Microbial mineralized river sand strength non-destructive evaluation method and system

Abstract

The invention relates to the technical field of material performance detection, and discloses a non-destructive evaluation method and system for the strength of microorganism mineralized river sand, and the method comprises the following steps: applying a sweep frequency sound wave excitation signal to a microorganism mineralized river sand standard sample model; performing penetration simulation operation on the microorganism mineralized river sand standard sample model according to the sweep frequency sound wave excitation signal; extracting mineralization specific characteristic parameters corresponding to the sample model through the penetration response signal; according to the mineralization specificity characteristic parameters, constructing a biological cementation index of microorganism mineralization, and according to the biological cementation index, analyzing the compressive strength of the microorganism mineralization river sand standard sample model; constructing a destructive calibration curve of the microorganism mineralized river sand standard sample model; the target compressive strength of the to-be-tested sample model is analyzed through the destructive calibration curve, and the strength non-destructiveness of the microorganism river sand corresponding to the to-be-tested sample model is analyzed based on the target compressive strength. According to the method, the accuracy of non-destructive evaluation of the strength of the microorganism mineralized river sand can be improved.

Description

Technical Field

[0001] This invention relates to the field of material performance testing technology, and in particular to a non-destructive method and system for evaluating the strength of microbially mineralized river sand. Background Technology

[0002] In the fields of civil engineering and geological engineering, microbial mineralization technology, as a green and environmentally friendly foundation reinforcement method, has been widely used in engineering scenarios such as river sand foundation improvement and dam seepage prevention. This technology uses minerals produced by microbial metabolism to cement river sand particles, thereby improving the overall structural strength. Its core lies in precisely controlling the mineralization reaction process to ensure project quality.

[0003] In existing technologies, the strength assessment of microbially mineralized river sand largely relies on destructive sampling and testing, i.e., inferring the overall strength by obtaining partial samples for compressive strength testing. This method has significant technical drawbacks. On the one hand, the sampling process disrupts the overall structure of the river sand, resulting in test results that cannot accurately reflect the actual mineralization state. On the other hand, due to the spatiotemporal inhomogeneity of the mineralization reaction, the test results of local samples are difficult to represent the overall mineralization strength distribution, which can easily lead to assessment bias and affect engineering safety. Summary of the Invention

[0004] This invention provides a non-destructive assessment method and system for the strength of microbially mineralized river sand, the main purpose of which is to solve the problem of low accuracy in non-destructive assessment of the strength of microbially mineralized river sand.

[0005] To achieve the above objectives, the present invention provides a non-destructive method for assessing the strength of microbially mineralized river sand, comprising: Based on the pre-acquired mineralization properties, a standard sample model of microbial mineralized river sand and a sample model to be tested are constructed, and a preset sweep frequency acoustic excitation signal is applied to the microbial mineralized river sand standard sample model. The microbial mineralized river sand standard sample model was subjected to a penetration simulation operation based on the frequency sweeping acoustic excitation signal to obtain the penetration response signals of the microbial mineralized river sand at different reaction stages. Mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model were extracted by using the penetration response signals at different reaction stages. The biocementation index of microbial mineralization was constructed based on the specific mineralization characteristic parameters, and the compressive strength of the standard sample model of microbial mineralized river sand was analyzed based on the biocementation index. The destructive calibration curve of the standard sample model of the microbial mineralized river sand was constructed based on the biocementation index and the compressive strength. The target compressive strength of the test sample model is analyzed using the destructive calibration curve, and the non-destructive strength of the microbial river sand corresponding to the test sample model is analyzed based on the target compressive strength.

[0006] Optionally, the construction of a standard sample model of microbially mineralized river sand based on pre-acquired mineralization properties includes: The spatiotemporal gradient of mineralization reaction concentration was analyzed based on the microbial metabolic characteristics in the mineralization properties. A layered injection strategy for generating a standard sample model of microbially mineralized river sand based on the spatiotemporal variation gradient; According to the layered infusion strategy, the microbial inoculum and river sand are infused and solidified in a spatial sequence within a preset simulated space. Based on the spatiotemporal variation gradient, the microbial liquid and river sand after solidification simulation were adjusted in reverse, and a standard sample model of microbial mineralized river sand was generated according to the parameter attributes corresponding to the adjusted microbial liquid and river sand.

[0007] Optionally, applying a preset frequency-sweeping acoustic excitation signal to the microbial mineralized river sand standard sample model includes: The focal region and scanning path of acoustic excitation are dynamically divided based on the concentration gradient distribution of the microbial mineralized river sand standard sample model during the solidification process. The beamforming parameters of the preset multi-element acoustic transducer array are adaptively adjusted according to the scanning path. A swept-frequency acoustic excitation signal is applied in the focal region based on the beamforming parameters.

[0008] Optionally, the step of performing a penetration simulation operation on the standard sample model of microbially mineralized river sand based on the swept-frequency acoustic excitation signal to obtain the penetration response signals of different reaction stages of the microbially mineralized river sand includes: When the frequency sweeping acoustic excitation signal is applied, the vibration micro-change signal corresponding to the surface target point of the microbial mineralized river sand standard sample model is collected simultaneously. The acoustic signal after the penetration simulation operation is divided into different types of wave components, and the signal energy attenuation curves of different types of wave components at different spatial locations on the scanning path are extracted. The signal energy attenuation curve and the vibration micro-variation signal are time-domain aligned and fused to generate a penetration-enhanced response signal. Extract signal segments corresponding to different reaction stages of microbial mineralization of river sand from the penetration-enhanced response signal; The response signal corresponding to the signal segment is used as the penetration response signal for different reaction stages.

[0009] Optionally, the step of extracting mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through penetration response signals at different reaction stages includes: Calculate the ratio of longitudinal to transverse wave velocities of the penetration response signal at different reaction stages, and analyze the variance of the wave velocity ratio and the spatial position change of the penetration response signal. The biological cementation spatial distribution index of the microbial mineralized river sand standard sample model is determined based on the variance. Extract the dispersion curve features of the surface wave components of the penetration response signal at different reaction stages, and calculate the deviation between the dispersion curve features and the preset standard elastic half-space model. The spatial distribution index of biocementation, the wave velocity ratio, and the deviation are used as the mineralization-specific characteristic parameters.

