Surface grouting construction method

Abstract

The application discloses a surface grouting construction method, comprising the following steps: S1, drilling a grouting hole at a designed position; S2, when grouting is performed on a target grouting hole, a monitoring rod is placed into a grouting hole around the target grouting hole, and a sound sensor is arranged on the monitoring rod; and S3, a sound signal collected by the sound sensor is acquired, a signal feature is extracted, and a grouting parameter of the target grouting hole is adjusted according to the signal feature. The application can monitor surface grouting construction and provide a basis for grouting parameter adjustment.

Description

Technical Field

[0001] This invention relates to the technical field of tunnel construction. More specifically, this invention relates to a surface grouting construction method. Background Technology

[0002] During tunnel construction, unfavorable geological formations are inevitable. To avoid impacting tunnel construction, surface grouting reinforcement is necessary for high-risk sections of the tunnel excavation. However, surface grouting is highly unreliable, lacking accurate monitoring of grout diffusion within the strata and timely assessment of construction quality. Adjustments to grouting parameters often rely solely on subjective judgment. Therefore, it is essential to design a technical solution that can overcome these shortcomings to some extent. Summary of the Invention

[0003] One objective of this invention is to provide a surface grouting construction method that can monitor the surface grouting construction and provide a basis for adjusting grouting parameters.

[0004] To achieve these objectives and other advantages of the present invention, according to one aspect of the present invention, a surface grouting construction method is provided, comprising: S1: drilling grouting holes at a designed location; S2: when grouting is performed on a target grouting hole, inserting a monitoring rod into grouting holes surrounding the target grouting hole, the monitoring rod being equipped with a sound sensor; S3: acquiring sound signals collected by the sound sensor, extracting signal features, and adjusting the grouting parameters for the target grouting hole according to the signal features.

[0005] Furthermore, in S2, the lower end of the monitoring rod is lowered to the bottom of the grouting hole, and multiple sound sensors are spaced apart on the monitoring rod. When grouting is performed on the target grouting hole, the sound sensor closest to the current grouting position is selected to acquire the sound signal.

[0006] Further, in S3, environmental noise in the sound signal is removed, a sound waveform is constructed, and the signal features representing slurry diffusion are extracted from the sound waveform.

[0007] Furthermore, the signal characteristics are compared with preset signal characteristics. If the error exceeds a predetermined range, the grouting parameters of the target grouting hole are adjusted. If the error is within a predetermined range, the grouting parameters are not adjusted.

[0008] Furthermore, the grouting parameters include grouting pressure and grouting volume.

[0009] Furthermore, the preset signal features are determined based on the grouting test. The sound signal is collected from the start of grouting to the point where the grout reaches the diffusion radius. Environmental noise in the sound signal is removed, a sound waveform diagram is constructed, the signal features are extracted from the sound waveform diagram, and the preset signal features are constructed.

[0010] Furthermore, a training set and a test set are established based on the grouting test. The training set includes grouting pressure, grouting volume, and corresponding signal features. A neural network model is constructed using the corresponding signal features as input and grouting pressure and grouting volume as output. When grouting is performed on the target grouting hole, the extracted signal features are input into the neural network model to obtain the apparent grouting pressure and apparent grouting volume. The grouting parameters are adjusted based on the apparent grouting pressure and apparent grouting volume.

[0011] Furthermore, the grouting hole is divided into an outer layer, a middle layer, and an inner layer from the outer periphery to the center. The monitoring rod is placed in the grouting hole in the middle layer. Grouting is first performed on the grouting hole in the outer layer, then on the grouting hole in the inner layer. Finally, the monitoring rod is removed in sequence, and grouting is performed on the grouting hole in the middle layer in sequence.

[0012] The present invention has at least the following beneficial effects:

[0013] This invention involves placing monitoring rods into surrounding grouting holes during grouting of a target grouting hole. The monitoring rods are equipped with sound sensors to acquire sound signals, extract signal characteristics, and adjust the grouting parameters for the target grouting hole based on these characteristics. This invention eliminates the need for additional holes for the monitoring rods, directly utilizing the grouting holes for sound monitoring, resulting in lower costs. By judging the grout diffusion effect based on the sound of grout diffusion, the construction quality can be fully understood, allowing for more objective adjustments to the grouting parameters.

