Soft soil reinforcement material performance analysis method and system

Abstract

The application discloses a soft soil reinforcing material performance analysis method and system, and particularly relates to the technical field of material testing, and aims to solve the problem that the existing discrete testing method cannot accurately determine the material performance stabilization critical time point; through continuous monitoring of a sensor to obtain performance parameter time series data, a mode stability quantity representing evolution consistency and a complexity balance quantity of multi-time scale behavior regularity are calculated, a candidate critical point is identified when the two quantities are continuously higher and lower than the set threshold value respectively, and finally, the effective performance stabilization critical time point is output through comparison and verification with a standard performance development envelope band, so that continuous monitoring and accurate analysis of the whole process of material performance development are realized, and a reliable technical means is provided for performance evaluation of the soft soil reinforcing material.

Description

Technical Field

[0001] This invention relates to the field of materials testing technology, and more specifically, to a method and system for analyzing the performance of soft soil reinforcement materials. Background Technology

[0002] The performance evaluation of soft soil reinforcement materials typically relies on standardized laboratory testing methods. Existing techniques involve destructive mechanical testing of specimens cured to a specific age to obtain performance data at discrete time points, thereby characterizing the strength development of the material. These methods are based on the assumption that material properties monotonically increase and gradually stabilize with age, extrapolating long-term performance from a limited number of data points.

[0003] However, since the performance development of soft soil reinforcement materials is a continuous time-varying process, existing discretization testing methods cannot capture the complete dynamic characteristics of performance evolution, making it impossible to accurately determine the critical time point for material performance stabilization. The inherent insufficient time resolution of this testing method results in significant uncertainties in the performance development patterns obtained based on discrete data, making it difficult to reliably predict the long-term behavior of materials. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for analyzing the performance of soft soil reinforcement materials to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for analyzing the performance of soft soil reinforcement materials includes the following steps:

[0007] S1. Continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process using sensors to obtain a continuous data sequence of performance parameters changing over time;

[0008] S2. Based on continuous data sequences, calculate the pattern stability quantity used to characterize the consistency of its evolutionary characteristics and the complexity balance quantity used to characterize the regularity of its behavior across multiple time scales.

[0009] S3. When the mode stability value is consistently higher than the first threshold and the complexity balance value is consistently lower than the second threshold, the corresponding time point will be identified as a candidate critical point for performance stabilization.

[0010] S4. Compare and analyze the continuous data sequence with the pre-set standard performance development envelope of the corresponding material class;

[0011] S5. Based on the comparative analysis results, when the continuous data sequence is within the preset envelope of the standard performance development of the corresponding material, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point.

[0012] S6 outputs the effective performance stabilization critical time point.

[0013] Furthermore, by continuously monitoring the performance parameters of soft soil reinforcement material specimens during the curing process using sensors, a continuous data sequence of performance parameters changing over time is obtained, including:

[0014] The ultrasonic wave velocity changes of the soft soil reinforcement material specimens during the curing process were continuously monitored by a piezoelectric ceramic sensor embedded in the specimens.

[0015] The resistivity changes of the soft soil reinforcement material specimens during the curing process were continuously monitored by a four-electrode resistance probe embedded in the specimens.

[0016] To obtain continuous data sequences of ultrasonic wave velocity and resistivity as a function of time.

[0017] Furthermore, based on continuous data sequences, the pattern stability quantity used to characterize the consistency of its evolutionary characteristics and the complexity balance quantity used to characterize the regularity of its behavior across multiple time scales are calculated, including:

[0018] Based on a continuous data sequence of ultrasonic wave velocity changing with time, the normalized path distance between the current data sequence and the reference stable mode sequence is calculated using a dynamic time warping algorithm as a mode stability quantity.

[0019] Based on a continuous data sequence of resistivity changing over time, the variance of entropy values ​​at different time scales is calculated using a multi-scale entropy analysis method as a complexity balancing measure.

[0020] Furthermore, based on a continuous data sequence of ultrasonic wave velocity varying over time, the normalized path distance between the current data sequence and the reference stable mode sequence is calculated using a dynamic time warping algorithm as a mode stability quantity, including:

[0021] Obtain a baseline stability mode sequence established in advance using a large number of stable-period material samples;

[0022] The minimum cumulative distance between the current ultrasonic wave velocity data sequence and the reference stable mode sequence is calculated using a dynamic time warping algorithm, and the minimum cumulative distance is quantized into a warped path distance.

[0023] The smaller the regular path distance value, the higher the consistency between the current data sequence and the stable pattern.

[0024] Furthermore, based on a continuous data sequence of resistivity changing over time, the variance of entropy values ​​at different time scales is calculated using a multi-scale entropy analysis method as a complexity balancing measure, including:

[0025] Coarsening the resistivity data sequence yields subsequences at multiple time scales.

[0026] Calculate the sample entropy value of the subsequence at each scale;

[0027] The dispersion of sample entropy values ​​across all scales is statistically analyzed, and the variance of sample entropy values ​​is used as a measure of complexity balance.

[0028] The smaller the variance of the sample entropy, the more consistent the material's behavior is across different time scales.

[0029] Furthermore, when the pattern stability value consistently exceeds the first threshold and the complexity balance value consistently falls below the second threshold, the corresponding time point is identified as a candidate critical point for performance stabilization, including:

[0030] Based on the mode stability measure, its first threshold is set as the statistical significance level of the distance value obtained by comparing the benchmark stable mode sequence with itself using the dynamic time warping algorithm.

[0031] Based on the complexity balance, its second threshold is set as the relative stable state reference value of the variance of the entropy value obtained by calculating the reference material sequence under the fully stable state using the multi-scale entropy analysis method.

[0032] When the duration of the pattern stability value exceeding the first threshold reaches the preset monitoring period and the duration of the complexity balance value falling below the second threshold reaches the preset monitoring period, the first time point that simultaneously meets the duration requirement will be recorded as a candidate critical point for performance stabilization.