[0010] Optionally, constructing the biocementing index of microbial mineralization based on the mineralization-specific characteristic parameters includes: Identify the mineralization reaction stages of microorganisms, and select dynamic weight coefficients corresponding to different mineralization reaction stages from a preset coefficient mapping table based on the dominant cementing parameters corresponding to the mineralization reaction stages. The wave velocity ratio and deviation in the mineralization-specific characteristic parameters are weighted according to the dynamic weighting coefficient. The weighted result is then calculated with the biocementation spatial distribution index in the mineralization-specific characteristic parameters to obtain the biocementation index of microbial mineralization.

[0011] Optionally, the destructive calibration curve for constructing the standard sample model of the microbially mineralized river sand based on the biocementation index and the compressive strength includes: A sound wave simulation was performed on the pre-set target standard sample model, and the target bio-cementation index after the sound wave simulation was calculated. The destructive compressive strength of the pre-set target standard specimen model is simulated to obtain the true compressive strength; A pre-set Gaussian process regression model is trained using the target bio-cementation index and the actual compressive strength, and a probabilistic mapping relationship is determined based on the trained Gaussian process regression model. The destructive calibration curve of the microbial mineralized river sand standard sample model is constructed based on the probabilistic mapping relationship.

[0012] Optionally, the step of analyzing the target compressive strength of the test sample model using the destructive calibration curve includes: Acquire the multimodal response data of the test sample model under acoustic excitation; The multimodal response data is subjected to spatiotemporal synchronization and fusion processing, and the fused target mineralization feature parameters are extracted. The dynamic biocementation index of the test sample model is calculated based on the target mineralization characteristic parameters. The target intensity distribution corresponding to the dynamic bio-cementation index was analyzed using the destructive calibration curve. The variance of the target strength distribution is used as the confidence index of the test sample model, and the target compressive strength of the test sample model is determined based on the confidence index and the expected value of the target strength distribution.

[0013] Optionally, the non-destructive analysis of the microbial river sand strength corresponding to the test sample model based on the target compressive strength includes: Based on the target compressive strength and the confidence index, destructive location is performed in a preset multidimensional strength decision space to obtain the destruction location point; Based on the damage location points, a pre-generated microbial mineralization health status map is queried to obtain the intensity level of the model to be tested. Based on the convergence and fluctuation characteristics of the dynamic biocementation index, the completion and stability of the test sample model for the mineralization reaction are analyzed. The strength and non-destructive state of microbial river sand were analyzed based on the strength grade, the completion degree, and the stability.

[0014] To address the above problems, the present invention also provides a non-destructive assessment system for the strength of microbially mineralized river sand, the system comprising: The sample model construction module is used to construct a standard sample model of microbial mineralized river sand and a sample model to be tested based on the pre-acquired mineralization properties, and to apply a preset sweep frequency acoustic excitation signal to the standard sample model of microbial mineralized river sand. The penetration simulation operation module is used to perform penetration simulation operation on the microbial mineralized river sand standard sample model according to the frequency sweeping acoustic excitation signal, and obtain the penetration response signals of the microbial mineralized river sand at different reaction stages. The mineralization-specific characteristic parameter extraction module is used to extract the mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through the penetration response signals of different reaction stages. The compressive strength analysis module is used to construct the biocementation index of microbial mineralization based on the mineralization-specific characteristic parameters, and to analyze the compressive strength of the microbial mineralized river sand standard sample model based on the biocementation index. The destructive calibration curve construction module is used to construct the destructive calibration curve of the microbial mineralized river sand standard sample model based on the biocementation index and the compressive strength. The microbial river sand strength non-destructive analysis module is used to analyze the target compressive strength of the test sample model using the destructive calibration curve, and to analyze the non-destructive strength of the microbial river sand corresponding to the test sample model based on the target compressive strength.

[0015] This invention achieves precise simulation and excitation of the mineralization process by constructing a standard sample model and applying a swept-frequency acoustic excitation signal. Penetration response signals at different reaction stages are obtained through penetration simulation, enriching the information reflecting mineralization characteristics. Extracted mineralization-specific characteristic parameters quantify mineralization features from multiple dimensions, providing accurate indicators for subsequent evaluation. The constructed biocementation index integrates various characteristic parameters, enabling intuitive evaluation of the degree of mineralization and analyzing compressive strength accordingly, solving the problem of a single evaluation method. The constructed destructive calibration curve establishes the correlation between the biocementation index and compressive strength, providing a standard for non-destructive evaluation. Analyzing the target compressive strength of the test sample model using the destructive calibration curve and evaluating its strength non-destructively achieves non-destructive detection, solving the problem of relying on destructive sampling in existing technologies and improving evaluation efficiency and accuracy. Therefore, the non-destructive evaluation method and system for the strength of microbially mineralized river sand proposed in this invention can solve the problem of low accuracy in non-destructive evaluation of the strength of microbially mineralized river sand. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a non-destructive method for assessing the strength of microbially mineralized river sand according to an embodiment of the present invention. Figure 2 This is a functional block diagram of a non-destructive assessment system for the strength of microbially mineralized river sand provided in an embodiment of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a non-destructive assessment method for the strength of microbially mineralized river sand. The execution subject of this non-destructive assessment method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the non-destructive assessment method for the strength of microbially mineralized river sand can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a non-destructive assessment method for the strength of microbially mineralized river sand according to an embodiment of the present invention. In this embodiment, the non-destructive assessment method for the strength of microbially mineralized river sand includes: S1. Construct a standard sample model and a test sample model of microbial mineralized river sand based on the pre-acquired mineralization properties, and apply a preset sweep frequency acoustic excitation signal to the microbial mineralized river sand standard sample model.

[0021] In this embodiment of the invention, mineralization properties refer to the characteristic parameters exhibited by microorganisms during the mineralization reaction process, including microbial species, metabolic rate, mineralization amount, etc.; the microbial mineralized river sand standard sample model refers to a model constructed based on known mineralization properties and used as a reference standard.