[0014] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0015] Figure 1 A flowchart of one embodiment of this application;

[0016] Figure 2 This is a construction diagram for this application. Detailed Implementation

[0017] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0018] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0019] like Figure 1 , 2As shown, embodiments of this application provide a surface grouting construction method, including:

[0020] S1: Drill grouting holes 1 at the designed location; Optionally, according to the tunnel construction location, drill multiple grouting holes 1 above and on both sides of the tunnel construction location, and the multiple grouting holes 1 can be arranged in a quincunx pattern.

[0021] S2: When grouting is performed on the target grouting hole 1, a monitoring rod 3 is placed into the grouting holes 1 around the target grouting hole 1. The monitoring rod 3 is equipped with a sound sensor 4. Optionally, the monitoring rod 3 is hollow and has a cable inside for data transmission. The sound sensor 4 is installed on the side wall of the monitoring rod 3 and is connected to the cable. A grouting pipe 2 is lowered into the target grouting hole 1 for grouting. The grout is composed of water, cement and water glass.

[0022] S3: Acquire the sound signal collected by the sound sensor 4, extract the signal features, and adjust the grouting parameters for the target grouting hole 1 according to the signal features; Optionally, acquire the collected sound signal and extract the signal features using a server or terminal set on the ground surface; The diffusion of grout in the stratum compresses the rock and generates sound signals, so the signal features can reflect the diffusion effect of grout in the stratum; By analyzing the sound signals, the current grouting construction quality can be known, and then the grouting parameters can be adjusted;

[0023] As can be seen, this embodiment does not require the additional excavation of holes for placing the monitoring rod 3. It directly utilizes the grouting hole 1 for sound monitoring, which is low-cost. The sound of grout diffusion is used to judge the grout diffusion effect, which can provide a more comprehensive understanding of the construction quality and thus adjust the grouting parameters. The adjustment of grouting parameters is more objective and improves the construction quality.

[0024] In another embodiment, in S2, the lower end of the monitoring rod 3 is lowered to the bottom of the grouting hole 1. Multiple sound sensors 4 are spaced apart on the monitoring rod 3. When grouting is performed on the target grouting hole 1, the sound sensor 4 closest to the current grouting position is selected to obtain a sound signal. Optionally, each sound sensor 4 is numbered, and the lowering depth of the monitoring rod 3 is predetermined, thereby determining the depth of each sound sensor 4. During grouting, a sound sensor 4 of a suitable depth is selected according to the height of the grouting pipe 2 to monitor the grouting construction at the current position.

[0025] In another embodiment, in S3, environmental noise in the sound signal is removed, a sound waveform is constructed, and the signal features representing slurry diffusion are extracted from the sound waveform. Optionally, the environmental noise is the sound of the grouting equipment, and the sound containing only slurry diffusion is obtained and converted into a sound waveform. The signal features can be numerical values ​​such as peak amplitude and peak interval in the sound waveform.

[0026] One feasible approach is to compare the signal features with preset signal features. If the error exceeds a predetermined range, the grouting parameters of the target grouting hole 1 are adjusted; if the error is within the predetermined range, the grouting parameters are not adjusted. Optionally, the grouting parameters include grouting pressure and grouting volume. Optionally, the preset signal features are determined based on grouting tests. The sound signal from the start of grouting to the point where the grout reaches its diffusion radius is collected during the grouting test. Environmental noise in the sound signal is removed, and a sound waveform diagram is constructed. The signal features are extracted from the sound waveform diagram, and the preset signal features are constructed. The preset signal features and the corresponding sound signals are collected. The signal time periods are equal; the grouting test is carried out in an area with the same soil quality near grouting hole 1. The grouting pressure and grouting volume are adjusted, the diffusion radius is measured, and at the same time, under the same conditions as in the above embodiment, the sound signal is acquired, the signal characteristics are extracted, and after multiple tests, the grouting pressure, grouting volume and preset signal characteristics that can achieve the diffusion radius are determined; the preset signal characteristics are used as the standard, grouting is performed on the target grouting hole 1, the sound signal is collected, the signal characteristics are obtained, and compared with the preset signal characteristics. When the deviation from the preset signal characteristics is small, that is, within the predetermined range, no adjustment is made; otherwise, the grouting parameters are adjusted until the signal characteristics enter the predetermined range.

