 represents the evaluation weight for the welding hardness, represents the ratio of the wall height to the wall thickness, which is used to measure the anti-overturning ability of the i-th group of precast retaining walls, α 5 represents the evaluation weight for the ratio of the wall height to the wall thickness, α 1 +α 2 +α 3 +α 4 +α 5 = 1, represents that according to α 1 , α 2 , α 3 , α 4 and α 5 , the bearing coefficient Czx of the i-th group of precast retaining walls is obtained i . By performing weighted analysis based on each material in the wall structure and combining cloud computing technology, the bearing coefficient Czx of all precast retaining walls can be accurately managed, providing a timely and accurate basis for decision-making by all parties;

[0050] The monitoring and evaluation unit is set with a monitoring cycle ZQ of a fixed duration. By combining the construction data set and the environmental data set, the deformation degree and compressive state of each group of precast retaining walls are analyzed, and the corresponding deformation coefficient Bxx and lateral thrust coefficient Ctx are generated;

[0051] The calculation process of the deformation coefficient Bxx is as follows:

[0052] According to the construction data set, the completion time point of the i-th group of precast retaining walls is marked as i d ;

[0053] According to the environmental data set, during the monitoring cycle ZQ, the environmental monitoring data of the i-th group of precast retaining walls are statistically analyzed, and the deformation amounts of the i-th group of precast retaining walls are marked as {bx 1 , bx 2 , bx 3 , …, bx e}, where bx 1 to bx e are the deformation amounts of the i-th group of precast retaining walls during the first to the e-th monitoring respectively;

[0054]

[0055] In the formula, bx f represents the deformation of the i-th prefabricated retaining wall at the f-th monitoring during the monitoring period ZQ, where i s represents the specific time point of the environmental monitoring data of the i-th prefabricated retaining wall, where i s -i d represents the time difference between the monitoring time point and the completion time point, which is the completion duration of the i-th prefabricated retaining wall. represents the ratio of the deformation to the completion duration, which is the deformation coefficient Bxx of the i-th prefabricated retaining wall. i Specifically, the deformation includes but is not limited to the settlement amount, the inclination angle, and the number of cracks. The data source can be obtained by monitoring the appearance of the wall. According to the results of each monitoring, the deformation coefficient Bxx is updated in real time, which helps to accurately judge the stability of the retaining wall.

[0056] The calculation process of the lateral thrust coefficient Ctx is as follows:

[0057] According to the construction data set, mark the wall length of the i-th prefabricated retaining wall as CD. i ;

[0058] According to the environmental data set, count the environmental monitoring data of the i-th prefabricated retaining wall during the monitoring period zQ, and mark the earth pressure received by the i-th prefabricated retaining wall as {ty 1 , ty 2 , ty 3 , …, ty u}, where ty 1 to ty u are the earth pressures received by the i-th prefabricated retaining wall at the first to the u-th monitoring respectively.

[0059]

[0060] In the formula, CD i ×GD i represents the compression area of the i-th prefabricated retaining wall, and ty g represents the earth pressure received by the i-th prefabricated retaining wall at the g-th monitoring during the monitoring period ZQ. represents the ratio of the earth pressure to the compression area, which is the lateral thrust coefficient Ctx of the i-th prefabricated retaining wall. i Quantitatively evaluate whether there is a risk of lateral instability of the retaining wall, and provide a decision-making basis for later maintenance and reinforcement.

[0061] The early warning management unit analyzes the influence degree of various environmental factors on the prefabricated retaining wall according to the environmental data set, and generates the corresponding influence coefficient Yxx. The calculation process is as follows:

[0062] Extract the environmental monitoring data of the i-th group of prefabricated retaining walls according to the environmental data set, and mark the vibration frequency of the environment where the i-th group of prefabricated retaining walls is located as ZP i Mark the vibration amplitude of the environment where the i-th group of prefabricated retaining walls is located as ZF i Mark the runoff of the environment where the i-th group of prefabricated retaining walls is located as JL i ;

[0063] Yxx i =β 1 ×ZP i +β 2 ×ZF i +β 3 ×JL i +β 4 ×(i s -i d )

[0064] In the formula, β 1 represents the evaluation weight for the vibration frequency, β 2 represents the evaluation weight for the vibration amplitude, β 3 represents the evaluation weight for the runoff, β 4 represents the evaluation weight for the construction duration, β 1 +β 2 +β 3 +β 4 =1, β 1 ×ZP i +β 2 ×ZF i +β 3 ×JL i +β 4 ×(i s -i d ) represents the influence coefficient Yxxi of the environment where the i-th group of prefabricated retaining walls is located according to the weights of β 1 , β 2 , β 3 and β 4 , and establish a unified evaluation system to comprehensively consider the influence of environmental factors on the long-term cumulative effect of wall stability;

