Concrete internal quality detection method based on complex environment of diversion tunnel

Abstract

The application relates to the technical field of concrete quality detection, and discloses a method for detecting the internal quality of concrete based on the complex environment of a diversion tunnel. In multiple environment scenes, the method calibrates the uniformity of the internal quality of the concrete according to starting quality characteristic parameters of each scene, and obtains calibrated quality characteristic parameters; a target state that the concrete needs to reach is set, a detection device is operated according to the calibration parameters corresponding to the target state, and a real quality state is collected; when the difference between the target state and the real state reaches a predetermined tolerance limit, the quality difference parameters corresponding to the two are deduced; the calibration parameters corresponding to the target state are changed based on the parameters, and changed quality characteristic parameters are generated; the changed parameters are used as new calibration parameters to execute the detection operation again, and the iteration is circular until the difference between the target state and the real state is smaller than the predetermined tolerance limit.

Description

Technical Field

[0001] This invention relates to the field of concrete quality testing technology, specifically a method for testing the internal quality of concrete in the complex environment of a water diversion tunnel. Background Technology

[0002] As a key structure in water conservancy projects, the internal quality of the concrete lining of water diversion tunnels directly affects the safety and durability of the project. The internal environment of tunnels is complex, with high humidity, temperature variations, and seepage pressure, among other factors. These environmental conditions significantly impact the forming and long-term performance of concrete. Traditional concrete quality testing methods mainly rely on core drilling for laboratory compressive strength tests or the use of rebound hammers and ultrasonic testing for surface or shallow non-destructive testing. Core drilling is a destructive testing method with limited sampling points, making it difficult to reflect the overall quality of the concrete and causing structural damage. Non-destructive testing methods such as rebound hammers and ultrasonic testing are significantly affected by surface conditions, humidity, and internal reinforcement, making it difficult to guarantee accuracy and reliability in the complex environment of tunnels.

[0003] Existing internal quality inspection technologies, such as ground-penetrating radar (GPR) and impact-echo methods, can theoretically detect internal defects, but they have limitations in practical applications. GPR is sensitive to the moisture content of the medium, and its signal attenuation is severe in humid tunnel environments, resulting in decreased resolution. Impact-echo methods struggle to accurately identify deep defects and are highly dependent on operator experience. These methods typically provide qualitative defect indications and are difficult to quantitatively assess quality characteristics such as concrete homogeneity and strength distribution. More importantly, the parameter settings of existing detection methods largely rely on general empirical values ​​and fail to adaptively adjust to specific tunnel environments, leading to unstable detection results in different environmental areas and a high risk of missed or false detections.

[0004] The internal quality of concrete interacts in a complex way with its external environment; the formation and development of the internal structure of concrete with the same mix proportion will differ under different environmental scenarios. However, currently, there is a lack of a method that can take into account the influence of environmental factors and dynamically calibrate and optimize the testing process. Testing devices operate according to a fixed pattern, and the data acquired may have systematic deviations from the actual quality state, which cannot be effectively corrected by a single measurement. Therefore, there is a need for an intelligent testing method that can adapt to complex environments, achieve self-calibration of testing parameters, and gradually approach the actual quality state. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting the internal quality of concrete in the complex environment of water diversion tunnels, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a method for detecting the internal quality of concrete in the complex environment of a water diversion tunnel. The method includes: performing uniformity calibration on the internal quality of concrete under multiple environmental scenarios, based on the initial quality characteristic parameters corresponding to each environmental scenario, to obtain calibrated quality characteristic parameters of concrete under each environmental scenario; setting a target state for the internal quality of concrete, manipulating a detection device to operate based on the calibrated quality characteristic parameters corresponding to the target state, while simultaneously collecting the actual quality state of concrete through a sensing device; if the difference between the target state and the actual state reaches or exceeds a predetermined tolerance limit, deriving a quality difference parameter using the calibrated quality characteristic parameters corresponding to the target state and the quality characteristic parameters corresponding to the actual state; modifying the calibrated quality characteristic parameters corresponding to the target state using the quality difference parameter, generating modified quality characteristic parameters corresponding to the target state; treating the modified quality characteristic parameters as the new calibrated quality characteristic parameters corresponding to the target state, and re-executing the operation of manipulating the detection device until the difference between the target state and the actual state is less than the predetermined tolerance limit.

[0007] Preferably, the process of calibrating the internal quality uniformity of concrete specifically includes: using initial quality characteristic parameters to guide the detection device to scan the concrete and obtain the internal response information of the concrete; extracting the true quality state parameters from the internal response information; calculating the quality uniformity measure based on the true quality state parameters; when the quality uniformity measure is higher than the preset uniformity limit, transforming the true quality state parameters into true quality characteristic parameters according to the pre-constructed environmental quality correlation model, wherein the environmental quality correlation model expresses the relationship between the environmental scenario and the quality characteristic parameters; using a parameter update model to iteratively process the true quality characteristic parameters to generate iterated quality characteristic parameters; using the iterated quality characteristic parameters as initial quality characteristic parameters and performing the scanning operation again until the quality uniformity measure is lower than the preset uniformity limit; and identifying the iterated quality characteristic parameters as calibrated quality characteristic parameters.

[0008] Preferably, when using a parameter update model for iterative processing, the following actions are performed: compare the actual quality characteristic parameters with the initial quality characteristic parameters to find the difference parameters in the current iteration cycle; obtain the corresponding correction weight parameters for the current iteration cycle, use the correction weight parameters to scale the difference parameters, and generate a correction amount for the initial quality characteristic parameters; apply the correction amount to the initial quality characteristic parameters to realize the quality characteristic parameters after iteration.

[0009] Preferably, the method for obtaining the corresponding correction weight parameters for the current iteration cycle is as follows: summarize the difference change direction, difference change magnitude, and difference change tendency of all detection positions in the current iteration cycle; construct the iteration state vector for each detection position based on the difference change direction, difference change magnitude, and difference change tendency; use the iteration state vector as the search key to find the corresponding correction weight in the pre-prepared correction weight list; and assemble the correction weight parameters for the current iteration cycle according to the correction weight of each detection position.

[0010] Preferably, after obtaining the calibrated quality characteristic parameters, the method is further implemented as follows: for each environmental scenario, the quality fitting coefficient parameters under each environmental scenario are solved by combining the corresponding initial quality characteristic parameters and the calibrated quality characteristic parameters; based on each environmental scenario and its quality fitting coefficient parameters, an environmental quality mapping model is created, wherein the environmental quality mapping model defines the correspondence between environmental scenarios and quality fitting coefficient parameters.

[0011] Preferably, the step of modifying the calibrated quality characteristic parameters using the quality difference parameters includes: retrieving the quality fitting coefficient parameters corresponding to the target state through the environmental quality mapping model; using the quality fitting coefficient parameters to correct the quality difference parameters to obtain the correction amount for the calibrated quality characteristic parameters; and applying the correction amount to modify the calibrated quality characteristic parameters to obtain the modified quality characteristic parameters.

[0012] Preferably, when obtaining the internal response information of concrete, ultrasonic detection technology is used to project sound wave signals onto the concrete and record the sound wave transmission time and the degree of signal attenuation; based on the sound wave transmission time and the degree of signal attenuation, a sound velocity distribution image and an attenuation distribution image are created; the sound velocity distribution image and the attenuation distribution image are fused to form the internal response information.

[0013] Preferably, when identifying the difference parameters for the current iteration cycle, the numerical difference between the actual quality characteristic parameters and the initial quality characteristic parameters is calculated; the numerical difference is standardized to obtain the normed difference; and the difference parameters are formed based on the normed difference.

[0014] The preferred process for constructing the corrected weight list is as follows: performing iterative operations on several experimental samples, recording the iterative state vector and the optimal corrected weight at each detection position; learning the mapping relationship from the iterative state vector to the optimal corrected weight through a neural network algorithm; and saving the mapping relationship as a corrected weight list.

[0015] Preferably, when solving the quality fitting coefficient parameters for each environmental scenario, for each environmental scenario, the initial quality characteristic parameters and the calibrated quality characteristic parameters are fed into the multivariate regression model; the gradient descent method is used to optimize the coefficients, and the quality fitting coefficient parameters are output.