[0022] In this embodiment of the invention, the construction of a standard sample model of microbially mineralized river sand based on pre-acquired mineralization properties includes: The spatiotemporal gradient of mineralization reaction concentration was analyzed based on the microbial metabolic characteristics in the mineralization properties. A layered injection strategy for generating a standard sample model of microbially mineralized river sand based on the spatiotemporal variation gradient; According to the layered infusion strategy, the microbial inoculum and river sand are infused and solidified in a spatial sequence within a preset simulated space. Based on the spatiotemporal variation gradient, the microbial liquid and river sand after solidification simulation were adjusted in reverse, and a standard sample model of microbial mineralized river sand was generated according to the parameter attributes corresponding to the adjusted microbial liquid and river sand.

[0023] In detail, metabolic data during the microbial mineralization process are collected in real time using monitoring equipment, such as hourly data on urease activity. Based on this data, a correlation model is established between metabolic characteristics and mineralization, for example, the relationship between urease activity and calcium carbonate formation rate. Finally, based on this model, the changes in mineral concentration at different times and spatial locations are calculated, yielding a spatiotemporal gradient. For example, in the initial stage, the mineral concentration on the surface of river sand increases by 0.2 g / cm³ per hour, while in the deeper layers it increases by 0.1 g / cm³ per hour, forming a specific spatiotemporal gradient. This gradient accurately reflects the dynamic distribution of the mineralization reaction. Microbial metabolic characteristics refer to the characteristics of microbial metabolism during growth and reproduction, such as acid production rate and enzyme activity. The spatiotemporal gradient of mineralization concentration refers to the rate of change of mineral concentration at different times and spatial locations.

[0024] Specifically, the stratified injection strategy refers to an injection scheme for microbial inoculum and river sand based on different spatial levels. According to the spatiotemporal gradient, the space of the standard sample model is divided into multiple levels, each corresponding to a different rate of change in mineralization concentration. For example, the model can be divided into three layers from top to bottom: the first layer corresponds to the region with rapid mineralization concentration growth, the second layer to the region with moderate growth, and the third layer to the region with slow growth. Different injection parameters, such as injection rate and inoculum concentration, are then set for each layer. The stratified injection strategy ensures that the mineralization reaction proceeds according to the expected spatiotemporal gradient, avoiding the problem of poor mineralization results caused by uneven injection.

[0025] Furthermore, within the pre-defined three-dimensional simulation space, following the hierarchical sequence determined by the layered injection strategy, injection is performed sequentially starting from the top layer. First, river sand is filled into the simulation space and compacted. Then, microbial inoculum is injected into the first layer through injection pipes at a set rate and concentration. Simultaneously, the solidification simulation device is activated, controlling environmental parameters such as temperature and humidity, for example, maintaining the temperature at 25℃ and the humidity at 80%, to simulate the solidification process. Once the first layer of injection and solidification reaches a certain stage, such as reaching a set mineralization concentration of 30%, the second layer of injection and solidification is then performed, and so on. This ensures that the standard sample model can realistically reflect the actual mineralization reaction, linking the model to the actual scenario. The simulation space refers to a virtual or physical experimental space used to simulate the actual mineralization environment.

[0026] Furthermore, the parameter attributes refer to the characteristic parameters of the microbial inoculum and river sand after solidification, such as porosity and cementation degree. Based on the spatiotemporal gradient, the difference between the actual and expected mineralization concentrations at each layer after the solidification simulation is compared. If the actual concentration at a certain layer is lower than expected, a certain amount of microbial inoculum is added to that layer; the amount added is calculated based on the concentration difference. If the actual concentration is higher than expected, the amount of river sand added is increased to adjust it. After adjustment, the parameter attributes of each layer are measured, such as porosity between 20% and 30% and cementation degree between 50% and 70%. These parameter attributes are then integrated to generate a standard sample model of microbially mineralized river sand.

[0027] In addition, the test sample model refers to the model constructed for detection and evaluation based on the initial state of microbial mineralized river sand in actual engineering. The steps for generating the test model are the same as those for generating the standard sample model of microbial mineralized river sand, and will not be repeated here.

[0028] In this embodiment of the invention, the swept-frequency acoustic excitation signal refers to an acoustic signal whose frequency changes continuously within a certain range and is used to excite the model to obtain a response.

[0029] In this embodiment of the invention, applying a preset sweeping acoustic excitation signal to the microbial mineralized river sand standard sample model includes: The focal region and scanning path of acoustic excitation are dynamically divided based on the concentration gradient distribution of the microbial mineralized river sand standard sample model during the solidification process. The beamforming parameters of the preset multi-element acoustic transducer array are adaptively adjusted according to the scanning path. A swept-frequency acoustic excitation signal is applied in the focal region based on the beamforming parameters.

[0030] In detail, the concentration gradient distribution data of the standard sample model at various moments during the curing process are acquired through monitoring equipment. The concentration gradient distribution refers to the change in mineral concentration at different locations on the model. Areas with more dramatic concentration changes are identified as focal regions, which are the key areas for acoustic excitation, such as areas where the mineral concentration changes by more than 0.15 g / cm³ per hour. Based on the distribution of focal regions, a scanning path is planned. The scanning path refers to the propagation path of the acoustic waves on the model, ensuring that the acoustic waves can cover all focal regions and that the path is as short as possible to improve detection efficiency. For example, a spiral scanning path is used, gradually expanding outward from the center of the model, so that the acoustic excitation can be targeted at key areas.

[0031] Specifically, a multi-element acoustic transducer array refers to an array composed of multiple acoustic transducers used to transmit and receive acoustic signals. Beamforming parameters are parameters that affect the shape and direction of the acoustic beam, such as element spacing and phase difference. Based on the direction of the scanning path and the location of the focal region, the phase and amplitude of each element in the multi-element acoustic transducer array are calculated in real time. For example, when the scanning path turns towards the focal region on the right side of the model, the phase of the right-side element is adjusted to focus the emitted acoustic beam to the right, while the element spacing is adjusted to adapt the beamwidth to the size of the focal region. Through this adaptive adjustment, it is ensured that the acoustic beam accurately reaches each focal region along the scanning path, improving the accuracy of acoustic excitation.

[0032] Furthermore, based on the adjusted beamforming parameters, the multi-element acoustic transducer array is controlled to emit a swept-frequency acoustic excitation signal. If the frequency range of this signal is set to 1kHz-10kHz, it propagates along a predetermined scanning path within the focal region. For example, in the first focal region, the acoustic frequency gradually increases from 1kHz to 5kHz, and then in the second focal region, the frequency increases from 5kHz to 10kHz. By applying swept-frequency acoustic excitation to the focal region, the model can be excited to generate rich response signals, providing sufficient data for subsequent penetration simulation operations.