[0065] The early warning management unit is set with fixed-range bearing thresholds CZY, deformation thresholds BXY, lateral thrust thresholds CTY, and influence thresholds YXY. Combining with the bearing coefficient Czx, deformation coefficient Bxx, lateral thrust coefficient Ctx, and influence coefficient Yxx, it judges the bearing grade of the assembled retaining wall, as well as the structural strength and durability of the assembled retaining wall. When the bearing coefficient Czx is lower than the bearing threshold CZY, the assembled retaining wall is of the third bearing grade. When the bearing coefficient Czx is included in the bearing threshold CZY, the assembled retaining wall is of the second bearing grade. When the bearing coefficient Czx is higher than the bearing threshold CZY, the assembled retaining wall is of the first bearing grade. The stability of the assembled retaining wall of the first bearing grade is higher than that of the second bearing grade, and the stability of the assembled retaining wall of the second bearing grade is higher than that of the third bearing grade. Specifically, for assembled retaining walls of different bearing grades, the corresponding inspection cycles are also different. The inspection cycle of the retaining wall of the third bearing grade is shorter than that of the retaining wall of the second bearing grade. Within the monitoring cycle ZQ, when the deformation coefficient Bxx exceeds the deformation threshold BXY or the lateral thrust coefficient Ct x When it exceeds the lateral thrust threshold CTY, it indicates that the structural strength of the assembled retaining wall has been damaged. The influence coefficient Yx x When it exceeds the influence threshold YXY, it indicates that the durability of the assembled retaining wall has been damaged. It can not only evaluate the current situation of the retaining wall, but also predict the future stability changes, issue early warnings in advance, reduce risks, and help extend the service life of the structure.

[0066] Please refer to Figure 2 , the method for analyzing the stability of the assembled retaining wall based on cloud computing includes the following steps:

[0067] Step 1: Connect to the monitoring device through the network, obtain the construction data of all assembled retaining walls and the environmental monitoring data at all time points, and classify and form a construction data set and an environmental data set;

[0068] Step 2: According to the construction data set and the environmental data set, analyze the bearing coefficient Czx of each group of assembled retaining walls, as well as the deformation degree and compression state of each group of assembled retaining walls, and generate the corresponding deformation coefficient Bxx and lateral thrust coefficient Ctx;

[0069] Step 3: According to the environmental data set, analyze the influence degree of various environmental factors on the assembled retaining wall, and generate the corresponding influence coefficient Yxx, with high precision in multi-dimensional analysis;

[0070] Step 4: Set fixed-range bearing thresholds CZY, deformation thresholds BXY, lateral thrust thresholds CTY, and influence thresholds YXY. Combining with the bearing coefficient Czx, deformation coefficient Bxx, lateral thrust coefficient Ctx, and influence coefficient Yxx, judge the bearing grade of the assembled retaining wall, as well as the structural strength and durability of the assembled retaining wall, and output the corresponding judgment results, with high safety in intelligent early warning.

[0071] Example 1: In this experiment, a prefabricated retaining wall in the highway slope area was selected as the experimental object. After testing, the concrete compressive strength of this retaining wall is 30 MPa, the yield strength of the steel bars is 400 MPa, the bonding strength of the slurry is 2.5 MPa, the welding hardness is 1.8 MPa, the height of the wall is 5 m, and the thickness of the wall is 0.2 m. The calculation formula for the bearing coefficient Czx of this retaining wall is as follows:

[0072]

[0073] In the formula, represents the ratio of the wall height to the wall thickness, which is used to measure the anti-overturning ability of the retaining wall, α 1 +α 2 +α 3 +α 4 +α 5 = 1, α 1 、α 2 、α 3 、α 4 and α 5 are all 0.2, and the bearing coefficient Czx of this retaining wall is 86.868.

[0074] Example 2: In this experiment, a prefabricated retaining wall in the railway track area was selected as the experimental object. After three quarters of testing, after this retaining wall was built, the deformation amounts in each quarter were 2 mm, 3 mm, and 4 mm in sequence. The calculation formula for the deformation coefficient Bxx of this retaining wall is as follows:

[0075]

[0076] In the formula, i s -i d represents the time difference between the monitoring time point and the completion time point, that is, the completion duration of this retaining wall. When monitoring in the first quarter, the completion duration is 3 months. When monitoring in the second quarter, the completion duration is 6 months. When monitoring in the third quarter, the completion duration is 9 months. After the three-time monitoring is accumulated, the deformation coefficient of this retaining wall is 1.1.