[0016] Compared with existing technologies, the beneficial effects of this invention are: by performing uniformity calibration under multiple environmental scenarios, this invention reduces the interference of complex environmental factors on the detection results. Environmental conditions vary in different sections within the water diversion tunnel, affecting the apparent properties of concrete and the propagation of detection signals. This method performs independent calibration for different environmental scenarios, generating calibrated parameters adapted to specific scenarios, thus improving the adaptability of the detection under different environmental conditions and the comparability of results.

[0017] By employing a mechanism that compares the target state with the actual state and sets tolerance limits, a closed-loop feedback and self-correction mechanism is achieved in the detection process. Traditional detection methods are mostly open-loop processes, generating detection results all at once. This method, however, collects the actual state in real time and compares it with the target expectation. When the deviation exceeds the limit, a parameter correction process is automatically triggered, enabling the detection process to have self-correcting capabilities and gradually narrowing the gap between the measured value and the actual value.

[0018] This method dynamically adjusts the detection characteristic parameters based on quality difference parameters, enabling the detection device to adapt its behavior to the actual quality condition of the concrete. Instead of mechanically executing a preset detection procedure, it intelligently adjusts the guidance parameters for the next detection based on the deviation between the previous detection result and the desired target, making the detection action more targeted and improving detection efficiency and accuracy.

[0019] The iterative execution of detection and parameter adjustment processes ensures the high reliability of the final detection results. Through multiple cycles of "detection-comparison-adjustment," the system continuously optimizes its detection strategy, ultimately keeping the difference between the detection results obtained under the guidance of the set target state and the expected target within an acceptable range. This iterative approximation method effectively overcomes random errors or systematic biases that may be caused by environmental interference, equipment errors, and other factors in a single detection, significantly improving the confidence level of the detection results.

[0020] This method transforms concrete quality testing from a static, passive operation into a dynamic, proactive optimization process, providing a more accurate and robust technical means for the health diagnosis of concrete structures in complex environments such as water diversion tunnels. This is of great significance for ensuring the long-term safe operation of water conservancy projects. Attached Figure Description

[0021] Figure 1 This is an iterative change diagram showing the difference between the target state and the actual state.

[0022] Figure 2 A sub-flowchart for calibrating the internal quality uniformity of concrete.

[0023] Figure 3 Flowchart of parameter update model iterative processing.

[0024] Figure 4 A comparison chart showing the effects of iterative correction of concrete quality parameters. Detailed Implementation

[0025] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 This invention provides a method for detecting the internal quality of concrete in the complex environment of a water diversion tunnel. The method includes: 1) Under multiple environmental scenarios within the water diversion tunnel, each environmental scenario corresponds to an initial quality characteristic parameter, which is used to initialize the detection process. 2) The internal quality of the concrete is calibrated for homogeneity based on the initial quality characteristic parameter. This homogeneity calibration aims to eliminate the influence of environmental factors on quality detection, resulting in calibrated quality characteristic parameters of the concrete under each environmental scenario. The calibrated quality characteristic parameters represent the standardized quality state of the concrete under a specific environment. 3) A target state for the internal quality of the concrete is set, which is an ideal quality level predefined based on engineering requirements. 4) A detection device is operated based on the calibrated quality characteristic parameters corresponding to the target state. This detection device includes, but is not limited to, ultrasonic detection equipment or electromagnetic sensors. 5) The actual quality state of the concrete is collected through sensing equipment, reflecting the actual internal conditions of the concrete. 6) The difference between the target state and the actual state is compared, and the difference is calculated numerically. If the difference reaches or exceeds a predetermined tolerance limit, which is set by the user and represents the acceptable range of quality deviation. The quality difference parameter is derived using the calibrated quality characteristic parameters corresponding to the target state and the actual state. This parameter quantifies the deviation between the target and actual states. Using this quality difference parameter, the calibrated quality characteristic parameters corresponding to the target state are modified. This modification process involves a parameter adjustment algorithm, generating modified quality characteristic parameters for the target state. These modified quality characteristic parameters are then considered as the new calibrated quality characteristic parameters for the target state. The operation of manipulating the detection device is then repeated. This iterative process continues until the difference between the target and actual states is less than a predetermined tolerance limit, thereby ensuring detection accuracy and reliability.

[0027] Example 1: See Figure 2The process of calibrating the uniformity of concrete internal quality involves using initial quality characteristic parameters to guide a detection device in scanning the concrete. The detection device's operating mode, set based on these initial quality characteristic parameters, includes the initial configuration of transmission power, scanning frequency, and scanning path. The scanning operation covers a predetermined area of ​​the concrete structure and obtains the concrete's internal response information, which is represented by a sequence of raw voltage signals captured by the sensor array. The extraction of true quality state parameters from this internal response information employs digital signal processing technology. After noise reduction and filtering of the raw voltage signals, time-domain and frequency-domain feature values ​​are extracted. These true quality state parameters include wave velocity, amplitude attenuation coefficient, and spectral centroid position. A quality uniformity metric is then calculated based on these true quality state parameters. This metric is obtained by calculating the coefficient of variation of the parameter values ​​at each scanning point, defined as the ratio of the parameter's standard deviation to its mean. When the quality uniformity metric exceeds the preset uniformity limit, the preset uniformity limit is set to a value range of 0.15-0.25 based on the concrete strength grade. The real quality state parameters are transformed into real quality feature parameters according to the pre-built environmental quality correlation model. The environmental quality correlation model is trained and generated using the random forest algorithm. The model input includes three types of environmental scenario parameters: ambient temperature, humidity, and surrounding rock pressure.

[0028] The environmental quality correlation model is constructed based on a historical testing database, which stores calibration data of concrete test blocks under different environmental conditions. The model output is the corrected value of quality characteristic parameters after compensating for environmental interference. A parameter update model is used to iteratively process the actual quality characteristic parameters. The parameter update model employs a gradient descent algorithm with a momentum coefficient set to 0.9. The process of generating the iteratively updated quality characteristic parameters includes vector projection operations in the parameter space. The iteratively updated quality characteristic parameters are used as the initial quality characteristic parameters for a second scanning operation. The scanning operation uses a spiral path to cover the concrete cross-section, and the path spacing is dynamically adjusted according to the detection accuracy requirements. The parameter update and scanning operations are executed cyclically until the quality uniformity metric falls below a preset uniformity limit. The termination condition simultaneously meets the convergence threshold that the parameter change rate is less than 1e-5. The iteratively updated quality characteristic parameters are recognized as calibrated quality characteristic parameters and stored in a structured data format for subsequent quality assessment. The detection device integrates a temperature compensation module during the scanning process. The temperature compensation module monitors the concrete surface temperature in real time and corrects the sound wave transmission speed. Internal response information is acquired using a multi-probe synchronous acquisition scheme, with sixteen ultrasonic probes arranged in a matrix to simultaneously transmit and receive signals. The calculation of quality uniformity measurement incorporates a spatial weighting factor, with the weight of scan points in the edge region set to 0.7 times that of the central region to reduce boundary effects. The online update mechanism of the environmental quality correlation model is implemented through a sliding window, with the window size set to the most recent 100 sets of detection data. The iteration step size of the parameter update model adopts an adaptive adjustment strategy, halving the step size when the error increases after three consecutive iterations. The repeated execution of the scanning operation is automated and looped via a PLC controller, with the loop cycle synchronized with the concrete hydration heat monitoring. The dynamic adjustment of the preset uniformity limit is based on the tunnel burial depth parameter, increasing the limit value by 0.02 for every ten meters of depth increase. The transformation of the actual quality state parameters incorporates an outlier removal mechanism, eliminating data points that deviate from the mean by more than three standard deviations.