[0033] Furthermore, the standard sample model is used to provide a reference benchmark with known mineralization properties, while applying a swept-frequency acoustic excitation signal to it is to obtain the response characteristics of the model under different acoustic waves, and the response characteristics will serve as the basis for subsequent analysis of mineralization intensity.

[0034] S2. Perform a penetration simulation operation on the microbial mineralized river sand standard sample model according to the frequency sweeping acoustic excitation signal to obtain the penetration response signals of different reaction stages of the microbial mineralized river sand.

[0035] In this embodiment of the invention, the penetration simulation operation refers to the process of simulating the passing of a frequency-sweeping acoustic excitation signal through a microbial mineralized river sand standard sample model, and the penetration response signal refers to the response signal generated after the acoustic wave passes through the model.

[0036] In this embodiment of the invention, the step of performing a penetration simulation operation on the standard sample model of microbially mineralized river sand based on the frequency-sweeping acoustic excitation signal to obtain penetration response signals of different reaction stages of microbially mineralized river sand includes: When the frequency sweeping acoustic excitation signal is applied, the vibration micro-change signal corresponding to the surface target point of the microbial mineralized river sand standard sample model is collected simultaneously. The acoustic signal after the penetration simulation operation is divided into different types of wave components, and the signal energy attenuation curves of different types of wave components at different spatial locations on the scanning path are extracted. The signal energy attenuation curve and the vibration micro-variation signal are time-domain aligned and fused to generate a penetration-enhanced response signal. Extract signal segments corresponding to different reaction stages of microbial mineralization of river sand from the penetration-enhanced response signal; The response signal corresponding to the signal segment is used as the penetration response signal for different reaction stages.

[0037] In detail, the target points on the surface refer to specific points selected on the surface of the sample model for signal acquisition. Multiple target points are uniformly selected on the surface of the sample model, for example, one acquisition point is set every 5 centimeters on the model surface. While the multi-element acoustic transducer array emits a swept-frequency acoustic excitation signal, vibration sensors installed on the target points acquire vibration micro-variation signals in real time. The sampling frequency is set to 10 kHz to ensure that minute vibration changes can be captured. The vibration micro-variation signal refers to the minute vibration signal generated by the target point under acoustic excitation. The synchronous acquisition process can obtain the vibration information of the model surface caused by acoustic excitation, which supplements the deficiencies of the penetrating acoustic signal. Wave components refer to the different types of waves contained in an acoustic signal, such as longitudinal waves, transverse waves, and surface waves. The acoustic signal received after the penetration simulation operation is processed using wavelet decomposition and other methods to decompose it into different types of wave components, such as longitudinal waves, transverse waves, and surface waves. Then, based on the spatial coordinates along the scanning path, the energy value of each wave component at different locations is calculated. For example, the energy value of the longitudinal wave is recorded every 2 centimeters along the scanning path. With spatial position as the abscissa and energy value as the ordinate, a signal energy attenuation curve for each wave component is plotted. The signal energy attenuation curve is the curve showing how the energy of the wave component changes with spatial position during propagation.

[0038] Specifically, based on the timestamp of signal acquisition, the signal energy attenuation curve and the vibration micro-variation signal are aligned in time to ensure that signals at the same moment correspond. Then, a data fusion algorithm, such as the weighted average method, is used to fuse the two signals. For example, for the energy attenuation curve of the longitudinal wave and the corresponding vibration micro-variation signal, the weights are determined according to their signal-to-noise ratios (SNRs). The signal with a higher SNR is weighted at 0.6, and the signal with a lower SNR is weighted at 0.4. After weighted merging, the penetration-enhanced response signal corresponding to that wave component is obtained, thereby integrating different types of signal information and improving the quality and information content of the response signal.

[0039] Furthermore, based on the spatiotemporal gradient, different stages of the mineralization reaction are determined. For example, when the initial mineral concentration is low and the concentration growth rate is slow, it is the initial stage, with a corresponding time window of 0-24 hours; when the concentration growth rate accelerates, it is the intermediate stage, with a time window of 24-72 hours; and when the concentration growth tends to level off, it is the mature stage, with a time window of 72-120 hours. Then, based on these time windows, corresponding signal segments are extracted from the penetration-enhanced response signal. Each signal segment corresponds to a reaction stage, and the division and extraction process ensures that the response signal corresponds to the mineralization reaction stage, providing a basis for subsequent extraction of characteristic parameters for different stages. The signal segment within each time window is determined as the penetration response signal for that reaction stage. For example, the signal segment within the initial stage time window is the penetration response signal for the initial stage, and the same applies to the intermediate and mature stages. These penetration response signals contain characteristic information of microbial mineralization of river sand at different reaction stages.

[0040] Furthermore, by performing penetration simulation on the sample model, penetration response signals at different reaction stages were obtained, and these penetration response signals contain information reflecting the degree of mineralization.

[0041] S3. Extract the mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through the penetration response signals of different reaction stages.

[0042] In this embodiment of the invention, mineralization-specific characteristic parameters refer to parameters that can reflect the unique mineralization characteristics of microbial mineralized river sand and are used to distinguish different mineralization states.

[0043] In this embodiment of the invention, the extraction of mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through penetration response signals at different reaction stages includes: Calculate the ratio of longitudinal to transverse wave velocities of the penetration response signal at different reaction stages, and analyze the variance of the wave velocity ratio and the spatial position change of the penetration response signal. The biological cementation spatial distribution index of the microbial mineralized river sand standard sample model is determined based on the variance. Extract the dispersion curve features of the surface wave components of the penetration response signal at different reaction stages, and calculate the deviation between the dispersion curve features and the preset standard elastic half-space model. The spatial distribution index of biocementation, the wave velocity ratio, and the deviation are used as the mineralization-specific characteristic parameters.