[0033] Furthermore, the continuous data sequence is compared and analyzed with the pre-defined development envelope of the standard properties of the corresponding material class, including:

[0034] Obtain the pre-defined standard performance development envelope of the corresponding material type, including the standard development envelope of ultrasonic wave velocity and the standard development envelope of resistivity;

[0035] A point-by-point comparative analysis was conducted between the continuous data sequence of ultrasonic wave velocity changing over time and the standard development envelope of ultrasonic wave velocity.

[0036] Simultaneously, a point-by-point comparative analysis is performed between the continuous data sequence of resistivity changes over time and the resistivity standard development envelope.

[0037] Record the positional relationship between each data point in a continuous data sequence and the corresponding standard developmental envelope.

[0038] Furthermore, based on the comparative analysis results, when the continuous data sequence lies entirely within the pre-defined envelope of the standard performance development of the corresponding material type, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point, including:

[0039] Based on the positional relationship between each data point in the recorded continuous data sequence and the corresponding standard development envelope, it is determined whether all data points in the continuous data sequence of ultrasonic wave velocity changing with time are within the range of the standard development envelope of ultrasonic wave velocity.

[0040] Simultaneously determine whether all data points in a continuous data sequence of resistivity changes over time are within the resistivity standard development envelope.

[0041] When both the continuous data sequence of ultrasonic wave velocity and the continuous data sequence of resistivity are within the corresponding standard development envelope range throughout the entire process, the candidate critical point is confirmed as an effective performance stabilization critical time point.

[0042] Furthermore, output the effective performance stabilization critical time points, including:

[0043] The confirmed effective performance stabilization critical time points are associated and stored with their corresponding ultrasonic wave velocity data sequence characteristics and resistivity data sequence characteristics; a report file containing the effective performance stabilization critical time points and their corresponding monitoring data analysis results is generated.

[0044] The system displays the position markers of effective performance stabilization critical time points in continuous data sequences of ultrasonic wave velocity and resistivity over time through a visual interface. It also displays the mode stability value and complexity equilibrium value corresponding to the time point and their comparison with their respective thresholds. Furthermore, it displays the comparative analysis results of the continuous data sequence with the preset standard performance development envelope of the corresponding material type.

[0045] On the other hand, the present invention provides a performance analysis system for soft soil reinforcement materials, comprising the following modules:

[0046] The continuous monitoring module is used to continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process through sensors, and obtain a continuous data sequence of performance parameters changing over time.

[0047] The feature calculation module is used to calculate, based on continuous data sequences, the pattern stability quantity used to characterize the consistency of their evolutionary features and the complexity balance quantity used to characterize the regularity of their behavior across multiple time scales.

[0048] The critical identification module is used to identify the corresponding time point as a candidate critical point for performance stabilization when the pattern stability value is continuously higher than the first threshold and the complexity balance value is continuously lower than the second threshold.

[0049] The comparative analysis module is used to compare and analyze continuous data sequences with preset standard performance development envelopes for corresponding material types.

[0050] The confirmation and judgment module is used to confirm the corresponding candidate critical point as an effective performance stabilization critical time point when the continuous data sequence is within the preset standard performance development envelope of the corresponding material type throughout the entire process, based on the comparative analysis results.

[0051] The results output module is used to output the effective performance stabilization critical time point.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. By acquiring complete time-series data on the performance development of soft soil reinforcement materials through continuous monitoring, this method overcomes the time resolution limitations of traditional discretized testing methods. Employing multi-dimensional feature quantification analysis and co-calculating mode stability and complexity balance parameters, it achieves a dynamic characterization of the material performance evolution process, accurately capturing the critical characteristics of the material's transition from an unsteady state to a steady state. This analysis method based on continuous monitoring throughout the entire process eliminates the uncertainties introduced by traditional methods relying on extrapolation from limited data points, providing a reliable data foundation for determining the stabilization of material performance.

[0054] 2. A multi-level verification mechanism was established. Through dual verification of mode stability judgment and standard development envelope comparison, the accuracy of critical time point identification was ensured. This not only reflects the macroscopic laws of material performance development, but also reveals microscopic behavioral characteristics through multi-timescale analysis, realizing a comprehensive performance evaluation from phenomenon to essence. Attached Figure Description

[0055] Figure 1 This is a flowchart of a method for analyzing the performance of soft soil reinforcement materials according to the present invention;

[0056] Figure 2 This is a schematic diagram of the performance analysis system for soft soil reinforcement materials according to the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1: Figure 1 This invention provides a method for analyzing the performance of soft soil reinforcement materials, which includes the following steps:

[0059] S1. Continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process using sensors to obtain a continuous data sequence of performance parameters changing over time;

[0060] S2. Based on continuous data sequences, calculate the pattern stability quantity used to characterize the consistency of its evolutionary characteristics and the complexity balance quantity used to characterize the regularity of its behavior across multiple time scales.

[0061] S3. When the mode stability value is consistently higher than the first threshold and the complexity balance value is consistently lower than the second threshold, the corresponding time point will be identified as a candidate critical point for performance stabilization.

[0062] S4. Compare and analyze the continuous data sequence with the pre-set standard performance development envelope of the corresponding material class;

[0063] S5. Based on the comparative analysis results, when the continuous data sequence is within the preset envelope of the standard performance development of the corresponding material, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point.

[0064] S6 outputs the effective performance stabilization critical time point.