[0029] The input preprocessing of the environmental quality correlation model includes environmental parameter normalization, mapping temperature, humidity, and pressure to the [0,1] interval. The iteration termination judgment of the parameter update model adds a maximum iteration limit, set to 200 iterations to prevent infinite loops. The spatial distribution evaluation of the quality uniformity metric uses the Moran index method; an index greater than 0.5 is considered significant spatial autocorrelation. The command protocol for the detection device adopts the Modbus communication standard, with a transmission rate set to 115200bps. Real-time parsing of internal response information uses the Fast Fourier Transform algorithm, with a transform length of 2048 points. Feature extraction of the true quality state parameters includes wavelet packet decomposition, with four decomposition layers covering the 0-100kHz frequency band. The prediction confidence evaluation of the environmental quality correlation model uses the Bootstrap method; model retraining is triggered when the confidence level falls below 90%. Parameter initialization of the parameter update model uses the Xavier initialization method to maintain stable variance during forward propagation. The mesh generation of the concrete scanning area adopts an adaptive quadtree structure, with automatic mesh refinement in areas with dense defects. The initial quality characteristic parameters were set based on the concrete design mix proportion, with the water-cement ratio parameter converted into the acoustic reference velocity. Verification of the calibrated quality characteristic parameters employed cross-validation, dividing the concrete specimens into training and testing sets. Feature selection for the environmental quality correlation model used the mutual information method, retaining the top five features with mutual information values ​​related to environmental parameters. The momentum term update of the parameter update model used the Nesterov accelerated gradient method, pre-calculating the gradient descent direction. Time-series analysis of quality uniformity measurement used a sliding window standard deviation, with a window width set to 10 scan cycles. The detection device's fault self-diagnosis function monitored probe impedance changes, triggering an alarm when impedance anomalies exceeded 20%. Compressed storage of internal response information used a run-length encoding algorithm, with a compression ratio controlled at approximately 50%. Standardization of the actual quality state parameters employed the RobustScaler method, scaling using median and quartile ranges.

[0030] The distributed training of the environmental quality correlation model employs a parameter server architecture, with worker nodes processing data from different environmental scenarios in parallel. Parallel computation of the parameter update model is accelerated using CUDA, utilizing GPUs to process parameter updates for multiple detection points in parallel. Scan path optimization utilizes a genetic algorithm, with the fitness function defined as a weighted sum of path length and coverage. Preset uniformity limits are set based on concrete pouring layers, with each layer having its own independent limit value. The fusion of real quality state parameters employs DS evidence theory, merging confidence assignments from multiple sensors. The interpretability analysis of the environmental quality correlation model uses the SHAP value method to quantify the contribution of each environmental parameter to the output. Robustness enhancement of the parameter update model utilizes the Huber loss function to reduce the impact of outliers on parameter updates. Visualization of quality uniformity measurement uses heatmaps, with color mapping using Jet chromatography. Synchronization triggering of the detection devices utilizes a GPS timing module, with time synchronization errors between probes less than 1 microsecond. The quality assessment of internal response information uses signal-to-noise ratio (SNR) calculation; data segments with an SNR below 20 dB are marked as invalid. The temporal alignment of the true quality state parameters employs a dynamic time warping algorithm to eliminate the impact of scanning speed fluctuations. Version management of the environmental quality correlation model utilizes a Git system, generating new version tags with each model update. Hyperparameter tuning of the parameter update model employs a Bayesian optimization method, with the optimization objective being iterative convergence speed. An interruption recovery mechanism for the scanning operation records the breakpoint location, allowing scanning to resume from the last valid point after recovery. The adaptive adjustment of the preset uniformity limit is based on the age parameter, tightening the limit value by 0.01 for every seven days increase in age. Spatial interpolation of the true quality state parameters uses the Kriging method, with a Gaussian model selected as the variogram function. Online learning of the environmental quality correlation model employs an incremental learning algorithm, updating model parameters via streaming new data. The memory effect of the parameter update model utilizes hidden state vectors, which carry historical iteration information. Multi-scale analysis of quality uniformity measurement employs wavelet transform, with decomposition scales covering the range of 1 cm to 1 m. The calibration cycle of the detection device is set to 24 hours, and standard aluminum alloy test blocks are used as calibration standard blocks. Anomaly detection for internal response information employs the Isolation Forest algorithm, removing data points with anomaly scores exceeding 0.6. Dimensionality reduction of the true quality state parameters utilizes the t-SNE method, mapping high-dimensional parameters to a two-dimensional space for visualization.

[0031] The environmental quality correlation model is deployed via a RESTful interface, receiving environmental parameters in JSON format. The numerical stability of the parameter update model is maintained through gradient pruning, with the gradient norm threshold set to 1.0. The scanning operation features a safety interlock design including an emergency stop button and light curtain protection, immediately halting transmission upon abnormal triggering. An expert adjustment interface for preset uniformity limits provides slider controls, allowing engineers to fine-tune based on experience. Persistent storage of real-world quality state parameters utilizes a time-series database, with data points accompanied by timestamps and location tags. A / B testing of the environmental quality correlation model employs a blue-green deployment scheme, running the old and new models in parallel for comparison. Convergence diagnosis of the parameter update model uses the Gelman-Rubin statistic, with convergence determined when the statistic is less than 1.1. The quality uniformity measurement alarm threshold is set with multiple levels: a yellow alert triggers a review, and a red alert triggers a shutdown. The detection device's status monitoring integrates a vibration sensor, automatically pausing scanning when vibration exceeds limits. Internal response information is backed up using an off-site disaster recovery solution, synchronizing to cloud storage every ten minutes. Access control for real-world quality status parameters employs role-based access control, allowing quality inspectors to read and engineers to write. The feature cross-referencing in the environmental quality correlation model utilizes multinomial expansion to generate interaction terms for temperature and humidity. Distributed training of the parameter update model uses the AllReduce communication mode, synchronizing gradient information between nodes. The obstacle avoidance algorithm for the scanning path employs an artificial potential field method to avoid areas with dense rebar. Automatic optimization of the preset uniformity limit uses a reinforcement learning algorithm, with detection efficiency as the reward function. Version tracking of real-world quality status parameters uses blockchain notarization, recording the hash value of each parameter modification. Fairness constraints in the environmental quality correlation model include statistical parity conditions to prevent discrimination caused by environmental parameter bias. Uncertainty quantification in the parameter update model uses the Monte Carlo Dropout method, outputting the parameter prediction interval. Trend prediction for quality uniformity measurement uses the ARIMA model, predicting uniformity changes three periods in advance. Lifetime prediction for the detection device uses the Weibull distribution model, estimating remaining lifespan based on runtime. Encrypted transmission of internal response information uses the AES-256 algorithm, with the key rotating hourly. Laplace noise is added to the differential privacy protection of the true quality state parameters, and the privacy budget is set to 0.1.

[0032] Example 2: See Figure 3When using a parameter update model for iterative processing, the following actions are performed: First, the true mass characteristic parameters are compared with the initial mass characteristic parameters. The true mass characteristic parameters are derived from the sound velocity values ​​and attenuation coefficient matrix collected by the sensor array during the current scanning cycle, while the initial mass characteristic parameters are the sound velocity reference values ​​and attenuation reference values ​​calculated in the previous iteration. Second, the difference parameters for the current iteration cycle are identified. These difference parameters are obtained through element-wise vector subtraction, and the calculation results form a difference matrix. Third, the corresponding correction weight parameters for the current iteration cycle are obtained. These correction weight parameters are weight coefficient matrices with the same dimensions as the difference matrix. Fourth, the difference parameters are scaled using the correction weight parameters. The scaling process involves the Hadamard product of the weight coefficient matrix and the difference matrix, generating a correction matrix for the initial mass characteristic parameters. Fifth, the correction is applied to the initial mass characteristic parameters. This application operation is represented by matrix addition, thus updating the mass characteristic parameters after each iteration.