[0044] In detail, longitudinal waves (PWs) are waves in which the direction of particle vibration is the same as the direction of wave propagation; transverse waves (SWs) are waves in which the direction of particle vibration is perpendicular to the direction of wave propagation. The wave velocity ratio is the ratio of the propagation velocity of the PW to that of the SW. It is calculated by identifying the propagation times of the PW and SW from the penetration response signals at different reaction stages, combining this with the known propagation distance, and then calculating the PW and SW velocities to obtain the wave velocity ratio. The wave velocity ratio reflects the density and cementation state of the river sand. Based on the wave velocity ratio data at different spatial locations along the scanning path, its variance is calculated. Variance is a statistical measure of the dispersion of the wave velocity ratio with spatial location; for example, the variance in the initial stage is 0.05, and the variance in the intermediate stage is 0.03. Variance reflects the uniformity of mineralization. The calculated variance is then substituted into a preset exponential calculation formula, using the formula: Calculate the spatial distribution index of bio-agglomeration, where Let be the spatial distribution index of biocementation, and be the variance. The average wave velocity ratio is the biocementation spatial distribution index, which is an indicator used to characterize the spatial uniformity of biocementation during microbial mineralization. The closer the index value is to 1, the more uniform the biocementation distribution.

[0045] Specifically, the surface wave component is extracted from the penetration response signal. The surface wave component refers to the wave component propagating along the model surface, and its dispersion curve is obtained through signal processing techniques. The dispersion curve characteristic refers to the curve feature of the surface wave propagation speed changing with frequency. For example, the propagation speed is 500 m / s at a surface wave frequency of 2 kHz, and 600 m / s at a frequency of 5 kHz. This dispersion curve is compared with the dispersion curve of a preset standard elastic half-space model, and the velocity difference between the two at the same frequency point is calculated. Then, the deviation is obtained through integration or averaging methods. The deviation can reflect the difference between mineralized river sand and an ideal elastic body, indirectly reflecting the degree of mineralization, thereby evaluating the elastic characteristics of the mineralized body. The preset standard elastic half-space model refers to a pre-defined model of an ideal elastic half-space used as a reference.

[0046] Furthermore, the spatial distribution index of biocementation, wave velocity ratio, and deviation are integrated as parameters that can comprehensively reflect the mineralization characteristics of the standard sample model of microbially mineralized river sand. These parameters describe the mineralization state from different perspectives, enabling a more intuitive assessment of the degree of mineralization and subsequent analysis of compressive strength.

[0047] S4. Construct a biocementation index for microbial mineralization based on the specific mineralization characteristic parameters, and analyze the compressive strength of the standard sample model of the microbial mineralized river sand based on the biocementation index.

[0048] In this embodiment of the invention, the biocementation index refers to an index that comprehensively reflects the degree of biocementation in microbially mineralized river sand.

[0049] In this embodiment of the invention, constructing the biocementing index of microbial mineralization based on the mineralization-specific characteristic parameters includes: Identify the mineralization reaction stages of microorganisms, and select dynamic weight coefficients corresponding to different mineralization reaction stages from a preset coefficient mapping table based on the dominant cementing parameters corresponding to the mineralization reaction stages. The wave velocity ratio and deviation in the mineralization-specific characteristic parameters are weighted according to the dynamic weighting coefficient. The weighted result is then calculated with the biocementation spatial distribution index in the mineralization-specific characteristic parameters to obtain the biocementation index of microbial mineralization.

[0050] In detail, the dominant cementing parameters refer to the parameters that play a major role in mineralization cementation at different reaction stages. The preset coefficient mapping table is a table that pre-determines the correspondence between reaction stages and dynamic weighting coefficients. By analyzing metabolic data and mineral concentration changes during the mineralization reaction process, the current reaction stage is determined, such as the initial stage, intermediate stage, or mature stage. The dominant cementing parameters differ at different reaction stages; the initial stage may be dominated by microbial activity, the intermediate stage by the mineralization rate, and the mature stage by the degree of cement solidification. Based on these dominant cementing parameters, the corresponding dynamic weighting coefficients are found in the preset coefficient mapping table. For example, the weighting coefficient for wave velocity ratio is 0.3 and the weighting coefficient for deviation is 0.2 in the initial stage; 0.4 for wave velocity ratio and 0.3 for deviation in the intermediate stage; and 0.3 for wave velocity ratio and 0.4 for deviation in the mature stage. The selection of dynamic weighting coefficients ensures that the characteristic parameters of different stages receive reasonable attention, solving the problem of inaccurate evaluation caused by fixed weights in existing technologies.

[0051] Specifically, the wave velocity ratio and deviation are multiplied by their corresponding dynamic weighting coefficients and then summed to obtain a weighted result. This weighted result is then calculated using the biocementation spatial distribution index from the mineralization-specific characteristic parameters to obtain the biocementation index of microbial mineralization. ,in The bio-cementation index, This is the dynamic weighting coefficient corresponding to the wave velocity ratio. This refers to the dynamic weighting coefficient corresponding to the deviation of the dispersion curve. For P-wave values, For transverse wave values, The deviation of the dispersion curve, The biocementation index is a spatial distribution index of biocementation. It integrates information from multiple characteristic parameters, providing a more comprehensive reflection of the degree of biocementation. Furthermore, a correlation model between the biocementing index and compressive strength was established using extensive experimental data. Compressive strength refers to the ability of a microbially mineralized river sand standard sample model to resist pressure failure. For example, for every 0.1 increase in the biocementing index, the compressive strength increases by an average of 5 MPa. Based on this model, the compressive strength was extrapolated from the calculated biocementing index; for instance, when the biocementing index was 0.834, the compressive strength was approximately 41.7 MPa, thus achieving a non-destructive assessment of compressive strength. The biocementing index and compressive strength are key data for constructing a destructive calibration curve, which will be used for subsequent evaluation of the test sample model.

[0052] S5. Construct the destructive calibration curve of the standard sample model of the microbial mineralized river sand based on the biocementation index and the compressive strength.

[0053] In this embodiment of the invention, the destructive calibration curve refers to a curve used to describe the relationship between the bio-cementation index and compressive strength, and the compressive strength can be inferred from the bio-cementation index through this curve.

[0054] In this embodiment of the invention, the destructive calibration curve for constructing the standard sample model of microbially mineralized river sand based on the biocementation index and the compressive strength includes: A sound wave simulation was performed on the pre-set target standard sample model, and the target bio-cementation index after the sound wave simulation was calculated. The destructive compressive strength of the pre-set target standard specimen model is simulated to obtain the true compressive strength; A pre-set Gaussian process regression model is trained using the target bio-cementation index and the actual compressive strength, and a probabilistic mapping relationship is determined based on the trained Gaussian process regression model. The destructive calibration curve of the microbial mineralized river sand standard sample model is constructed based on the probabilistic mapping relationship.