[0065] S1. Continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process using sensors to obtain a continuous data sequence of performance parameters changing over time. Specifically, this is implemented as follows:

[0066] To achieve continuous monitoring of the performance development of soft soil reinforcement materials, this step is implemented as follows: First, a standard cylindrical soft soil reinforcement material specimen is prepared, with dimensions of 50 mm in diameter and 100 mm in height. During specimen preparation, two piezoelectric ceramic sensors are embedded at the center of both ends of the specimen. The sensors have a diameter of 10 mm, a thickness of 2 mm, and an operating frequency of 1 MHz. Simultaneously, a four-electrode resistance probe is embedded in the middle of the specimen. The probe electrodes are made of stainless steel, with an electrode spacing of 10 mm and an electrode diameter of 1 mm. During sensor embedding, it is ensured that there is full contact with the surrounding material without gaps. The sensor wires are led out from the side of the specimen and connected to the data acquisition system.

[0067] The data acquisition system includes an ultrasonic transmitter and receiver and a resistance meter. The ultrasonic transmitter and receiver generates pulse voltage signals to drive the transmitting sensor and receives signals from the receiving sensor; the resistance meter uses a four-terminal method to measure resistance values, eliminating the influence of wire resistance. During monitoring, the specimens are kept under standard curing conditions, with the temperature controlled at 20±2 degrees Celsius and the relative humidity maintained above 95%.

[0068] During monitoring, the ultrasonic wave velocity is measured using the pulse transmission method, for example, data is collected every hour. The specific process is as follows: the ultrasonic transmitter / receiver applies a pulse voltage signal to the transmitting sensor, which converts the electrical signal into mechanical vibration, generating ultrasonic waves that propagate within the specimen; the receiving sensor converts the received ultrasonic signal back into an electrical signal, which is amplified and recorded by the data acquisition card; by measuring the propagation time of the ultrasonic wave from transmission to reception, and combining this with the calibrated distance between the two sensors, the ultrasonic wave velocity is calculated. The formula for calculating the ultrasonic wave velocity is: Wave velocity = Propagation distance ÷ Propagation time, where the propagation distance is the actual distance between the two sensors, precisely measured and determined to be 100 mm.

[0069] Resistivity measurement is performed simultaneously with ultrasonic wave velocity measurement, for example, data is collected every hour. A constant alternating current with a frequency of 1000 Hz and a current intensity of 1 mA is applied through the two outer electrodes of the four-electrode resistance probe; the voltage drop is measured through the two inner electrodes, the resistance value is calculated according to Ohm's law, and the resistivity is then calculated based on the probe's geometric parameters. The formula for calculating resistivity is: Resistivity = Resistance value × Cross-sectional area ÷ Electrode spacing, where the cross-sectional area is the cross-sectional area of ​​the specimen, and the electrode spacing is the distance between the two inner electrodes.

[0070] All monitoring data are automatically stored in the computer, forming time-series data. Ultrasonic velocity data is stored as pairs containing timestamps and velocity values, and resistivity data is stored as pairs containing timestamps and resistivity values. Data acquisition continues until the specimen performance stabilizes, typically requiring more than 28 days. In this way, complete continuous data sequences of ultrasonic velocity and resistivity over time are obtained, providing a data foundation for subsequent analysis.

[0071] To ensure data quality, several measures were implemented. After sensor installation, an impedance analyzer was used to check their operational status, ensuring the sensors were intact and well-coupled with the materials. The data acquisition system was calibrated weekly, and measurement accuracy was verified using standard specimens. For example, an acrylic glass standard specimen with known acoustic properties was used to calibrate the ultrasonic measurement system, and a standard resistance box was used to calibrate the resistance measurement system. During monitoring, abnormal data values ​​were checked in real time. When an anomaly was detected, the sensor connections and instrument status were immediately checked. For instance, if three consecutive data points deviated from the normal range by more than 10%, the system automatically issued an alarm. All monitoring data was automatically backed up to a cloud storage system to prevent data loss.

[0072] In the specific implementation process, the ultrasonic propagation time is measured using a threshold detection method, setting the arrival time as the moment when the voltage amplitude reaches 10% of its maximum value. For resistance measurement, an AC excitation signal is used to avoid electrode polarization effects, and the measurement frequency is selected within the range of 100Hz to 10kHz, preferably at 1000Hz. The data acquisition time interval can be adjusted as needed; for example, a shorter interval, such as 30 minutes, can be used in the initial hardening stage, while a longer interval, such as 2 hours, can be used later, but consistency in the acquisition interval must be maintained.

[0073] The monitoring data is stored in standard CSV format. Each row contains three data items: timestamp, ultrasonic velocity value, and resistivity value. The timestamp is accurate to the second. To eliminate the influence of ambient temperature changes on the measurement results, the ambient temperature around the specimen is recorded simultaneously, and the measurement results are compensated and corrected according to the material temperature coefficient. For example, the temperature compensation coefficient for ultrasonic velocity is taken as -0.5 m / s / ℃, and the temperature compensation coefficient for resistivity is taken as 2% / ℃.

[0074] S2. Based on continuous data sequences, calculate the pattern stability quantity used to characterize the consistency of its evolutionary characteristics and the complexity balance quantity used to characterize the regularity of its behavior across multiple time scales, respectively. The specific implementation is as follows:

[0075] Based on continuous data sequences of ultrasonic wave velocity and resistivity changing over time, this step is implemented as follows: First, the raw data obtained from the monitoring is preprocessed, including outlier removal and data smoothing. Outlier removal uses the 3σ criterion, which calculates the mean and standard deviation of the data sequence, and removes data points that exceed the mean ± 3 times the standard deviation. Data smoothing uses a moving average method, for example, smoothing with a window size of 5 data points to eliminate the influence of random fluctuations and ensure that the data quality meets the requirements of subsequent analysis.

[0076] The baseline stability mode sequence was established by collecting a large number of ultrasonic wave velocity data sequences from soft soil reinforcement material specimens that had reached a stable state. These data sequences were normalized and time-aligned, and the average value at each time point was used to construct the sequence. Specifically, at least 30 sets of stable-period specimen data with different formulations were selected. Each set of data was normalized to the same length along the time dimension, for example, by using linear interpolation to unify it to 100 time points. Then, the average value of all data at each time point was calculated to form the baseline stability mode sequence. This sequence represents the stability mode of material performance development under ideal conditions and serves as a reference benchmark for subsequent calculations of mode stability quantities.