[0033] The method for obtaining the corresponding correction weight parameters for the current iteration cycle is as follows: Summarize the direction, magnitude, and tendency of difference changes for all detection locations within the current iteration cycle, along with the coordinates of the grid cells corresponding to the concrete surface at each detection location. The direction of difference change is determined by the sign function of the parameter change value, the magnitude of difference change is calculated using the absolute value of the parameter change, and the tendency of difference change is obtained by calculating the slope of the parameter change over three consecutive iteration cycles. Based on the direction, magnitude, and tendency of difference changes for each detection location, an iterative state vector is constructed for each location. This iterative state vector is a three-dimensional vector containing a direction code, a normalized magnitude value, and a tendency angle value. Using the iterative state vector as the search key, the corresponding correction weight is found in a pre-prepared list of correction weights, which is stored as a key-value pair database. Based on the correction weights for each detection location, the correction weight parameters for the current iteration cycle are assembled. The assembly process uses a spatial interpolation algorithm to fill in the blank areas of the grid.

[0034] The revised weight list is constructed based on the offline training phase, with training data derived from standard tests on laboratory concrete specimens. The dimensional expansion of the iterative state vector incorporates environmental parameters, with temperature gradients and humidity changes as additional dimensions. Fuzzy logic is introduced to determine the direction of difference changes; changes with absolute values ​​less than a threshold are considered directionless. Logarithmic scaling is used to dynamically adjust the magnitude of difference changes, accommodating parameter variations with large magnitudes. Exponentially weighted moving averages are used for long-term tracking of difference change tendencies, assigning higher weights to recent data. Incremental learning is employed to update the revised weight list, with new detection data progressively optimizing the weight mapping. L2 penalty terms are added to the regularization of the weight coefficient matrix to prevent overfitting. Preprocessing of the difference matrix includes outlier detection, with Grubbs 2 testing to remove significant outliers. The numerical stability of the Hadamard product operation is ensured through scaling factors to avoid floating-point underflow. Kahan's summation algorithm is used to control rounding errors in matrix addition operations, compensating for accumulated errors. A dynamic adjustment strategy is used for mesh generation at detection locations, automatically increasing mesh density in areas with dense defects. The transmission of iterative state vectors employs compressed encoding, with a lossy compression ratio of 10:1 to balance accuracy and efficiency. Distributed storage of the corrected weight list utilizes a consistent hashing algorithm, with data shards stored across multiple nodes. Spatiotemporal correlation analysis of the direction of difference changes uses a Markov model to predict the trend of changes in the next cycle. Gaussian mixture models are used to fit the probability distribution of the magnitude of difference changes to identify typical change patterns. Multi-scale analysis of the tendency of difference changes uses wavelet transform to separate long-term trends from short-term fluctuations. Reliability assessment of the corrected weight parameters uses a confidence propagation algorithm, triggering a review scan in low-confidence regions. Sparsity of the weight coefficient matrix is ​​achieved through threshold pruning, with elements having an absolute value less than 1e-6 forced to zero. Rank estimation of the difference matrix uses singular value decomposition, automatically increasing sampling points when the rank is insufficient. Hardware acceleration of the Hadamard product operation is implemented using an FPGA, with 1024 parallel computing units.

[0035] The persistent storage of the iterative state vector adopts a columnar storage format, facilitating batch analysis of historical iteration data. Version management of the corrected weight list employs multi-version concurrency control, supporting rolling rollback operations. Cluster analysis of the direction of difference changes uses the DBSCAN algorithm to identify spatial clustering patterns. Quantile statistics of the magnitude of difference changes use a t-digest structure to calculate percentile values ​​in real time. Abrupt change detection of difference change trends uses a CUSUM control chart to promptly detect abnormal parameter jumps. Uncertainty quantification of corrected weight parameters uses the Monte Carlo method to generate a probability distribution of weight values. Condition number monitoring of the weight coefficient matrix uses SVD decomposition, triggering matrix reconstruction when the condition number is too high. Parallel decomposition of the difference matrix uses the LU decomposition algorithm, utilizing GPU acceleration. Automatic differentiation of the Hadamard product operation supports backpropagation, facilitating integration with neural networks. Memory access optimization for matrix addition operations uses a cache block strategy to improve cache hit rate.

[0036] The coordinate registration of concrete testing points utilizes a total station measurement system, achieving millimeter-level accuracy in 3D coordinates. Visualization of the iterative state vector employs a 3D scatter plot, with color mapping representing vector magnitude. The query interface for the corrected weight list provides a RESTful API, supporting remote service calls. Spatial autocorrelation calculation of the direction of difference changes uses the Moran exponent; a rescan is initiated when the exponent exceeds 0.7. Spatial interpolation of the magnitude of difference changes uses the radial basis function method, with the interpolation radius adaptively adjusted based on the grid size. Prediction of the tendency of difference changes utilizes a long short-term memory network, with the input sequence length set to 10 cycles. Online learning of the corrected weight parameters employs stochastic gradient descent, with the learning rate dynamically adjusted based on the convergence speed. Eigenvalue analysis of the weight coefficient matrix uses a power iteration method, with the dominant eigenvector guiding the parameter update direction. Rank-one updates of the difference matrix utilize the Sherman-Morrison formula, avoiding full matrix inversion. Numerical precision control of the Hadamard product operation employs Kahan compensated summation to reduce accumulated rounding errors.

[0037] Principal component analysis is used for dimensionality reduction of the iterative state vector, retaining 95% of the variance in the component dimensions. Backup and recovery of the corrected weight list utilizes a multi-site active-active architecture, achieving 99.99% data reliability. Pattern recognition of the direction of difference changes employs a convolutional neural network with four convolutional layers. Anomaly detection of the magnitude of difference changes uses the isolated forest algorithm, with an anomaly score threshold set at 0.65. Frequency domain analysis of the tendency of difference changes uses Fast Fourier Transform to identify periodic fluctuation components. Distributed computation of corrected weight parameters uses the MapReduce framework, processing grid partitions during the mapping phase. Pathological diagnosis of the weight coefficient matrix uses condition number monitoring; regularization is triggered when the condition number exceeds 1E10. Low-rank approximation of the difference matrix uses a random projection method, with approximation error controlled within 5%. Energy consumption optimization for Hadamard product operations uses dynamic voltage and frequency adjustment, regulating power consumption based on computational load. Fault-tolerant design for matrix addition operations employs triple module redundancy, ensuring that single-point failures do not affect system operation.

[0038] See Figure 4In the parameter iterative correction process of the concrete internal quality detection method in the complex environment of water diversion tunnels, the correction effects of sound velocity parameters and attenuation coefficients are comprehensively displayed through the time-series variation curves of the absolute difference values. Difference quantification is achieved by calculating the absolute values ​​of the sound velocity difference and the attenuation coefficient difference. Before correction, the peak difference in the sound velocity parameter reached 180 m / s (cycle 3), which decreased to 130 m / s after correction. Before correction, the difference in the attenuation coefficient fluctuated between 0.2 and 1.0 dB / m, and after correction, the multi-cycle error was less than 0.1 dB / m. The correction algorithm is driven by the difference parameters, which are obtained through vector subtraction of the actual quality characteristic parameters and the initial quality characteristic parameters. The correction weight parameters are then applied for Hadamard product scaling to generate the correction matrix. Iterative weight adjustment relies on the dynamic query of the iterative state vector. The vector construction includes normalization processing of the direction, amplitude, and tendency of difference changes, ensuring that the difference decreases by more than 30% in key cycles (such as cycles 8 and 14) after correction, highlighting the robustness of the parameter update model in complex environments. In the parameter configuration, the tolerance limit is set as the difference threshold, the iteration termination condition is that the difference is less than the limit, and the mesh generation and weight assembly adopt spatial interpolation optimization.