[0055] In detail, the target standard specimen model refers to multiple pre-defined standard specimen models with different mineralization degrees used to construct calibration curves. Several pre-defined target standard specimen models with different mineralization degrees are selected, such as five models with different biocementing indices. Acoustic simulation is performed on each model, including applying a swept-frequency acoustic excitation signal, acquiring the penetration response signal, and extracting mineralization-specific characteristic parameters. The target biocementing index corresponding to each model is then calculated, providing rich biocementing index data for constructing calibration curves. Destructive compressive strength simulation refers to simulating the process of conducting destructive tests on the specimen models to obtain their compressive strength. Mechanical simulation software is used to simulate destructive compressive strength tests on each pre-defined target standard specimen model, recording the pressure value experienced when the model is destroyed as the true compressive strength. For example, the true compressive strengths corresponding to the first five models are 20 MPa, 25 MPa, 30 MPa, 35 MPa, and 40 MPa, respectively. The true compressive strength data is an important reference for constructing calibration curves.

[0056] Specifically, the pre-defined Gaussian process regression model refers to a pre-set Gaussian process model used for regression analysis. The target biocementing index is used as input data, and the actual compressive strength is used as output data; both are input into the pre-defined Gaussian process regression model for training. By adjusting the model parameters, the model can accurately fit the relationship between the input and output. After training, the model can output the probability distribution of the corresponding compressive strength based on the input biocementing index. The probabilistic mapping relationship refers to the correspondence between the target biocementing index and the actual compressive strength, which has probabilistic distribution characteristics. For example, when the biocementing index is 0.75, the probability of the compressive strength being between 32-33 MPa is 90%, thus determining the probabilistic mapping relationship.

[0057] Furthermore, a destructive calibration curve was plotted using the target biocementation index as the x-axis and the actual compressive strength as the y-axis, combined with a probabilistic mapping relationship. This curve not only includes the trend of average compressive strength change but also reflects the probability distribution characteristics through the confidence intervals around the curve. For example, the curve shows that as the biocementation index increases, the average compressive strength increases linearly, and the confidence interval narrows as the biocementation index increases, indicating improved reliability of the assessment.

[0058] S6. Analyze the target compressive strength of the test sample model using the destructive calibration curve, and analyze the non-destructive strength of the microbial river sand corresponding to the test sample model based on the target compressive strength.

[0059] In this embodiment of the invention, the target compressive strength refers to the compressive strength of the test sample model obtained from the analysis of the destructive calibration curve.

[0060] In this embodiment of the invention, the step of analyzing the target compressive strength of the test sample model using the destructive calibration curve includes: Acquire the multimodal response data of the test sample model under acoustic excitation; The multimodal response data is subjected to spatiotemporal synchronization and fusion processing, and the fused target mineralization feature parameters are extracted. The dynamic biocementation index of the test sample model is calculated based on the target mineralization characteristic parameters. The target intensity distribution corresponding to the dynamic bio-cementation index was analyzed using the destructive calibration curve. The variance of the target strength distribution is used as the confidence index of the test sample model, and the target compressive strength of the test sample model is determined based on the confidence index and the expected value of the target strength distribution.

[0061] In detail, multimodal response data refers to the various types of response data generated by the test sample model under acoustic excitation. The same frequency-sweeping acoustic excitation signal as the standard sample model is applied to the test sample model. Through devices such as multi-element acoustic transducer arrays and distributed fiber optic sensors, the transmitted acoustic wave signal and the distributed fiber optic inductively coupled transducer signal generated by acoustic vibration are simultaneously acquired and integrated into multimodal response data. For example, the acquired transmitted acoustic wave signal contains components such as longitudinal waves, transverse waves, and surface waves, while the distributed fiber optic inductively coupled transducer signal reflects strain changes at different locations. Multimodal response data provides more comprehensive information. Based on the time and spatial markers of data acquisition, the transmitted acoustic wave signal and the distributed fiber optic inductively coupled transducer signal are aligned in time and space to ensure that different types of data at the same time and location correspond. Then, data fusion algorithms, such as principal component analysis, are used to fuse the synchronized multimodal data and extract key features. Target mineralization characteristic parameters, including wave velocity ratio, biocementation spatial distribution index, and deviation, are extracted from the fused data.

[0062] Specifically, the dynamic biocementation index is an index calculated based on the target mineralization characteristic parameters of the test sample model, reflecting its current degree of mineralization cementation. First, the reaction stage of the test sample model is determined, and the corresponding dynamic weighting coefficient is selected. Then, the wave velocity ratio and deviation in the target mineralization characteristic parameters are weighted and calculated with the biocementation spatial distribution index. The dynamic biocementation index can reflect the mineralization state of the test sample model in real time. The calculated dynamic biocementation index is input into the destructive calibration curve, and the probability distribution of the corresponding compressive strength is obtained according to the probabilistic mapping relationship. For example, when the dynamic biocementation index is 0.633, the target strength distribution shows that the probability of the compressive strength being between 28-30 MPa is 85%, thus comprehensively reflecting the possible range of compressive strength of the test sample model.

[0063] Furthermore, the confidence index is an indicator used to measure the reliability of the target intensity distribution; the smaller the variance, the higher the confidence level. The expected value refers to the average value of the target intensity distribution. The variance of the target intensity distribution is calculated as the confidence index, and the expected value is also calculated. This expected value is then used as the target compressive strength of the model to be tested. Combining this with the confidence index reveals the reliability of this strength value.

[0064] In this embodiment of the invention, the non-destructive strength of microbial river sand refers to the strength characteristics and state of microbial mineralized river sand without being destroyed.

[0065] In this embodiment of the invention, the non-destructive analysis of the microbial river sand strength corresponding to the test sample model based on the target compressive strength includes: Based on the target compressive strength and the confidence index, destructive location is performed in a preset multidimensional strength decision space to obtain the destruction location point; Based on the damage location points, a pre-generated microbial mineralization health status map is queried to obtain the intensity level of the model to be tested. Based on the convergence and fluctuation characteristics of the dynamic biocementation index, the completion and stability of the test sample model for the mineralization reaction are analyzed. The strength and non-destructive state of microbial river sand were analyzed based on the strength grade, the completion degree, and the stability.