[0077] When calculating the warped path distance between the current ultrasonic velocity data sequence and the reference stable mode sequence using the dynamic time warping algorithm, the two sequences are first time-warped to find the warped path that minimizes the cumulative distance between them. Specifically, a distance matrix is ​​constructed, where each element represents the Euclidean distance between the i-th point in the current sequence and the j-th point in the reference sequence. i is the index of the data point in the current ultrasonic velocity data sequence, and j is the index of the data point in the reference stable mode sequence.

[0078] The normalized path distance (NPD) is calculated by using dynamic programming to find a path from the top left to the bottom right of the matrix that minimizes the sum of distances between points along that path. The NPD takes into account potential nonlinear temporal distortions between sequences and effectively assesses the similarity between the current sequence and a stable pattern. A smaller NPD value indicates a higher degree of consistency between the current data sequence and the stable pattern.

[0079] For resistivity data sequences, a multi-scale entropy analysis method is used to calculate the complexity balance. First, the continuous resistivity data sequence over time is coarsened. By selecting different scale factors, such as scale factor τ taking integer values ​​from 1 to 10, the original sequence is divided into multiple coarse-grained subsequences. The coarsening process involves averaging τ consecutive data points from the original sequence to form a new coarse-grained sequence. Each scale factor corresponds to one coarse-grained sequence, thus obtaining subsequences at multiple time scales.

[0080] Then, the sample entropy value of the subsequence at each scale is calculated. When calculating the sample entropy, the pattern dimension *m* and the similarity tolerance *r* need to be set. Typically, *m* is set to 2, and *r* is taken as 0.1 to 0.25 times the standard deviation of the original sequence, for example, 0.2 times. For each coarse-grained subsequence, its sample entropy value is calculated; this value reflects the complexity of the sequence. The larger the sample entropy value, the more complex the sequence and the worse its regularity. The specific calculation process includes: first, defining a set of *m*-dimensional vectors in the sequence; then, counting the proportion of these vectors whose distance is less than *r*; then, similarly calculating the proportion of *m+1*-dimensional vectors; finally, taking the natural logarithm of the ratio of the two as the sample entropy value.

[0081] Finally, the dispersion of sample entropy values ​​across all scales is statistically analyzed, and the variance of these sample entropy values ​​is calculated as a measure of complexity equilibrium. The variance is calculated using the standard deviation formula, which is the average of the sum of squares of the differences between each sample entropy value and the mean of the sample entropy values ​​across all scales. This variance reflects the consistency of the material's behavior across different time scales; a smaller variance indicates that the material's performance development is more consistent across different time scales, suggesting that the material's performance tends to be stable.

[0082] In practical implementation, the parameters of the dynamic time warping algorithm are set as follows: the warping window width is limited to 10% of the sequence length to avoid over-warping; the local path constraint adopts a symmetric Sakoe-Chiba band constraint. The scale factor in multi-scale entropy analysis is typically selected from 1 to 10, but can be adjusted according to the actual data length. For example, when there are few data points, the maximum scale factor can be appropriately reduced. The similarity tolerance r in sample entropy calculation needs to be adjusted according to the data characteristics, and is generally determined through trial and error to ensure that the sample entropy values ​​have good discriminative power.

[0083] All calculations were implemented through programming, with the computational program written in Python and standard scientific computing libraries such as NumPy used for numerical computation. To ensure the reliability of the calculation results, key computational steps were cross-validated, for example, by repeating the calculations with different parameter settings and observing the stability of the results. Intermediate and final results generated during the calculation process were saved as structured data files for easy subsequent analysis and verification. Through the above specific implementation method, the pattern stability quantity characterizing the consistency of evolutionary features and the complexity balance quantity characterizing the regularity of behavior across multiple time scales can be accurately calculated, providing a quantitative basis for subsequent identification of performance stabilization critical points.

[0084] S3. When the mode stability value is consistently higher than the first threshold and the complexity balance value is consistently lower than the second threshold, the corresponding time point is identified as a candidate critical point for performance stabilization. Specifically, this is implemented as follows:

[0085] Based on the calculation results of the mode stability and complexity balance, this step specifically implements the identification process of candidate critical points for performance stabilization. The first threshold is determined using statistical analysis methods, establishing a reference standard by calculating the distribution characteristics of the regularized path distance between the benchmark stable mode sequence and itself. Specifically, the benchmark stable mode sequence and itself are subjected to dynamic time warping algorithm calculation to obtain a set of self-similar distance values, and then the statistical significance level is determined based on the distribution characteristics of these distance values. For example, the average of these self-similar distance values ​​plus twice the standard deviation is taken as the first threshold. This threshold indicates that when the similarity between the current sequence and the benchmark sequence reaches a statistical significance level comparable to the self-similarity of the benchmark sequence, the material performance is considered to be stable.

[0086] The second threshold is set based on the entropy variance analysis of a reference material sequence under fully steady-state conditions. Several soft soil reinforcement material specimens known to have reached a fully steady state are selected, and their resistivity data sequences are collected. The entropy variance of each specimen is calculated using a multi-scale entropy analysis method, and the distribution characteristics of these variance values ​​are then statistically analyzed. For example, the average entropy variance of these fully steady-state specimens is taken as the second threshold, which represents the level of multi-scale behavioral regularity exhibited by the material under fully steady-state conditions. When the current material's complexity equilibrium is below this threshold, it indicates that its behavioral regularity has reached the steady-state reference level.