[0039] Example 3: After obtaining the calibrated quality characteristic parameters, the method further implements a solution for each environmental scenario. Combining the corresponding initial and calibrated quality characteristic parameters, the method calculates the quality fitting coefficient parameters for each environmental scenario. These coefficients characterize the transformation relationship from the initial state to the calibrated state. Based on each environmental scenario and its quality fitting coefficient parameters, an environmental quality mapping model is created, establishing a correspondence between environmental conditions and parameter transformation laws. The step of modifying the calibrated quality characteristic parameters using quality difference parameters includes retrieving the quality fitting coefficient parameters corresponding to the target state through the environmental quality mapping model. The retrieval process is based on environmental feature vector matching. The quality fitting coefficient parameters are used to correct the quality difference parameters. The correction operation maps the quality difference parameters to the transformation space, obtaining the correction amount for the calibrated quality characteristic parameters. The correction amount is applied to modify the calibrated quality characteristic parameters, maintaining the integrity of the physical meaning of the parameters, resulting in the modified quality characteristic parameters. The environmental scenario is described using a multi-dimensional feature vector form, with vector elements containing three basic environmental indicators: temperature reading, humidity percentage, and surrounding rock pressure value. The quality fit coefficients are calculated using a multiple linear regression method, with the regression model considering the nonlinear interaction between environmental factors and parameter changes. The environmental quality mapping model is stored using a hierarchical index structure: the first level indexes the environment type, and the second level indexes the specific parameter categories. The correction for quality difference parameters involves Jacobian matrix operations, with matrix elements describing the coupling relationships between the various quality parameters. The formula for calculating the quality fit coefficients is expressed as follows: ,in: This represents the vector of quality fit coefficients. Represents the environmental scene feature matrix. This represents the weight diagonal matrix. Represents the regularization coefficient. Represents the identity matrix. This represents the vector of quality characteristic parameters after calibration. The coefficients in this formula are solved using the regularized least squares method to ensure numerical stability.

[0040] The query interface of the environmental quality mapping model implements a nearest neighbor search algorithm, with the search radius adaptively adjusted according to the dimensions of the environmental parameters. The quality fitting coefficient parameters are validated using leave-one-out cross-validation, with each environmental scenario used sequentially as a test set to verify the model's generalization ability. The calculation of correction values ​​includes boundary constraint handling, ensuring the correction magnitude does not exceed the allowable fluctuation range of the parameters. The physical rationality check of the changed quality characteristic parameters is achieved through a material constitutive model; results violating physical laws trigger recalculation. Preprocessing of the environmental scenario feature matrix includes outlier removal, using box plots to identify data points deviating more than 1.5 times the interquartile range. The construction of the weighted diagonal matrix is ​​based on measurement accuracy indicators, with larger weight values ​​set for high-precision sensors. The regularization coefficient is determined using the L-curve method to balance goodness of fit and model complexity. The dimension of the identity matrix is ​​consistent with the number of columns in the environmental scenario feature matrix to maintain dimensionality matching for matrix operations. The standardization of the calibrated quality characteristic parameter vector uses the z-score method to ensure all parameters are of the same order of magnitude. Sparsity reduction of the quality fitting coefficient parameter vector uses LASSO regression to automatically filter significant environmental influencing factors. Collinearity diagnosis of the environmental scene feature matrix employs a variance inflation factor, with features having a factor greater than 10 undergoing principal component transformation. The dynamic update of the weight diagonal matrix is ​​based on the sensor calibration cycle, with new calibration data triggering weight recalculation. Adaptive adjustment of the regularization coefficient is based on condition number monitoring, increasing the regularization strength when the matrix is ​​ill-conditioned. An extended version of the identity matrix includes off-diagonal elements, representing prior correlations between parameters. Smoothing of the calibrated quality feature parameter vector uses a Savitzky-Golay filter to preserve parameter change trends. Incremental learning of the environmental quality mapping model employs an online sequential least squares algorithm, incrementally updating model parameters upon the arrival of new environmental data. Uncertainty quantification of the quality fitting coefficient parameter vector uses Bayesian linear regression, outputting the posterior probability distribution of the parameters. The direction verification of the correction amount is performed using a gradient check method, ensuring the correction direction aligns with the convergence direction of the target state. Iterative optimization of the changed quality feature parameters uses a trust domain method, evaluating the degree of improvement in the objective function after each change.

[0041] The dimensionality reduction of the environmental scene feature matrix employs kernel principal component analysis, nonlinearly mapping to a high-dimensional feature space before extracting principal components. Constraint optimization of the weight diagonal matrix uses the Lagrange multiplier method, satisfying the normalization condition that the sum of weights equals 1. The grid search for regularization coefficients uses five-fold cross-validation, testing optimal values ​​in the range of 0.001 to 1000 on a logarithmic scale. An alternative to the identity matrix is ​​a diagonal weight matrix, with diagonal elements reflecting the relative importance of each environmental parameter. Missing values ​​in the calibrated quality feature parameter vector are handled using multiple imputation, generating multiple complete datasets for joint analysis. Regularization path analysis of the quality fitting coefficient parameter vector uses coordinate descent, plotting the trajectory of coefficient values ​​as regularization intensity changes. Feature engineering of the environmental scene feature matrix introduces polynomial expansion, generating higher-order interaction terms for environmental parameters. Robust estimation of the weight diagonal matrix uses the Huber loss function to reduce the impact of outlier measurements on weight allocation. Optimization of the regularization coefficients employs a Bayesian optimization framework, with a Gaussian process surrogate model guiding the parameter search. The generalized form of the identity matrix includes a block diagonal structure, and different parameter categories use independent regularization strengths. Outlier detection of the calibrated quality feature parameter vector employs a local anomaly factor algorithm to identify data points with anomalous density. Distributed training of the environmental quality mapping model utilizes a parameter server architecture, with worker nodes processing different environmental scenario subsets in parallel. Interpretive analysis of the quality fitting coefficient parameter vector uses the SHAP value method to quantify the contribution of each environmental feature to the coefficients. Step size control of the correction amount employs a line search algorithm, satisfying the Armijo condition to guarantee convergence. Sensitivity analysis of the changed quality feature parameters uses the Morris method to assess the impact of parameter changes on the final quality assessment.

[0042] The streaming processing of the environmental scene feature matrix employs a sliding window model, with the window size dynamically adjusted according to the rate of environmental change. Spatiotemporal modeling of the weight diagonal matrix uses Kriging interpolation, incorporating spatial correlation to improve weight allocation. Online learning of the regularization coefficients utilizes stochastic gradient descent, updating coefficient values ​​along the negative gradient direction in each iteration. The sparse approximation of the identity matrix employs a stochastic projection method to reduce the storage overhead of large-scale matrix operations. Distributed storage of the calibrated quality feature parameter vectors uses a consistent hashing algorithm, with data shards stored across multiple computing nodes. Constraint optimization of the quality fitting coefficient parameter vectors uses the projective gradient method, constraining coefficient values ​​within a physically feasible parameter space. Automatic encoding of the environmental scene feature matrix employs a variational autoencoder to learn a low-dimensional representation of the environmental data. Multi-objective optimization of the weight diagonal matrix uses the Pareto front method to balance fitting accuracy and model simplicity. Model selection for the regularization coefficients adopts the Akaike information criterion, comprehensively considering the likelihood function value and the number of parameters. Perturbation analysis of the identity matrix uses condition number evaluation, ensuring the impact of small perturbations on the solution is controllable. Time series analysis of the calibrated quality characteristic parameter vectors employs a state-space model, separating trend and periodic terms. The environmental quality mapping model is deployed using a microservice architecture, with a RESTful interface receiving environmental parameters and returning quality fitting coefficients. Version management of the quality fitting coefficient parameter vectors utilizes a Git pipeline, generating new version tags with each model update. Parallel computation of correction values ​​employs CUDA kernel functions, with GPU acceleration for the correction process of large-scale parameter sets. Persistent storage of changed quality characteristic parameters uses a time-series database, supporting rapid retrieval of historical parameters by time range.

[0043] Privacy protection for the environmental scene feature matrix employs differential privacy technology, adding Laplace noise to protect sensitive environmental information. Federated learning of the weight diagonal matrix uses a coordinated averaging algorithm, with multiple detection terminals training collaboratively without sharing raw data. Automatic differentiation of the regularization coefficients utilizes a computational graph model, supporting higher-order derivative calculations for optimization. Symbolic computation of the identity matrix employs a computer algebra system, accurately handling rational number operations to avoid floating-point errors. Visualization of the calibrated quality feature parameter vector uses a parallel coordinate graph, projecting the multi-dimensional parameter space onto a two-dimensional plane. Transmission compression of the quality fitting coefficient parameter vector uses sparse coding technology, storing only non-zero coefficient values ​​and position indices. Incremental updates of the environmental scene feature matrix use a rank-one correction formula to avoid repetitive calculations of the entire matrix, improving efficiency. The fault-tolerant design of the weight diagonal matrix employs a replication and backup mechanism, automatically switching to a backup matrix when the primary matrix is ​​damaged. Multi-scale optimization of the regularization coefficients uses simulated annealing to escape local optima and find the global optimum. Memory mapping of the identity matrix uses a paging storage strategy to avoid memory overflow issues during large matrix operations. The quality assessment of the calibrated quality characteristic parameter vector is performed using signal-to-noise ratio calculation, and low-quality data triggers a re-acquisition process.