[0027] In another embodiment, a training set and a test set are established based on the grouting test. The training set includes grouting pressure, grouting volume, and corresponding signal features. A neural network model is constructed using the corresponding signal features as input and grouting pressure and grouting volume as output. When grouting is performed on the target grouting hole 1, the extracted signal features are input into the neural network model to obtain the apparent grouting pressure and apparent grouting volume. The grouting parameters are adjusted according to the currently required grouting pressure and grouting volume. This embodiment further provides a reference for adjusting the grouting pressure and grouting volume. After inputting the signal features into the neural network model, the apparent grouting pressure and apparent grouting volume are obtained, which are the grouting pressure and grouting volume sensed by the sound sensor 4 in the stratum through signal features. Ground construction personnel can adjust the grouting parameters according to the apparent grouting pressure and apparent grouting volume. If the apparent grouting pressure and apparent grouting volume are small, the grouting pressure and grouting volume need to be increased. The neural network can be a BP neural network, and the data for training the neural network can come from the grouting test. The soil layer, grout, and equipment used in the grouting test are the same as those used in the actual grouting.

[0028] In another embodiment, the grouting hole 1 is divided into an outer layer, a middle layer, and an inner layer from the outer periphery to the center. The monitoring rod 3 is placed in the grouting hole 1 of the middle layer. Grouting is first performed on the grouting hole 1 located in the outer layer, and then grouting is performed on the grouting hole 1 of the inner layer. Finally, the monitoring rod 3 is removed in sequence, and grouting is performed on the grouting hole 1 of the middle layer in sequence. Grouting is first performed on the grouting hole 1 of the outer layer. After the grout initially sets, a sealing surface is formed, which prevents the grout from spreading widely and causing waste when grouting the grouting holes 1 of the middle and inner layers, and improves the accuracy of monitoring. Grouting is then performed on the grouting hole 1 of the inner layer, which avoids changing the position of the monitoring rod 3. Finally, grouting is performed on the grouting hole 1 of the middle layer in sequence, and the monitoring rod 3 is removed in sequence.

[0029] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the surface grouting construction method of the present invention will be readily apparent to those skilled in the art.

[0030] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A surface grouting construction method, characterized in that, include: S1: Drill grouting holes at the designed locations; S2: When grouting is performed on the target grouting hole, a monitoring rod is placed into the grouting holes around the target grouting hole. The monitoring rod is equipped with a sound sensor. S3: Acquire the sound signal collected by the sound sensor, extract the signal features, and adjust the grouting parameters for the target grouting hole according to the signal features; The signal characteristics are compared with preset signal characteristics. If the error exceeds the predetermined range, the grouting parameters of the target grouting hole are adjusted. If the error is within the predetermined range, the grouting parameters are not adjusted. The grouting parameters include grouting pressure and grouting volume; The preset signal features are determined based on grouting tests. The sound signal is collected from the start of grouting to the point where the grout reaches the diffusion radius. Environmental noise in the sound signal is removed, a sound waveform diagram is constructed, the signal features are extracted from the sound waveform diagram, and the preset signal features are constructed. A training set and a test set are established based on the grouting test. The training set includes grouting pressure, grouting volume and corresponding signal features. The corresponding signal features are used as input and grouting pressure and grouting volume are used as output to construct a neural network model. When grouting is performed on the target grouting hole, the extracted signal features are input into the neural network model to obtain the apparent grouting pressure and apparent grouting volume, and the grouting parameters are adjusted according to the apparent grouting pressure and apparent grouting volume.

2. The surface grouting construction method as described in claim 1, characterized in that, In S2, the lower end of the monitoring rod is lowered to the bottom of the grouting hole. Multiple sound sensors are spaced apart on the monitoring rod. When grouting is performed on the target grouting hole, the sound sensor closest to the current grouting position is selected to acquire the sound signal.

3. The surface grouting construction method as described in claim 1, characterized in that, In S3, environmental noise in the sound signal is removed, a sound waveform is constructed, and the signal features representing slurry diffusion are extracted from the sound waveform.

4. The surface grouting construction method as described in claim 1, characterized in that, The grouting holes are divided into an outer layer, a middle layer, and an inner layer from the outer periphery to the center. The monitoring rod is placed in the grouting hole in the middle layer. Grouting is first performed on the grouting holes in the outer layer, then on the grouting holes in the inner layer. Finally, the monitoring rod is removed in sequence, and grouting is performed on the grouting holes in the middle layer in sequence.

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