[0087] Determining the preset monitoring cycle requires considering the time-varying characteristics of material performance development, and is typically based on statistical analysis of extensive experimental data. For example, by analyzing the time distribution characteristics required for a material to reach a stable state from the start of curing in historical data, a certain percentile of the time distribution can be taken as the preset monitoring cycle. The preset monitoring cycle must ensure that it can capture a continuous and stable trend, while avoiding misjudgments due to excessively short timeframes. In practical applications, the preset monitoring cycle can be adjusted according to the specific material type and environmental conditions. For example, a shorter monitoring cycle can be used for materials that harden quickly, while a longer monitoring cycle can be used for materials that harden slowly.

[0088] The system begins recording the duration when the pattern stability value consistently exceeds a first threshold and the complexity equilibrium value consistently falls below a second threshold. The duration is calculated from the first time point when both conditions are simultaneously met, and resets when either condition is no longer met. For example, when the pattern stability value first simultaneously exceeds the first threshold and the complexity equilibrium value falls below the second threshold, a timer is started, and data at subsequent time points is continuously monitored. If both conditions are met simultaneously at all time points within a preset monitoring period, the starting point of that time period is recorded as a candidate critical point.

[0089] To ensure the reliability of the identification results, a verification mechanism was also implemented. For example, for identified candidate critical points, the data trend over a period of time before and after them needs to be checked to confirm the continuity of the stability trend. Simultaneously, a maximum allowable fluctuation range is set; for example, it is permissible for the pattern stability value at individual time points to be slightly lower than the first threshold or the complexity equilibrium value to be slightly higher than the second threshold. However, as long as the fluctuation is within the allowable range and the overall trend meets the requirements, it can still be considered as continuously meeting the conditions. The fluctuation range is set based on statistical analysis of historical data; for example, the average of the normal fluctuation range plus twice the standard deviation is taken as the maximum allowable fluctuation range.

[0090] All threshold parameters and judgment conditions are managed through configuration files, facilitating adjustments based on actual conditions. The system periodically and automatically saves intermediate and final results of the recognition process, including pattern stability and complexity balance values ​​at each time point, comparison results with thresholds, duration counts, etc., for subsequent traceability and verification.

[0091] S4. Compare and analyze the continuous data sequence with the pre-set development envelope of the corresponding material standard performance. The specific implementation is as follows:

[0092] Based on continuous data sequences of ultrasonic wave velocity and resistivity over time, and the identified candidate critical points, this step specifically implements a comparative analysis process. The establishment of the pre-defined standard performance development envelope for corresponding materials is achieved by collecting long-term monitoring data from a large number of similar soft soil reinforcement material specimens under standard curing conditions, and then statistically analyzing this data to construct a reference standard range. Specifically, at least 50 sets of data from similar material specimens with different formulations are selected, covering the entire development process from the initial state to the fully stable state. This data is normalized along the time dimension, and then the statistical characteristic values ​​of all data at each time point are calculated, such as the mean and standard deviation, to determine the boundary range of the envelope.

[0093] The construction of the ultrasonic wave velocity standard development envelope is determined by statistically analyzing the distribution characteristics of ultrasonic wave velocity values ​​at various time points to define the envelope boundaries. For example, the upper and lower boundaries of the envelope are taken as the average of all data values ​​at each time point plus or minus two standard deviations. This method covers more than 95% of the normal development data. The construction of the resistivity standard development envelope uses the same method, statistically analyzing the distribution characteristics of resistivity values ​​at various time points to define the envelope boundaries. Both envelopes use the same time base and normalization method to ensure consistency across the time dimension. The envelope data is stored in matrix form, containing time point sequences, upper boundary value sequences, and lower boundary value sequences, facilitating rapid retrieval and retrieval during subsequent comparative analysis.

[0094] When performing a point-by-point comparative analysis of a continuous data sequence of ultrasonic wave velocity varying over time with the standard development envelope of ultrasonic wave velocity, the time axis of the current data sequence is first aligned with the time axis of the envelope. Time alignment employs a linear interpolation method, interpolating the envelope boundary values ​​to each time point of the current data sequence to ensure that each monitoring time point has a corresponding envelope boundary value. Then, for each time point, the relationship between the current ultrasonic wave velocity value and the upper and lower boundary values ​​of the envelope at the corresponding time point is compared to determine whether the data point is within the envelope range. The relative distance between the data point and the nearest boundary is calculated; this distance value is used to quantify the degree of deviation.

[0095] The continuous data sequence of resistivity changing over time is compared point-by-point with the resistivity standard development envelope using the same processing flow. First, the time axis is aligned, and the boundary values ​​of the resistivity standard development envelope are interpolated to each time point of the current data sequence. Then, the relationship between the current resistivity value and the envelope boundary value is compared point-by-point. The two comparative analysis processes are kept synchronized to ensure consistent judgment of the two parameters at the same time point. This synchronized comparative analysis helps to comprehensively evaluate the coordination of material performance development.

[0096] When recording the positional relationship between each data point and its corresponding standard developmental envelope in a continuous data sequence, a categorized recording method is used. For each time point, the positional status of the ultrasonic velocity data point relative to the standard ultrasonic velocity developmental envelope is recorded, such as being within the envelope, below the lower boundary, or above the upper boundary. Simultaneously, the positional status of the resistivity data point relative to the standard resistivity developmental envelope is also recorded. Furthermore, the specific degree of deviation is recorded, for example, calculating the relative distance between the data point and the nearest boundary, expressed as a percentage for subsequent analysis. All recorded information is stored as a structured data table, containing fields such as timestamp, data value, boundary value, positional status, and degree of deviation.

[0097] To ensure the accuracy of the comparative analysis, a data quality control mechanism was implemented. For example, when a significant difference was found between the time range of the current data sequence and the time range of the envelope band, data extension or truncation methods were used to ensure consistency in the time range of the comparison. Simultaneously, rules for handling abnormal data were established. For instance, when encountering clearly abnormal data points, data verification was performed before executing the comparative analysis. Verification methods included checking sensor status and reviewing the original data. A tolerance range was also set during the comparative analysis process; for example, fluctuations in data points within a certain range were still considered acceptable. This tolerance range was determined based on the statistical characteristics of historical data.