[0044] Example 4: To obtain the internal response information of concrete, ultrasonic detection technology is used to project sound wave signals into the concrete. This technology uses a piezoelectric ceramic transducer to generate pulse waves with a frequency range of 50kHz to 200kHz. The sound wave transmission duration and signal attenuation are recorded. The sound wave transmission duration is measured using a high-speed digital acquisition card at a sampling rate of 100MHz to record the transmission and reception timestamps. The signal attenuation is calculated in decibels by comparing the voltage amplitudes at the transmitting and receiving ends. Based on the sound wave transmission duration and signal attenuation, sound velocity distribution images and attenuation distribution images are created. The sound velocity distribution image is generated using a back-projection algorithm to reconstruct the two-dimensional velocity field, and the attenuation distribution image is generated using algebraic reconstruction technology to calculate the energy absorption coefficient distribution. The sound velocity distribution images and attenuation distribution images are fused to form the internal response information. The fusion process uses wavelet transform multi-resolution analysis to decompose the two images into different frequency bands before weighted fusion. When identifying the difference parameters in the current iteration cycle, the numerical difference between the actual quality characteristic parameters and the initial quality characteristic parameters is calculated. The numerical difference calculation performs differential operations on the sound velocity value and attenuation coefficient for each grid cell. The numerical differences are standardized using the Z-score method to convert the differences into a distribution with a mean of 0 and a standard deviation of 1. Difference parameters are then generated based on the standardized differences, and these parameters, combined with the differences in sound velocity and attenuation, form a two-dimensional feature vector.

[0045] The ultrasonic transducers are arranged in a 32-element linear array with a 25mm element spacing, covering an 800mm detection width. An alternating triggering mechanism is used for sound wave transmission, with odd-numbered transducers transmitting and even-numbered transducers receiving, reducing crosstalk by switching roles. Transmission duration is measured using a time-to-digital converter chip, with a time resolution of 100 picoseconds corresponding to a sound velocity measurement accuracy of 0.1 m / s. Signal attenuation is calculated using a peak detection algorithm, identifying the maximum value of the received signal envelope and converting it to decibels. Sound velocity distribution image reconstruction employs synchronous iterative reconstruction technology, converging to a stable solution after 10 iterations. Attenuation distribution image correction considers geometric diffusion compensation, using the inverse square law of distance to correct propagation path loss. Image fusion weight allocation is based on signal-to-noise ratio evaluation, with a weight of 0.6 for the sound velocity image and 0.4 for the attenuation image. Standardized parameters are derived from a historical statistical database, with the mean and variance updated every 24 hours. The magnitude of the difference vector is calculated using the Euclidean norm, and the magnitude value is used as an evaluation index for quality uniformity. The transducer array's sealed design meets IP68 protection standards, enabling continuous operation in environments up to 30 meters underwater. The acoustic wave transmission circuit employs high-voltage pulse excitation with a 400V pulse voltage and a 5-microsecond pulse width. Signal conditioning for the received signal includes a bandpass filter, with the passband frequency matched to the transducer's center frequency. Time measurement calibration utilizes a standard delay line, automatically calibrating the system delay upon daily power-on. The attenuation calculation dynamic range reaches 80dB, meeting the detection requirements from intact concrete to severely defective concrete.

[0046] The image reconstruction mesh generation employs an adaptive refinement strategy, with the mesh in defect areas refined to 5mm × 5mm. Color mapping of the fused image uses the HSB color space, mapping sound velocity to hue attenuation to brightness. Threshold settings for difference parameters are based on probability distribution, with differences exceeding three standard deviations marked as significant. A specialized coupling agent is used for the coupling between the transducer and concrete, achieving an acoustic impedance matching coefficient exceeding 0.95. Gray code encoding is used for emission sequence optimization, improving the signal-to-noise ratio by more than 3dB. A PT1000 temperature sensor is used for time measurement temperature compensation, with a compensation coefficient of 0.17 m / s / ℃. Multi-frequency scanning is used for attenuation measurement frequency compensation, extracting the frequency-dependent attenuation slope. Regularization parameters for image reconstruction are determined using the L-curve method, balancing image smoothness and detail preservation. Multi-scale analysis of the fused image employs pyramid decomposition, with different fusion rules applied to different scales. The DBSCAN algorithm is used for spatial clustering of difference parameters to identify continuous abnormal regions. The mechanical scanning of the transducer array is driven by a servo motor, achieving a positioning accuracy of 0.1mm. The acoustic wave emission safety protection includes voltage monitoring, automatically cutting off power in case of overvoltage. Digital processing of the received signal is implemented using an FPGA, processing 32 channels of data in parallel. Time measurement interpolation uses cubic spline interpolation, improving the resolution to 10 picoseconds.

[0047] Temperature correction for attenuation measurement employs an empirical formula, with the correction coefficient adjusted non-linearly with temperature. The initial model for image reconstruction uses a homogeneous medium assumption to accelerate the iterative convergence process. Quality assessment of the fused image uses a structural similarity index to ensure preservation of original features after fusion. Temporal analysis of difference parameters uses a sliding window method with a window width of 10 detection cycles. Transducer array calibration uses standard test blocks with a sound velocity calibration error of less than 0.5%. Hanning window modulation is used for transmitted signal waveform optimization to reduce spectral leakage. Adaptive filtering is used for received signal noise reduction, with the reference signal coming from a receiver far from the defect area. Phase-locked loop technology is used to eliminate jitter in time measurement, suppressing clock source phase noise. Attenuation measurement scattering correction is based on Mie scattering theory, considering the influence of aggregate particle size distribution. Mirror expansion is used for boundary processing in image reconstruction to reduce boundary artifacts. The fused image is compressed and stored in JPEG2000 format, achieving a peak signal-to-noise ratio greater than 40 dB at a compression ratio of 20:1. Visualization of difference parameters uses a heatmap overlay, with red tables representing...

[0048] Positive differences are indicated by blue, while negative differences are indicated by blue. Refer to Table 1, which shows the typical range of values ​​for ultrasonic testing parameters.

[0049] Table 1: Specification Table of Ultrasonic Testing Parameters

[0050]

[0051] The transducer array beamforming employs a delay-summing algorithm to achieve electronic scanning. Acoustic wave emission energy control uses pulse width modulation (PWM) to adapt to the detection of concrete of varying strengths. The received signal time gate is adjustable, with a programmable gate width from 1 microsecond to 50 microseconds. The convergence criterion for image reconstruction is based on the residual norm; iteration stops when the residual is less than 1e-6. Registration of the fused images uses feature point matching to eliminate the influence of mechanical positioning errors. Spatial interpolation of the difference parameters uses the Kriging method to generate a continuous difference distribution field. Time-measured temperature difference compensation uses a thermocouple array to measure the temperature distribution on the concrete surface. Attenuation measurement humidity compensation is based on a relative humidity sensor, with an attenuation correction of 0.5 dB for every 10% increase in humidity. The model constraints for image reconstruction include prior information, with known rebar locations set as fixed sound velocity values. The fused image segmentation uses a region growing method to separate regions of different quality levels. Statistical analysis of the difference parameters uses a variogram to calculate the spatial correlation range. Fault diagnosis of the transducer array uses impedance analysis, with automatic isolation of abnormal transducers. Acoustic wave emission encoding uses Barker code sequences to improve anti-interference capabilities. The received signal spectrum analysis employs Fast Fourier Transform (FFT) to identify the material's frequency response characteristics. Time-measured temperature gradient correction utilizes 3D thermal imaging data to establish a temperature field correction model. Attenuation measurement stress compensation is based on strain gauge data, considering the impact of stress on acoustic attenuation. Multimodal fusion in image reconstruction incorporates electromagnetic wave data; super-resolution reconstruction enhances the resolution of the fused image, reducing pixel size to one-quarter of the original. Machine learning classification of difference parameters uses Support Vector Machines (SVM) to automatically identify defect types. Intelligent scheduling of the transducer array employs reinforcement learning to optimize the detection path and improve efficiency. Adaptive adjustment of the acoustic wave emission frequency is based on the concrete age, with low-frequency detection used for early-stage concrete. Wavelet denoising of the received signal uses the sym8 wavelet basis to preserve defect reflection characteristics. Dynamic calibration for time measurement uses a reference channel to correct system delay drift in real time. Nonlinear detection for attenuation measurement employs harmonic analysis to identify microscopic damage accumulation. GPU acceleration for image reconstruction utilizes CUDA programming, reducing reconstruction time. Deep learning recognition of the fused image uses convolutional neural networks, improving defect identification accuracy. Prediction of difference parameters uses time series analysis to provide early warning of quality degradation trends.

