A tunnel concrete detection and analysis method and system

Abstract

The present invention relates to the field of civil engineering technology, and specifically to a tunnel concrete detection and analysis method and system, comprising the following steps: based on an image acquisition device, concrete image data of multiple areas of the tunnel are collected and processed, the location and distribution of cracks are identified, and crack location identification results are obtained. In the present invention, image acquisition parameters are adjusted by ambient lighting, and the crack location is accurately identified in combination with edge detection technology, thereby improving the accuracy and efficiency of crack detection, and crack expansion path prediction and structural stability analysis are performed through geometric feature analysis and classification of cracks. The complexity of cracks is evaluated using fractal dimensions to improve the ability to evaluate the impact on tunnel structural stability, and the corrosion process of concrete is accurately identified through data provided by chemical sensors and analysis of concrete structural damage, providing data support for maintenance decisions, and evaluating the durability of concrete in combination with environmental impacts, thereby improving the foresight and effectiveness of tunnel safety management.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering, and in particular to a tunnel concrete detection and analysis method and system. Background Art

[0002] The field of civil engineering technology focuses on the design, construction, supervision, operation and maintenance of public and private infrastructure, including the construction of roads, bridges, tunnels, dams and buildings. Through the physical and functional design of structures, combined with materials science, geography, environmental science and mechanical engineering, environmental protection and sustainability are considered to ensure the safety and economy of construction structures. It includes multiple aspects of structural engineering, geological engineering, environmental engineering and transportation engineering, and uses computer-aided design, geographic information systems and structural health monitoring to optimize design, improve material performance, ensure safety standards, and improve construction efficiency and safety.

[0003] Among them, the tunnel concrete detection and analysis method focuses on evaluating and ensuring the safety and durability of tunnel concrete structures. By applying a variety of detection technologies, including acoustic wave detection, electromagnetic wave detection and chemical analysis, it identifies damage, cracks or corrosion problems in tunnel concrete. It aims to detect problems in advance and intervene in time, reduce large-scale structural damage, extend the service life of the tunnel, and ensure safety during use. It is crucial to maintaining the reliability of transportation infrastructure and avoiding traffic interruptions and accidents caused by tunnel failures.

[0004] Traditional tunnel concrete inspection and analysis methods have the problems of low detection accuracy, inability to provide crack geometric characteristics and expansion predictions, and inability to accurately measure the depth and complexity of cracks, which leads to overestimation or underestimation of the actual safety status of the tunnel. Complex on-site operations and highly skilled operators are required, which increases inspection costs and time. There is a lack of efficient data analysis and data processing tools, and it is difficult to extract effective information from batch data, which limits the application efficiency of inspection results, leading to delays in tunnel maintenance decisions, increased maintenance costs, and safety accidents due to failure to promptly discover serious structural problems, which affects the reliability of the transportation system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a tunnel concrete detection and analysis method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a tunnel concrete detection and analysis method, comprising the following steps:

[0007] S1: Based on the image acquisition equipment, collect and process the concrete image data of multiple areas of the tunnel, identify the location and distribution of cracks, and obtain the crack location identification results;

[0008] S2: Based on the crack position recognition result, analyzing the image data of multiple concrete cracks, identifying the geometric features of the cracks, classifying the cracks, and generating a crack type classification list;

[0009] S3: According to the crack type classification list, identify and track the paths of multiple cracks in the tunnel, analyze and predict the expansion direction of the cracks, and obtain crack path prediction results;

[0010] S4: Based on the crack path prediction result, by calculating the fractal dimension of the crack pattern, evaluating the complexity of the cracks, analyzing the impact on the tunnel structure stability, and generating structural stability analysis data;

[0011] S5: Based on the structural stability analysis data, a chemical sensor is used to collect pH value and chloride ion concentration data of the concrete, and the relationship between the target chemical data and concrete structural damage is analyzed to identify the corrosion process of the concrete and obtain a corrosion process identification result;

[0012] S6: Based on the corrosion process identification results, by evaluating the impact of various factors on the durability of concrete, simulating the long-term performance response of concrete, combining with actual monitoring data, analyzing the durability of the concrete structure, and generating tunnel concrete detection results.