[0098] All comparative analysis results are saved as structured data records, including timestamps for each time point, current data values, upper boundary values ​​of the envelope, lower boundary values ​​of the envelope, and location status markers. These records facilitate subsequent querying and verification, and also provide data support for confirming the final performance stabilization critical time point. Through the above specific implementation methods, comprehensive and accurate comparative analysis of data sequences and standard development envelopes can be performed, ensuring the reliability and validity of the analysis results.

[0099] S5. Based on the comparative analysis results, when the continuous data sequence is entirely within the preset envelope of the standard performance development of the corresponding material type, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point. The specific implementation is as follows:

[0100] Based on the positional relationship between each data point in the recorded continuous data sequence and the corresponding standard development envelope, this step specifically implements the process of confirming the critical time point for performance stabilization. To determine whether all data points in the continuous data sequence of ultrasonic wave velocity changing over time are within the standard development envelope of ultrasonic wave velocity, the positional status information of each data point recorded during the comparative analysis is first read. This positional status information includes three states: within the envelope, below the lower boundary, or above the upper boundary. This positional status information is then traversed and checked to confirm that the positional status of all data points from the candidate critical point to the current time point is within the envelope. During the check, data integrity verification is set to ensure that all time points have corresponding positional status records. For missing data points, data completion or re-analysis is required. Data completion methods include linear interpolation or trend extrapolation based on adjacent data points.

[0101] The system synchronously determines whether all data points in a continuous data sequence of resistivity changes over time fall within the standard resistivity development envelope, employing the same judgment logic. It reads the positional status records of resistivity data points and iterates through all data points from the candidate critical point to the current time point, confirming that all are within the envelope. The two judgment processes are synchronized to ensure consistency verification of the two parameters within the same time frame. The judgment process also considers the density and uniformity of data point distribution. For example, for time periods with large monitoring intervals, interpolation points are added to ensure accuracy. Cubic spline interpolation is used to ensure data smoothness.

[0102] When both the continuous ultrasonic wave velocity and resistivity data sequences remain within the corresponding standard development envelope throughout, the system performs a candidate critical point confirmation operation. The confirmation process includes multiple verification steps, such as checking the continuity of the data sequence to ensure there are no missing or abnormal interruptions; verifying the rationality of the envelope boundary to confirm that the envelope version used matches the current material type; and checking the consistency of the time range to ensure that the judgment period covers the entire curing process. These verification steps are based on historical data analysis results, such as setting corresponding inspection rules based on common error types in previous tests, including data jump detection and outlier identification.

[0103] To ensure the reliability of the confirmation results, a verification mechanism was implemented. For example, for a confirmed effective performance stabilization critical time point, the data trend over a period before and after it needs to be examined to confirm the continuity of the stability trend. Simultaneously, a maximum allowable deviation is set; for example, individual data points are allowed to deviate from the envelope centerline within a certain range, but as long as they remain within the envelope and the overall trend is stable, they can still be confirmed as effective. The deviation setting is based on the statistical characteristics of a large amount of stable-state data. For example, the average distance between the stable-period data and the envelope centerline plus twice the standard deviation is taken as the maximum allowable deviation range, which is usually controlled within ±5% of the envelope width.

[0104] The validation process also considered the unique characteristics of material performance development. For example, the performance development trends of certain specially formulated materials may systematically differ from the standard envelope. Therefore, an adaptive adjustment mechanism was established. When systematic differences are found, the envelope can be appropriately adjusted based on historical data for that type of material. However, the adjustment range must be controlled within statistically permissible limits; for example, the adjusted envelope boundary should not exceed ±10% of the original boundary. All adjustments must be recorded, including the reason for the adjustment, the adjustment range, and the basis for adjustment, for future reference. The basis for adjustment includes the statistical characteristics of historical data for that type of material and formulation difference analysis reports.

[0105] The final confirmed critical time point for performance stabilization, along with all related analytical data, is stored in the results database. This includes the original data sequence, envelope data, location status records, and various check results from the verification process. The storage format employs a standardized data structure, containing fields such as timestamps, data values, status markers, and verification results. Each field has a clearly defined definition and value range. This data provides comprehensive information support for subsequent result output and verification, and also facilitates access and analysis by other systems. Through the above specific implementation method, the critical time point for performance stabilization can be accurately and reliably confirmed, ensuring the scientific validity and effectiveness of the analytical results.

[0106] S6. Output the effective performance stabilization critical time point, specifically implemented as follows:

[0107] Based on the confirmed effective performance stabilization critical time point and its related analysis data, this step specifically implements the output process. When associating the confirmed effective performance stabilization critical time point with its corresponding ultrasonic wave velocity data sequence characteristics and resistivity data sequence characteristics, a structured database table is first established. This table contains fields such as time point identifier, critical timestamp, ultrasonic wave velocity data sequence characteristic summary, and resistivity data sequence characteristic summary. The ultrasonic wave velocity data sequence characteristic summary includes statistical characteristics such as mean, standard deviation, maximum, and minimum values, while the resistivity data sequence characteristic summary uses the same statistical indicators. The association storage is implemented using a relational database, establishing a primary-foreign key relationship through the time point identifier to ensure data consistency and integrity. A data validation mechanism is set up during storage, such as checking the reasonableness of the data range to prevent abnormal data from being entered into the database. Data validation includes range checks and logical consistency checks. Range checks confirm that the values ​​are within a reasonable physical range, while logical consistency checks ensure that the time sequence is correct and there are no contradictory data.