[0052] Example 5: The construction process of the corrected weight list involves iterative operations on several experimental samples. The experimental samples are standard C30 strength grade concrete test blocks with a size of 150mm×150mm×150mm. The iterative state vector and optimal corrected weight are recorded for each detection location, corresponding to a 5mm×5mm grid cell on the surface of the test block. The mapping relationship between the iterative state vector and the optimal corrected weight is learned through a neural network algorithm. The neural network algorithm adopts a three-layer fully connected structure containing 128 hidden layer neurons. The mapping relationship is saved as a corrected weight list, which is stored in binary file format for easy loading. When solving for the mass fitting coefficient parameters under each environmental scenario, the initial mass feature parameters and the calibrated mass feature parameters are fed into a multivariate regression model for each environmental scenario. The multivariate regression model considers the interaction terms of environmental temperature, humidity, and pressure parameters. The gradient descent method is used to optimize the coefficients, with the learning rate set to 0.01 and the momentum coefficient set to 0.9. The mass fitting coefficient parameters are then output. The experimental samples are prepared under standard curing conditions: temperature 20±2 degrees Celsius and humidity above 95% for 28 days. The iterative state vector was acquired using an ultrasonic scanner at a frequency of 100 kHz and a step size of 1 mm. The optimal correction weights were determined using a grid search method, with weight values ​​ranging from 0.1 to 1.0 and a step size of 0.05. The neural network algorithm was trained using the Adam optimizer, with a batch size of 256 and 1000 training cycles. The mapping relationship was validated using 10-fold cross-validation, achieving an average coefficient of determination greater than 0.93. The input features of the multiple regression model included temperature values, temperature gradients, and historical temperature change rates. The iteration stopping condition for the gradient descent method was that the change in the loss function was less than 1e-6, and the loss function was expressed as mean squared error.

[0053] The experimental samples exhibited diversity, including various water-cement ratios ranging from 0.35 to 0.55, covering commonly used ratios. Principal component analysis was used for dimensionality compression of the iterative state vectors, retaining a feature subset that retained 95% of the original information. Simulated annealing was used to optimize the optimal corrected weights, with an initial temperature set to 1000 and a cooling coefficient of 0.95. The ReLU activation function was used in the neural network algorithm, and the Sigmoid function was used in the output layer to restrict the weights to the range of 0-1. Online learning was employed to update the mapping relationship, incrementally updating the network weights as new samples arrived. Environmental scene classification was based on the K-means clustering algorithm, dividing the temperature-humidity-pressure three-dimensional space into eight typical scenes. Minimum-maximum normalization was used to standardize the initial quality feature parameters, mapping each parameter to the [0,1] interval. Anomaly detection of the calibrated quality feature parameters was performed using the isolated forest algorithm, removing data points that deviated from the main distribution. Regularization of the multivariate regression model employed an elastic network penalty term to balance the strengths of L1 and L2 regularization. The gradient descent method is implemented in parallel using a data-parallel strategy, updating model parameters synchronously across multiple GPUs. Defect prefabrication of concrete test blocks is achieved through manual drilling, with hole diameters ranging from 2mm to 10mm to simulate defects of different scales. The timestamps of the iterative state vectors are recorded to millisecond accuracy and stored synchronously with the ultrasonic waveform data. Validation of the optimal corrected weights is achieved through physical model simulation, comparing numerical simulation results with actual measurement results. The dropout rate of the neural network algorithm is set to 0.2 to prevent overfitting. The visualization of the mapping relationship employs the t-SNE method, projecting the high-dimensional weight space onto a two-dimensional plane.

[0054] The boundary processing of environmental scenes employs a support vector machine classifier to clearly define the boundaries of each scene. Gaussian white noise is added to the initial quality feature parameters, with a signal-to-noise ratio of 30dB to simulate actual measurement conditions. After calibration, the quality feature parameters are smoothed using a moving average filter with a window width of 5 data points. Forward selection is used for interaction term selection in the multivariate regression model, gradually introducing significant interaction terms. The learning rate decay in the gradient descent method employs an exponential decay strategy, halving the learning rate every 100 iterations. Temperature cycling of the experimental samples is controlled using an environmental chamber, with a temperature range of -10℃ to 50℃ simulating seasonal conditions. A columnar database is used to store the iterative state vectors, improving time series query efficiency. The interval estimation of the optimal corrected weights uses the Bootstrap method to generate 95% confidence intervals. Batch normalization layers in the neural network algorithm are inserted before each hidden layer to accelerate training convergence. Compressed transmission of mapping relationships uses differential encoding, transmitting only weight changes to reduce bandwidth consumption. Physical constraints on the quality fitting coefficient parameters include positive definiteness of sound velocity, forcing the sound velocity coefficient to be positive to conform to physical laws. Fuzzy membership functions are used in the transition regions of the environmental scene to handle the uncertainty of the scene boundaries. Missing values ​​of the initial quality feature parameters are imputed using multiple imputation, generating five complete datasets for joint analysis. The uncertainty propagation of the calibrated quality feature parameters is assessed using the Monte Carlo method to evaluate the variability of the coefficient estimates. The variance inflation factor of the multiple regression model is monitored, and features with factors greater than 10 undergo principal component transformation. Age-related changes in concrete test blocks are observed over a 90-day long-term period to record the entire strength development process. Frequency domain feature extraction of the iterative state vector uses Fast Fourier Transform to identify characteristic frequency components. Sensitivity analysis of the optimal corrected weights uses the Sobol exponent to quantify the influence of each input feature on the weights. The early stopping mechanism of the neural network algorithm is monitored using a validation set; training terminates when the loss function does not decrease for 10 consecutive iterations. Version control of the mapping relationships is managed using a Git system to record the update log of each weight list.

[0055] The dynamic switching of environmental scenes employs a Hidden Markov Model (HMM) to predict scene transition probabilities. The spatiotemporal correlation of initial quality feature parameters is modeled using a covariance function to model the spatial correlation structure. Bayesian updates of calibrated quality feature parameters utilize conjugate prior distributions, incorporating historical prior information to improve estimation accuracy. Heteroscedasticity handling in the multivariate regression model employs weighted least squares, with weights inversely proportional to error variance. Gradient clipping in gradient descent is set to a threshold of 1.0 to prevent gradient explosion. Damage evolution of experimental samples is simulated using a fatigue loading device with a cyclic loading frequency of 5Hz to mimic long-term usage conditions. Iterative manifold learning of state vectors employs an isometric mapping algorithm to discover low-dimensional embeddings in high-dimensional data. Multi-objective optimization of optimal corrected weights uses the NSGA-II algorithm to balance convergence speed and weight stability. The attention mechanism of the neural network algorithm incorporates a self-attention layer to highlight the influence of important feature dimensions. Distributed storage of mapping relationships uses the IPFS protocol to achieve decentralized data sharing. Real-time environmental scene recognition employs a random forest classifier, inputting current sensor data and outputting scene labels. The initial inter-device calibration of mass characteristic parameters employs the transfer standard method to eliminate system measurement bias. Long-term drift correction of calibrated mass characteristic parameters utilizes linear regression to fit the parameter trends over time. The robustness of the multivariate regression model is tested using Cook's distance to eliminate the influence of high-leverage points. A Nesterov accelerated version of the gradient descent method is applied, with the gradient descent direction pre-calculated to improve convergence speed.