[0066] In detail, the multidimensional strength decision space refers to a space constructed for decision analysis using dimensions such as target compressive strength and confidence index. Therefore, a two-dimensional strength decision space is constructed with target compressive strength as the vertical axis and confidence index as the horizontal axis, and different regions are divided, such as safe regions, warning regions, and dangerous regions. Based on the target compressive strength and confidence index of the model to be tested, the corresponding failure location points are determined in this space. The failure location points are the points in the multidimensional strength decision space corresponding to the target compressive strength and confidence index. The location process can intuitively reflect the strength state of the model to be tested.

[0067] Specifically, the microbial mineralization health status map refers to a pre-generated map reflecting the correspondence between damage locations and intensity levels. Different regions in this map correspond to different intensity levels, such as Level 1 (high intensity), Level 2 (medium intensity), and Level 3 (low intensity). Based on the location of the damage location in the multi-dimensional intensity decision space, this map is consulted to determine the intensity level of the model to be tested. For example, the damage location might correspond to a Level 2 intensity level.

[0068] Furthermore, the changes in the dynamic biocementing index at different time points were analyzed. If it gradually tends towards a stable value, it indicates good convergence characteristics and a high degree of completion; if its fluctuation amplitude is small, it indicates good fluctuation characteristics and a high degree of stability, which can assess the progress and stability of the mineralization reaction. Here, convergence characteristics refer to the tendency of the dynamic biocementing index to stabilize over time; fluctuation characteristics refer to the degree of fluctuation of the dynamic biocementing index over time; completion refers to the extent to which the mineralization reaction has proceeded; and stability refers to the degree of stability of the mineralization reaction.

[0069] Furthermore, considering the comprehensive evaluation results of strength grade, completion rate, and stability, if the strength grade is level two or above, the completion rate is high, and the stability is good, it indicates that the non-destructive strength state of the microbial river sand is good and can meet engineering requirements; otherwise, further processing is required. For example, the test sample model has a strength grade of level two, a high completion rate, and good stability, therefore its non-destructive strength state is good, achieving a comprehensive assessment of the non-destructive strength of microbial mineralized river sand and solving the problem of relying on destructive testing in existing technologies. like Figure 2 The diagram shown is a functional block diagram of a non-destructive assessment system for the strength of microbially mineralized river sand provided in an embodiment of the present invention.

[0070] The non-destructive strength assessment system 100 for microbially mineralized river sand described in this invention can be installed in an electronic device. Depending on the functions implemented, the non-destructive strength assessment system 100 may include a sample model construction module 101, a penetration simulation operation module 102, a mineralization-specific characteristic parameter extraction module 103, a compressive strength analysis module 104, a destructive calibration curve construction module 105, and a non-destructive analysis module 106 for microbial river sand strength. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0071] In this embodiment, the functions of each module / unit are as follows: The sample model construction module 101 is used to construct a standard sample model of microbial mineralized river sand and a sample model to be tested based on the pre-acquired mineralization properties, and to apply a preset sweep frequency acoustic excitation signal to the standard sample model of microbial mineralized river sand. The penetration simulation operation module 102 is used to perform penetration simulation operation on the microbial mineralized river sand standard sample model according to the frequency sweeping acoustic excitation signal, so as to obtain the penetration response signal of the microbial mineralized river sand at different reaction stages. The mineralization-specific characteristic parameter extraction module 103 is used to extract the mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through the penetration response signals of different reaction stages. The compressive strength analysis module 104 is used to construct the biocementation index of microbial mineralization based on the mineralization-specific characteristic parameters, and to analyze the compressive strength of the microbial mineralized river sand standard sample model based on the biocementation index. The destructive calibration curve construction module 105 is used to construct the destructive calibration curve of the microbial mineralized river sand standard sample model based on the biocementation index and the compressive strength. The microbial river sand strength non-destructive analysis module 106 is used to analyze the target compressive strength of the test sample model using the destructive calibration curve, and to analyze the non-destructive strength of the microbial river sand corresponding to the test sample model based on the target compressive strength.

[0072] In detail, the modules described in the non-destructive strength assessment system 100 for microbial mineralized river sand in this embodiment of the invention employ the same methods as described above. Figure 1 The method used is the same as the non-destructive assessment method for the strength of microbially mineralized river sand described in the article, and can produce the same technical effect, so it will not be repeated here.

[0073] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0074] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0076] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0077] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects. The scope of the invention is not limited to the foregoing description, and all variations within the meaning and scope of equivalents falling within the protection scope are intended to be included in the invention.

[0078] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0079] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim may also be implemented by a single unit or system through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A non-destructive method for assessing the strength of microbially mineralized river sand, characterized in that, The method includes: Based on the pre-acquired mineralization properties, a standard sample model of microbial mineralized river sand and a sample model to be tested are constructed, and a preset sweep frequency acoustic excitation signal is applied to the microbial mineralized river sand standard sample model. The microbial mineralized river sand standard sample model was subjected to a penetration simulation operation based on the frequency sweeping acoustic excitation signal to obtain the penetration response signals of the microbial mineralized river sand at different reaction stages. Mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model were extracted by using the penetration response signals at different reaction stages. The biocementation index of microbial mineralization was constructed based on the specific mineralization characteristic parameters, and the compressive strength of the standard sample model of microbial mineralized river sand was analyzed based on the biocementation index. The destructive calibration curve of the standard sample model of the microbial mineralized river sand was constructed based on the biocementation index and the compressive strength. The target compressive strength of the test sample model is analyzed using the destructive calibration curve, and the non-destructive strength of the microbial river sand corresponding to the test sample model is analyzed based on the target compressive strength.

2. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 1, characterized in that, The construction of a standard sample model for microbially mineralized river sand based on pre-acquired mineralization properties includes: The spatiotemporal gradient of mineralization reaction concentration was analyzed based on the microbial metabolic characteristics in the mineralization properties. A layered injection strategy for generating a standard sample model of microbially mineralized river sand based on the spatiotemporal variation gradient; According to the layered infusion strategy, the microbial inoculum and river sand are infused and solidified in a spatial sequence within a preset simulated space. Based on the spatiotemporal variation gradient, the microbial liquid and river sand after solidification simulation were adjusted in reverse, and a standard sample model of microbial mineralized river sand was generated according to the parameter attributes corresponding to the adjusted microbial liquid and river sand.

3. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 1, characterized in that, The application of a preset sweep frequency acoustic excitation signal to the microbial mineralized river sand standard sample model includes: The focal region and scanning path of acoustic excitation are dynamically divided based on the concentration gradient distribution of the microbial mineralized river sand standard sample model during the solidification process. The beamforming parameters of the preset multi-element acoustic transducer array are adaptively adjusted according to the scanning path. A swept-frequency acoustic excitation signal is applied in the focal region based on the beamforming parameters.

4. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 3, characterized in that, The penetration simulation operation performed on the standard sample model of microbially mineralized river sand based on the frequency-sweeping acoustic excitation signal yields penetration response signals for different reaction stages of the microbially mineralized river sand, including: When the frequency sweeping acoustic excitation signal is applied, the vibration micro-change signal corresponding to the surface target point of the microbial mineralized river sand standard sample model is collected simultaneously. The acoustic signal after the penetration simulation operation is divided into different types of wave components, and the signal energy attenuation curves of different types of wave components at different spatial locations on the scanning path are extracted. The signal energy attenuation curve and the vibration micro-variation signal are time-domain aligned and fused to generate a penetration-enhanced response signal. Extract signal segments corresponding to different reaction stages of microbial mineralization of river sand from the penetration-enhanced response signal; The response signal corresponding to the signal segment is used as the penetration response signal for different reaction stages.

5. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 1, characterized in that, The extraction of mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through penetration response signals at different reaction stages includes: Calculate the ratio of longitudinal to transverse wave velocities of the penetration response signal at different reaction stages, and analyze the variance of the wave velocity ratio and the spatial position change of the penetration response signal. The biological cementation spatial distribution index of the microbial mineralized river sand standard sample model is determined based on the variance. Extract the dispersion curve features of the surface wave components of the penetration response signal at different reaction stages, and calculate the deviation between the dispersion curve features and the preset standard elastic half-space model. The spatial distribution index of biocementation, the wave velocity ratio, and the deviation are used as the mineralization-specific characteristic parameters.

6. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 1, characterized in that, The construction of the biocementing index of microbial mineralization based on the mineralization-specific characteristic parameters includes: Identify the mineralization reaction stages of microorganisms, and select dynamic weight coefficients corresponding to different mineralization reaction stages from a preset coefficient mapping table based on the dominant cementing parameters corresponding to the mineralization reaction stages. The wave velocity ratio and deviation in the mineralization-specific characteristic parameters are weighted according to the dynamic weighting coefficient. The weighted result is then calculated with the biocementation spatial distribution index in the mineralization-specific characteristic parameters to obtain the biocementation index of microbial mineralization.

7. The non-destructive method for assessing the strength of microbially mineralized river sand as described in claim 1, characterized in that, The destructive calibration curve for constructing the standard sample model of the microbially mineralized river sand based on the biocementation index and the compressive strength includes: A sound wave simulation was performed on the pre-set target standard sample model, and the target bio-cementation index after the sound wave simulation was calculated. The destructive compressive strength of the pre-set target standard specimen model is simulated to obtain the true compressive strength; A pre-set Gaussian process regression model is trained using the target bio-cementation index and the actual compressive strength, and a probabilistic mapping relationship is determined based on the trained Gaussian process regression model. The destructive calibration curve of the microbial mineralized river sand standard sample model is constructed based on the probabilistic mapping relationship.

8. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 1, characterized in that, The analysis of the target compressive strength of the test sample model using the destructive calibration curve includes: Acquire the multimodal response data of the test sample model under acoustic excitation; The multimodal response data is subjected to spatiotemporal synchronization and fusion processing, and the fused target mineralization feature parameters are extracted. The dynamic biocementation index of the test sample model is calculated based on the target mineralization characteristic parameters. The target intensity distribution corresponding to the dynamic bio-cementation index was analyzed using the destructive calibration curve. The variance of the target strength distribution is used as the confidence index of the test sample model, and the target compressive strength of the test sample model is determined based on the confidence index and the expected value of the target strength distribution.

9. The non-destructive assessment method for the strength of microbially mineralized river sand as described in claim 8, characterized in that, The analysis of the non-destructive strength of the microbial river sand corresponding to the test sample model based on the target compressive strength includes: Based on the target compressive strength and the confidence index, destructive location is performed in a preset multidimensional strength decision space to obtain the destruction location point; Based on the damage location points, a pre-generated microbial mineralization health status map is queried to obtain the intensity level of the model to be tested. Based on the convergence and fluctuation characteristics of the dynamic biocementation index, the completion and stability of the test sample model for the mineralization reaction are analyzed. The strength and non-destructive state of microbial river sand were analyzed based on the strength grade, the completion degree, and the stability.

10. A non-destructive assessment system for the strength of microbially mineralized river sand, characterized in that, The system for performing the non-destructive assessment method for the strength of microbially mineralized river sand as described in any one of claims 1-9 comprises: The sample model construction module is used to construct a standard sample model of microbial mineralized river sand and a sample model to be tested based on the pre-acquired mineralization properties, and to apply a preset sweep frequency acoustic excitation signal to the standard sample model of microbial mineralized river sand. The penetration simulation operation module is used to perform penetration simulation operation on the microbial mineralized river sand standard sample model according to the frequency sweeping acoustic excitation signal, and obtain the penetration response signals of the microbial mineralized river sand at different reaction stages. The mineralization-specific characteristic parameter extraction module is used to extract the mineralization-specific characteristic parameters corresponding to the microbial mineralized river sand standard sample model through the penetration response signals of different reaction stages. The compressive strength analysis module is used to construct the biocementation index of microbial mineralization based on the mineralization-specific characteristic parameters, and to analyze the compressive strength of the microbial mineralized river sand standard sample model based on the biocementation index. The destructive calibration curve construction module is used to construct the destructive calibration curve of the microbial mineralized river sand standard sample model based on the biocementation index and the compressive strength. The microbial river sand strength non-destructive analysis module is used to analyze the target compressive strength of the test sample model using the destructive calibration curve, and to analyze the non-destructive strength of the microbial river sand corresponding to the test sample model based on the target compressive strength.

Images