[0013] As a further solution of the present invention, the crack position identification result includes a crack coordinate data set, crack length information, and crack direction feature information; the crack type classification list includes a crack width range, a crack depth classification result, and a crack morphology type information; the crack path prediction result includes an expansion direction prediction result, an expansion speed prediction information, and an impact area prediction data; the structural stability analysis data includes a calculation result of a crack fractal dimension, a crack distribution density, and a threat level of cracks to structural integrity; the corrosion process identification result includes a pH level of concrete, a chloride ion concentration level, and corrosion rate assessment data; the tunnel concrete detection result includes a concrete durability score, an influencing factor weight distribution information, and a concrete predicted lifespan.

[0014] As a further solution of the present invention, based on the image acquisition device, concrete image data of multiple areas of the tunnel are collected and processed to identify the location and distribution of cracks. The steps of obtaining the crack location identification result are specifically as follows:

[0015] S101: Based on the image acquisition device, adjust the device parameters according to the ambient light information, including light sensitivity and exposure time, collect concrete image data of multiple areas of the tunnel, adjust the brightness and contrast to optimize the image quality, and generate image data processing results;

[0016] S102: Based on the image data processing result, analyzing and identifying the contour information of multiple cracks in the image, recording the position coordinates of the cracks, and generating crack coordinate information;

[0017] S103: Based on the crack coordinate information, the distribution pattern of the cracks on the concrete surface is analyzed, the length, direction and density of the cracks are evaluated, the uniformity and aggregation trend of the crack distribution are calculated, and a crack position identification result is generated.

[0018] As a further solution of the present invention, based on the crack position recognition result, the image data of multiple concrete cracks are analyzed, the geometric features of the cracks are identified, and the cracks are classified. The steps of generating a crack type classification list are specifically as follows:

[0019] S201: Based on the crack position recognition result and crack image information, analyze and identify the width, length and depth of multiple cracks to generate crack size data;

[0020] S202: Based on the crack size data, by calculating the crack volume and surface roughness, evaluating the three-dimensional characteristics of the crack, and generating crack geometric characteristic data;

[0021] S203: Based on the crack geometric feature data, the cracks are classified according to shape and length information, including surface cracks and structural cracks, and a crack type classification list is generated.

[0022] As a further solution of the present invention, according to the crack type classification list, the paths of multiple cracks in the tunnel are identified and tracked, the expansion directions of the cracks are analyzed and predicted, and the steps of obtaining the crack path prediction results are specifically as follows:

[0023] S301: identifying the start and end coordinates of multiple concrete cracks in the tunnel according to the crack type classification list, analyzing the directionality and length of the multiple cracks, and generating crack direction data;

[0024] S302: Based on the crack direction data, by identifying the expansion paths of multiple cracks in the image, analyzing the change trend of the crack length, measuring the distance and angle changes between key points, and generating expansion path information;

[0025] S303: Based on the expansion path information, combined with the crack starting position, direction, and expansion direction prediction data, the crack expansion area is evaluated, and combined with the crack state and tunnel environmental conditions, a crack path prediction result is generated.

[0026] As a further solution of the present invention, based on the crack path prediction result, by calculating the fractal dimension of the crack pattern, evaluating the complexity of the cracks, analyzing the impact on the tunnel structure stability, the steps of generating structural stability analysis data are specifically as follows:

[0027] S401: Calculating the fractal dimension of the crack pattern according to the crack path prediction result and the geometric information of the crack, quantitatively evaluating the complexity of the crack, and generating crack complexity data;

[0028] S402: Based on the crack complexity data, analyze the nonlinear characteristics and random distribution characteristics of the cracks, evaluate the impact on the stability of the tunnel structure, and generate influencing factor analysis data;

[0029] S403: Analyze the data based on the influencing factors, compare with the engineering safety standards, analyze the risk levels of multiple cracks, and generate structural stability analysis data.

[0030] As a further solution of the present invention, based on the structural stability analysis data, a chemical sensor is used to collect pH value and chloride ion concentration data of concrete, and the relationship between the target chemical data and concrete structural damage is analyzed to identify the corrosion process of concrete. The steps of obtaining the corrosion process identification result are specifically as follows:

[0031] S501: Based on the structural stability analysis data, a chemical sensor is used to collect pH values ​​and chloride ion concentrations of concrete in multiple areas of the tunnel to obtain real-time chemical monitoring data;

[0032] S502: Based on the real-time chemical monitoring data, by analyzing the pH value and chloride ion concentration of concrete samples with various corrosion degrees, calculating the correlation between various chemical index data and concrete structure damage, and generating corrosion damage correlation information;

[0033] S503: According to the corrosion damage association information and various chemical data of the concrete, the current corrosion state of the concrete is evaluated, key corrosion areas and corrosion processes are identified, and a corrosion process identification result is generated.