[0056] CT scanning was used for microstructural analysis of concrete test blocks to verify the accuracy of defect detection. The UMAP method was used for dimensionality reduction visualization of iterative state vectors to preserve local topological features. A pre-trained model was used for the transfer learning of optimal corrected weights, enabling rapid adaptation of weight mappings to new engineering sites. Knowledge distillation of the neural network algorithm employed a teacher-student network structure to compress the model size for easy embedded deployment. The AES-256 algorithm was used for encryption protection of mapping relationships to prevent unauthorized access to weight data. A time-series prediction model was used for environmental scene forecasting, predicting scene changes one hour in advance. The Siward theorem was used for quality control of initial quality feature parameters to identify abnormal measurements. Dempster-Shafer theory was used for multi-sensor fusion of calibrated quality feature parameters, merging confidence scores from different sources. Ridge regression was used to address multicollinearity in the multiple regression model, adding diagonal elements to improve matrix ill-conditionedness. The AdaGrad algorithm was used for adaptive learning rate adjustment in gradient descent, adjusting the learning step size based on parameter gradient history. Field validation of experimental samples was performed using actual tunnel engineering inspections, comparing the detection results of traditional and new methods. Real-time calculation of the iterative state vector utilizes edge computing devices to reduce cloud transmission latency. Online updates of the optimal correction weights employ a sliding window mechanism, dynamically updating the most recent 1000 data sets. Hardware acceleration of the neural network algorithm utilizes a dedicated AI chip, increasing inference speed by more than 10 times. The fault-tolerance mechanism for the mapping relationship employs triple module redundancy, ensuring that single-point failures do not affect system operation. Practical application of the mass fitting coefficient parameters shows that under a pressure of 5 MPa, a mass fitting coefficient parameter of 0.8 corresponds to a pressure compensation coefficient of -0.02 dB / MPa. The environmental scenario is expanded to consider chemical corrosion factors, adding a pH sensor to monitor the corrosiveness of the leaked liquid. The measurement uncertainty of the initial mass characteristic parameters is assessed using the GUM method, providing an expanded uncertainty report. The reliability assessment of the calibrated mass characteristic parameters uses survival analysis to calculate the effective lifetime distribution of the parameters. LASSO regression is used for variable selection in the multivariate regression model, automatically screening important environmental variables. The second-order optimization of the gradient descent method employs a quasi-Newton method, using an approximate Hessian matrix to accelerate convergence.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the internal quality of concrete in the complex environment of a water diversion tunnel, characterized in that, The method performs the following operations: under multiple environmental scenarios, the internal quality of concrete is calibrated for uniformity based on the initial quality characteristic parameters corresponding to each environmental scenario, so as to obtain the calibrated quality characteristic parameters of concrete under each environmental scenario; the target state that the internal quality of concrete needs to reach is set, and the detection device is operated to perform operations based on the calibrated quality characteristic parameters corresponding to the target state, while the actual quality state of concrete is collected through the sensing device. If the difference between the target state and the real state reaches or exceeds the predetermined tolerance limit, the quality difference parameter is derived by using the calibrated quality characteristic parameter corresponding to the target state and the quality characteristic parameter corresponding to the real state; the calibrated quality characteristic parameter corresponding to the target state is changed with the help of the quality difference parameter to generate the changed quality characteristic parameter corresponding to the target state. Treat the modified quality characteristic parameters as the new calibrated quality characteristic parameters corresponding to the target state, and re-execute the operation of manipulating the detection device until the difference between the target state and the actual state is less than the predetermined tolerance limit. The process of calibrating the internal quality uniformity of concrete specifically involves: using initial quality characteristic parameters to guide the detection device to scan the concrete and obtain its internal response information; extracting the true quality state parameters from the internal response information; calculating the quality uniformity measure based on the true quality state parameters; and transforming the true quality state parameters into true quality characteristic parameters according to a pre-constructed environmental quality correlation model, whereby the environmental quality correlation model expresses the relationship between the environmental scenario and the quality characteristic parameters. The parameter update model is used to iteratively process the real quality characteristic parameters to generate iterated quality characteristic parameters. The iterated quality characteristic parameters are used as the starting quality characteristic parameters, and the scanning operation is performed again until the quality uniformity measure is lower than the preset uniformity limit. The iterated quality characteristic parameters are then identified as calibrated quality characteristic parameters.

2. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 1, characterized in that, When using a parameter update model for iterative processing, the following actions are performed: compare the actual quality characteristic parameters with the initial quality characteristic parameters to find the difference parameters in the current iteration cycle; obtain the corresponding correction weight parameters for the current iteration cycle, use the correction weight parameters to scale the difference parameters, and generate a correction amount for the initial quality characteristic parameters; apply the correction amount to the initial quality characteristic parameters to realize the quality characteristic parameters after iteration.

3. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 2, characterized in that, The corrected weight parameters for the current iteration cycle are obtained by summarizing the direction, magnitude, and tendency of difference changes at all detection locations within the current iteration cycle; and constructing the iteration state vector for each detection location based on the direction, magnitude, and tendency of difference changes at each detection location. Using the iterative state vector as the search key, the corresponding correction weight is found in the pre-prepared list of correction weights; Based on the correction weights at each detection location, assemble the correction weight parameters for the current iteration cycle.

4. The method for detecting the internal quality of concrete in the complex environment of a water diversion tunnel according to claim 1, characterized in that, After obtaining the calibrated quality characteristic parameters, the method is further implemented as follows: for each environmental scenario, the quality fitting coefficient parameters under each environmental scenario are solved by combining the corresponding initial quality characteristic parameters and the calibrated quality characteristic parameters; based on each environmental scenario and its quality fitting coefficient parameters, an environmental quality mapping model is created, wherein the environmental quality mapping model defines the correspondence between environmental scenarios and quality fitting coefficient parameters.

5. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 4, characterized in that, The steps for modifying calibrated quality characteristic parameters using quality difference parameters include: retrieving the quality fitting coefficient parameter corresponding to the target state through an environmental quality mapping model; using the quality fitting coefficient parameter to correct the quality difference parameter to obtain the correction amount for the calibrated quality characteristic parameter; and applying the correction amount to modify the calibrated quality characteristic parameter to obtain the modified quality characteristic parameter.

6. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 1, characterized in that, To obtain the internal response information of concrete, ultrasonic detection technology is used to project sound wave signals into the concrete and record the sound wave transmission time and the degree of signal attenuation. Based on the sound wave transmission time and the degree of signal attenuation, sound velocity distribution images and attenuation distribution images are created. The sound velocity distribution images and attenuation distribution images are fused to form the internal response information.

7. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 2, characterized in that, When identifying the difference parameters for the current iteration cycle, calculate the numerical difference between the actual quality characteristic parameters and the initial quality characteristic parameters; standardize the numerical difference to obtain the normed difference; and form the difference parameters based on the normed difference.

8. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 3, characterized in that, The process of constructing the corrected weight list is as follows: iterative operations are carried out on several experimental samples, and the iterative state vector and the optimal corrected weight at each detection position are recorded. The mapping relationship between the iterative state vector and the optimal corrected weights is learned through a neural network algorithm. Save the mapping relationship as a list of corrected weights.

9. The method for detecting the internal quality of concrete in a complex environment of a water diversion tunnel according to claim 4, characterized in that, When solving for the quality fitting coefficient parameters under each environmental scenario, for each environmental scenario, the initial quality feature parameters and the calibrated quality feature parameters are fed into the multivariate regression model; the gradient descent method is used to optimize the coefficients, and the quality fitting coefficient parameters are output.

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