[0108] A standardized report template is used to generate a report containing valid performance stabilization critical time points and their corresponding monitoring data analysis results. The report contains multiple sections, including basic experimental information, a summary of monitoring data, critical time point analysis results, and data quality assessment. The basic experimental information records fundamental information such as specimen number, material ratio, and curing conditions. The monitoring data summary includes statistical information such as the data acquisition time range, the number of data points, and data integrity indicators. The critical time point analysis results record in detail the identified critical time points and their related parameters, including time point values ​​and confidence levels. The data quality assessment includes quality information such as sensor status records and data anomaly handling records. The report is output in PDF format, while also saving an editable document format for subsequent modifications. The report generation process is automated, automatically filling in data and analysis results based on the template, and performing format checks and content verification during the filling process.

[0109] When simultaneously displaying the effective performance stabilization critical time points within continuous data sequences of ultrasonic velocity and resistivity over time through a visual interface, a multi-view coordinated display technique is employed. The main view displays graphs of both data sequences, with critical time points marked on the curves using special markers, such as vertical dashed lines, and different colors distinguishing between ultrasonic velocity and resistivity data. Auxiliary views display magnified, detailed data, facilitating observation of data characteristics near the critical time points. The display interface supports interactive operations, such as hovering the mouse to display detailed data values, zooming, and panning the data view. The visualization interface is implemented using web technology, supporting cross-platform access and real-time data updates.

[0110] When simultaneously displaying the mode stability and complexity balance values ​​at corresponding time points and their comparison with their respective thresholds, a combination of dashboard and bar chart display is used. The dashboard displays the relative relationship between the current value and the threshold, using colors to distinguish between normal, warning, and abnormal ranges; for example, green indicates normal, yellow indicates warning, and red indicates abnormal. The bar chart displays historical value change trends and uses reference lines to mark threshold positions. The display interface supports multi-time point comparisons, allowing simultaneous display of value comparisons at multiple critical time points, facilitating the analysis of performance development trends and patterns. The threshold display employs a dynamic adjustment mechanism, automatically adjusting the display scale based on data characteristics.

[0111] When displaying the comparative analysis results of continuous data sequences and preset corresponding material standard performance development envelopes, an overlay display method is used. The current data sequence curve and the envelope region are simultaneously displayed in the same coordinate system, with the envelope represented by a semi-transparent band and the current data sequence represented by a solid line. The display interface supports envelope display options, such as choosing to display the complete envelope or only the boundary line, and adjusting the envelope transparency. It also provides difference analysis functions, such as calculating the average deviation between the data sequence and the center line of the envelope, and statistically analyzing the proportion of data points outside the envelope range. The difference analysis results are displayed simultaneously in numerical and graphical formats.

[0112] All output results are subject to version management and access control mechanisms. Each output result records the generation time, generation parameters, and operator information for easy traceability and verification. Access control is configured with different permission levels; for example, ordinary users can only view results, advanced users can modify display parameters, and administrators can adjust output templates and threshold parameters. Output data is backed up regularly using automatic backup strategies, such as daily incremental backups and weekly full backups, to ensure data security. Backup data is stored encrypted, and access logs record all data operations. Through the above specific implementation methods, the system can comprehensively, accurately, and reliably output the performance stabilization critical time point and its related analysis results, providing users with complete and intuitive analysis reports and visualizations.

[0113] Example 2: Figure 2 A schematic diagram of a soft soil reinforcement material performance analysis system according to the present invention is provided. The soft soil reinforcement material performance analysis system includes the following modules:

[0114] The continuous monitoring module is used to continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process through sensors, and obtain a continuous data sequence of performance parameters changing over time.

[0115] The feature calculation module is used to calculate, based on continuous data sequences, the pattern stability quantity used to characterize the consistency of their evolutionary features and the complexity balance quantity used to characterize the regularity of their behavior across multiple time scales.

[0116] The critical identification module is used to identify the corresponding time point as a candidate critical point for performance stabilization when the pattern stability value is continuously higher than the first threshold and the complexity balance value is continuously lower than the second threshold.

[0117] The comparative analysis module is used to compare and analyze continuous data sequences with preset standard performance development envelopes for corresponding material types.

[0118] The confirmation and judgment module is used to confirm the corresponding candidate critical point as an effective performance stabilization critical time point when the continuous data sequence is within the preset standard performance development envelope of the corresponding material type throughout the entire process, based on the comparative analysis results.

[0119] The results output module is used to output the effective performance stabilization critical time point.

[0120] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0121] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0122] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0125] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0126] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of analyzing the performance of a soft soil reinforcement material, characterized by, The method comprises the following steps: S1, continuously monitoring the performance parameters of the soft soil reinforcement material test piece during the curing process through a sensor to obtain a continuous data sequence of the performance parameters changing with time; S2, based on the continuous data sequence, respectively calculating a mode stability quantity for characterizing the consistency of the evolution characteristics and a complexity balance quantity for characterizing the behavior regularity in multiple time scales; S3, when the mode stability quantity continuously exceeds a first threshold value and the complexity balance quantity continuously falls below a second threshold value, identifying the corresponding time point as a candidate critical point of performance stabilization; S4, comparing and analyzing the continuous data sequence with a preset corresponding class material standard performance development envelope band; S5, according to the comparison and analysis result, when the continuous data sequence is within the range of the preset corresponding class material standard performance development envelope band all the time, confirming that the corresponding candidate critical point is an effective performance stabilization critical time point; S6, outputting the effective performance stabilization critical time point; Based on the continuous data sequence, respectively calculating a mode stability quantity for characterizing the consistency of the evolution characteristics and a complexity balance quantity for characterizing the behavior regularity in multiple time scales, comprising: Based on the continuous data sequence of the ultrasonic wave velocity changing with time, calculating the warping path distance between the current data sequence and the reference stable mode sequence through a dynamic time warping algorithm as the mode stability quantity; Based on the continuous data sequence of the resistivity changing with time, calculating the variance of the entropy values in different time scales through a multi-scale entropy analysis method as the complexity balance quantity; Based on the continuous data sequence of the ultrasonic wave velocity changing with time, calculating the warping path distance between the current data sequence and the reference stable mode sequence through a dynamic time warping algorithm as the mode stability quantity, comprising: Obtaining a reference stable mode sequence established in advance through a large number of stable period material samples; Calculating the minimum cumulative distance between the current ultrasonic wave velocity data sequence and the reference stable mode sequence through a dynamic time warping algorithm, and quantifying the minimum cumulative distance as the warping path distance; The smaller the warping path distance value is, the higher the consistency degree of the current data sequence with the stable mode is; Based on the continuous data sequence of the resistivity changing with time, calculating the variance of the entropy values in different time scales through a multi-scale entropy analysis method as the complexity balance quantity, comprising: Coarsely granulating the resistivity data sequence to obtain sub-sequences in multiple time scales; Calculating the sample entropy value of each scale sub-sequence; Statistically analyzing the dispersion degree of the sample entropy values in all scales, and taking the variance of the sample entropy values as the complexity balance quantity; The smaller the variance of the sample entropy values is, the more consistent the behavior of the material in different time scales is; Comparing and analyzing the continuous data sequence with a preset corresponding class material standard performance development envelope band, comprising: Obtaining a preset corresponding class material standard performance development envelope band, including an ultrasonic wave velocity standard development envelope band and a resistivity standard development envelope band; Comparing and analyzing the continuous data sequence of the ultrasonic wave velocity changing with time with the ultrasonic wave velocity standard development envelope band point by point; Synchronously comparing and analyzing the continuous data sequence of the resistivity changing with time with the resistivity standard development envelope band point by point; Record the position relationship of each data point in the continuous data sequence and the corresponding standard development envelope band.