[0077] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The prefabricated retaining wall stability analysis system based on cloud computing is characterized by: Including multi-dimensional data module and intelligent evaluation module; The multidimensional data module is composed of a construction data unit and an environment data unit. The construction data unit collects a construction data set through a network connection monitoring device, and the construction data set includes the construction data of all prefabricated retaining walls. The environment data unit collects an environment data set through a network connection monitoring device, and the environment data set includes the environment monitoring data at all time points. The intelligent evaluation module is composed of a quality analysis unit, a monitoring and evaluation unit and an early warning management unit. The quality analysis unit analyzes the bearing coefficient Czx of each group of prefabricated retaining walls according to the construction data set. The monitoring and evaluation unit is provided with a monitoring period ZQ of a fixed duration, and then combines the construction data set and the environmental data set to analyze the deformation degree and compression state of each group of prefabricated retaining walls, and generates corresponding deformation coefficients Bxx and thrust coefficients Ctx. The early warning management unit analyzes the influence of various environmental factors on the prefabricated retaining walls according to the environmental data set, and generates corresponding influence coefficients Yxx. The early warning management unit is provided with a fixed range of bearing thresholds CZY, deformation thresholds BXY, thrust thresholds CTY and influence thresholds YXY, and then combines the bearing coefficient Czx, deformation coefficient Bxx, thrust coefficient Ctx and influence coefficient Yxx to judge the bearing grade of the prefabricated retaining wall, as well as the structural strength and durability of the prefabricated retaining wall, and outputs the corresponding judgment results.

2. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 1, characterized in that: The expression of the construction data set is {Q1 d 、Q2 d 、Q3 d , ..., Qn d }, Q1 d To Qn d They are the construction data of the first to nth groups of prefabricated retaining walls, including concrete compressive strength, steel yield strength, slurry bond strength, welding hardness, wall length, wall height and wall thickness. d represents the construction time of each group of prefabricated retaining walls.

3. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 2, characterized in that: The expression of the environment data set is {BX s TY s , ZP s , ZF s , JL s }, BX s Indicates the deformation, TY s represents the earth pressure, ZP s Indicates the vibration frequency, ZF s Indicates the vibration amplitude, JL s represents the runoff volume, and s represents the specific time point for obtaining environmental monitoring data.

4. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 3 is characterized in that: The calculation process of the load factor Czx is as follows: Extract the construction data of the i-th group of prefabricated retaining walls in the construction data set, and mark the concrete compressive strength of the i-th group of prefabricated retaining walls as HK i , the steel bar yield strength of the i-th group of prefabricated retaining walls is marked as GQ i , the slurry bond strength of the i-th group of prefabricated retaining walls is marked as JN i , the welding hardness of the i-th group of prefabricated retaining walls is marked as HJ i , mark the wall height of the i-th group of prefabricated retaining walls as GD i , the wall thickness of the i-th group of prefabricated retaining walls is marked as HD i ; In the formula, α1 represents the evaluation weight for concrete compressive strength, α2 represents the evaluation weight for steel bar yield strength, α3 represents the evaluation weight for slurry bond strength, and α4 represents the evaluation weight for welding hardness. It represents the ratio of wall height to wall thickness, which is used to measure the anti-overturning capacity of the i-th group of prefabricated retaining walls. α5 represents the evaluation weight for the ratio of wall height to wall thickness. According to α1, α2, α3, α4 and α5, the bearing coefficient Czx of the i-th group of prefabricated retaining walls is obtained. i .

5. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 4, characterized in that: The calculation process of the deformation coefficient Bxx is as follows: According to the construction data set, the completion time of the i-th group of prefabricated retaining walls is marked as i d ; According to the environmental data set, the environmental monitoring data of the i-th group of prefabricated retaining walls in the monitoring period ZQ are counted, and the deformation of the i-th group of prefabricated retaining walls is marked as {bx 1 、bx 2 、bx 3 , ..., bx e }, bx 1 To bx e are the deformation amounts of the i-th group of prefabricated retaining walls from the first to the e-th monitoring respectively; In the formula, bx f represents the deformation of the i-th group of prefabricated retaining walls at the f-th monitoring within the monitoring period ZQ, i s represents the specific time point of the i-th group of prefabricated retaining wall environmental monitoring data, i s -i d represents the time difference between the monitoring time point and the completion time point, that is, the construction time of the i-th group of prefabricated retaining walls, It represents the ratio of deformation to construction time, which is the deformation coefficient Bxx of the i-th group of prefabricated retaining walls. i .

6. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 5, characterized in that: The calculation process of the thrust coefficient Ctx is as follows: According to the construction data set, the wall length of the i-th group of prefabricated retaining walls is marked as Cd i ; According to the environmental data set, the environmental monitoring data of the i-th group of prefabricated retaining walls in the monitoring period ZQ are counted, and the earth pressure on the i-th group of prefabricated retaining walls is marked as {ty 1 、ty 2 、ty 3 ,...,ty u },ty 1 To u are the earth pressures on the i-th group of prefabricated retaining walls from the first to the u-th monitoring; In the formula, CD i ×GD i represents the compression area of ​​the i-th group of prefabricated retaining walls, ty g It represents the earth pressure on the i-th group of prefabricated retaining walls during the g-th monitoring within the monitoring period ZQ. It represents the ratio of earth pressure to the compressed area, which is the thrust coefficient Ctx of the i-th group of prefabricated retaining walls. i .

7. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 6, characterized in that: The calculation process of the influence coefficient Yxx is as follows: According to the environmental data set, the environmental monitoring data of the i-th group of prefabricated retaining walls are extracted, and the vibration frequency of the environment where the i-th group of prefabricated retaining walls is located is marked as ZP i , the vibration amplitude of the environment where the i-th group of prefabricated retaining walls is located is marked as ZF i , the runoff volume of the environment where the i-th group of prefabricated retaining walls is located is marked as JL i ; Yxx i =β1×ZP i +β2×ZF i +β3×JL i +β4×(i s -i d ) In the formula, β1 represents the assessment weight for vibration frequency, β2 represents the assessment weight for vibration amplitude, β3 represents the assessment weight for runoff, and β4 represents the assessment weight for construction time. β1+β2+β3+β4=1, β1×ZP i +β2×ZF i +β3×JL i +β4×(i s -i d ) indicates that the influence coefficient Yxxi of the environment where the i-th group of prefabricated retaining walls is located is obtained according to the weights of β1, β2, β3 and β4.

8. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 7, characterized in that: When the bearing coefficient Czx is lower than the bearing threshold CZY, the prefabricated retaining wall is of the third bearing grade; when the bearing coefficient Czx is included in the bearing threshold CZY, the prefabricated retaining wall is of the second bearing grade; when the bearing coefficient Czx is higher than the bearing threshold CZY, the prefabricated retaining wall is of the first bearing grade. The stability of the prefabricated retaining wall of the first bearing grade is higher than that of the second bearing grade, and the stability of the prefabricated retaining wall of the second bearing grade is higher than that of the third bearing grade.

9. The cloud computing-based prefabricated retaining wall stability analysis system according to claim 8, characterized in that: During the monitoring period ZQ, when the deformation coefficient Bxx exceeds the deformation threshold BXY or the thrust coefficient Ctx exceeds the thrust threshold CTY, it indicates that the structural strength of the prefabricated retaining wall has been damaged; when the influence coefficient Yxx exceeds the influence threshold YXY, it indicates that the durability of the prefabricated retaining wall has been damaged.

10. A method for analyzing the stability of an assembled retaining wall based on cloud computing, applied to a system for analyzing the stability of an assembled retaining wall based on cloud computing as described in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Connect the monitoring device through the network to obtain the construction data of all prefabricated retaining walls and the environmental monitoring data at all time points, and classify them into construction data sets and environmental data sets; Step 2: According to the construction data set and the environmental data set, the bearing coefficient Czx of each group of prefabricated retaining walls, as well as the deformation degree and compression state of each group of prefabricated retaining walls are analyzed to generate the corresponding deformation coefficient Bxx and thrust coefficient Ctx; Step 3: Analyze the influence of various environmental factors on the prefabricated retaining wall according to the environmental data set, and generate the corresponding influence coefficient Yxx; Step 4: Set the fixed range of bearing threshold CZY, deformation threshold BXY, thrust threshold CTY and influence threshold YXY, and then combine the bearing coefficient Czx, deformation coefficient Bxx, thrust coefficient Ctx and influence coefficient Yxx to determine the bearing grade of the prefabricated retaining wall, as well as the structural strength and durability of the prefabricated retaining wall, and output the corresponding judgment results.

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