2. The method of claim 1, wherein The performance parameters of the soft soil reinforcement material test piece during the curing process are continuously monitored by sensors to obtain a continuous data sequence of the performance parameters changing over time, including: The change of ultrasonic wave velocity of the soft soil reinforcement material test piece during the curing process is continuously monitored by the piezoelectric ceramic sensor embedded in the soft soil reinforcement material test piece. The change of resistivity of the soft soil reinforcement material test piece during the curing process is continuously monitored by the four-electrode resistance probe embedded in the soft soil reinforcement material test piece. The continuous data sequence of ultrasonic wave velocity changing over time and the continuous data sequence of resistivity changing over time are obtained.

3. The method of claim 2, wherein the soft soil reinforcement material performance analysis method is characterized by, When the mode stability quantity continuously exceeds the first threshold value and the complexity balance quantity continuously falls below the second threshold value, the corresponding time point is identified as a candidate critical point of performance stabilization, including: Based on the mode stability quantity, the first threshold value is set to the statistical significance level of the distance value obtained by comparing the reference stable mode sequence with itself using the dynamic time warping algorithm; Based on the complexity balance quantity, the second threshold value is set to the relative stable state reference value of the entropy value obtained by analyzing the reference material sequence in a completely stable state using the multi-scale entropy analysis method; When the time length of the mode stability quantity continuously exceeding the first threshold value reaches the preset monitoring period and the time length of the complexity balance quantity continuously falling below the second threshold value reaches the preset monitoring period, the first time point that meets the time duration requirement is recorded as the candidate critical point of performance stabilization.

4. The method of claim 3, wherein the soft soil reinforcement material performance analysis method is characterized by, According to the comparative analysis result, when the continuous data sequence is within the range of the preset corresponding material standard performance development envelope band throughout the process, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point, including: Based on the position relationship of each data point in the recorded continuous data sequence and the corresponding standard development envelope band, it is judged whether all data points in the continuous data sequence of ultrasonic wave velocity changing over time are within the range of the ultrasonic wave velocity standard development envelope band; Synchronously judge whether all data points in the continuous data sequence of resistivity changing over time are within the range of the resistivity standard development envelope band; When the ultrasonic wave velocity continuous data sequence and the resistivity continuous data sequence are both within the range of the corresponding standard development envelope band throughout the process, the candidate critical point is confirmed as an effective performance stabilization critical time point.

5. The method of claim 4, wherein the soft soil reinforcement material performance analysis method is characterized by, Output the effective performance stabilization critical time point, including: The confirmed effective performance stabilization critical time point is associated with its corresponding ultrasonic wave velocity data sequence feature and resistivity data sequence feature and stored; a report file containing the effective performance stabilization critical time point and its corresponding monitoring data analysis result is generated; The effective performance stabilization critical time point is displayed in the continuous data sequence of ultrasonic wave velocity changing over time and the continuous data sequence of resistivity changing over time through a visual interface, and the mode stability quantity value and the complexity balance quantity value of the corresponding time point and their comparison with the respective threshold values are displayed, as well as the comparative analysis result of the continuous data sequence and the preset corresponding material standard performance development envelope band.

6. A soft soil reinforcing material performance analysis system for implementing the soft soil reinforcing material performance analysis method according to any one of claims 1 to 5, characterized by, The following modules are included: A continuous monitoring module is configured to continuously monitor the performance parameters of the soft soil reinforcement material test piece during the curing process through the sensors, and obtain a continuous data sequence of the performance parameters changing over time; A feature calculation module is configured to calculate a mode stability quantity for characterizing the consistency of the evolution features and a complexity balance quantity for characterizing the behavior regularity in the multi-time scale based on the continuous data sequence, respectively; A critical point identification module is configured to identify the corresponding time point as a candidate critical point of performance stabilization when the mode stability quantity is continuously higher than a first threshold value and the complexity balance quantity is continuously lower than a second threshold value; A comparative analysis module is configured to compare and analyze the continuous data sequence with a preset standard performance development envelope band of the corresponding material class; A confirmation and judgment module is configured to confirm the corresponding candidate critical point as an effective performance stabilization critical time point according to the comparative analysis result when the continuous data sequence is within the preset standard performance development envelope band of the corresponding material class throughout the range. A result output module is configured to output the effective performance stabilization critical time point.

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