[0034] As a further solution of the present invention, according to the corrosion process identification result, by evaluating the influence of various factors on the durability of concrete, simulating the long-term performance response of concrete, combining with actual monitoring data, analyzing the durability of concrete structure, and generating tunnel concrete detection results, the specific steps are:

[0035] S601: Based on the corrosion process identification result, by analyzing the corrosion rate and the affected area, evaluating the influence of various environmental factors on concrete corrosion, including temperature and humidity, calculating the weight coefficients of various influencing factors, and generating environmental impact assessment data;

[0036] S602: Based on the environmental impact assessment data, simulate the long-term performance response of concrete, perform durability analysis in combination with actual monitoring data, predict the state change trend of the concrete structure, and generate concrete durability simulation results;

[0037] S603: Based on the concrete durability simulation result, combined with concrete crack data and corrosion conditions, the durability and risk of the concrete structure in the tunnel are evaluated, the remaining life of the concrete is predicted, and the tunnel concrete detection result is generated.

[0038] As a further solution of the present invention, the specific formula for calculating the weight coefficients of the multiple influencing factors is:

[0039] ,

[0040] in, The number representing the total environmental factors, including those considered to affect concrete corrosion, It is the index number of the target environmental factor, which is used to distinguish different environmental factors. and It is the index used for iteration in the sum operation, representing the column number and row number in the judgment matrix, and is used to traverse the elements in the judgment matrix for calculation. is the first Line Elements of the column, representing environmental factors Relative to environmental factors The relative importance of is an element in the judgment matrix, located at Line Columns are used for normalization in the weight calculation formula to ensure that the weight of each factor is reasonably distributed. Representative The weight coefficients of the environmental factors are calculated by the hierarchical analysis method, which reflects the relative importance of the target factor among the considered environmental factors and evaluates and quantifies the influence of various environmental factors on the concrete corrosion process.

[0041] A tunnel concrete detection and analysis system, the tunnel concrete detection and analysis system is used to perform the above tunnel concrete detection and analysis method, the system comprises:

[0042] The crack recognition module is based on the image acquisition device, which collects image data of the concrete surface in the tunnel, identifies the crack location and records the coordinate information, analyzes the geometric characteristics of the crack and classifies the crack to obtain concrete crack classification data;

[0043] The crack tracking module identifies and tracks the paths of multiple cracks in the tunnel based on the concrete crack classification data, analyzes the crack trends and predicts the crack expansion directions, and generates crack trend analysis results;

[0044] The stability assessment module calculates the fractal dimension of the crack pattern based on the crack trend analysis result, analyzes the complexity of the crack and the impact on the stability of the tunnel structure, and generates a crack impact assessment result;

[0045] The corrosion state analysis module uses chemical sensors to monitor the pH value and chloride ion concentration of concrete based on the crack impact assessment results, analyzes the relationship between chemical data and concrete structure damage, identifies the development stage of corrosion, and generates corrosion stage assessment data;

[0046] The durability analysis module evaluates the impact of various environmental factors on the durability of concrete based on the corrosion stage assessment data, analyzes the durability of the concrete structure in combination with the actual monitoring data, and generates tunnel concrete detection results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, image acquisition parameters are adjusted by ambient lighting, and the crack positions are accurately identified in combination with edge detection technology, thereby improving the accuracy and efficiency of crack detection. Crack expansion path prediction and structural stability analysis are performed through analysis and classification of the geometric characteristics of cracks. The complexity of cracks is evaluated using fractal dimensions, thereby improving the ability to assess the impact on tunnel structure stability. The corrosion process of concrete is accurately identified through analysis of data provided by chemical sensors and concrete structure damage, providing data support for maintenance decisions. The durability of concrete is evaluated in combination with environmental impact, thereby improving the foresight and effectiveness of tunnel safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a tunnel concrete detection and analysis method, comprising the following steps:

[0060] S1: Based on the image acquisition equipment, collect and process the concrete image data of multiple areas of the tunnel, identify the location and distribution of cracks, and obtain the crack location identification results;

[0061] S2: Based on the crack position recognition results, the image data of multiple concrete cracks are analyzed to identify the geometric features of the cracks, classify the cracks, and generate a crack type classification list;

[0062] S3: According to the crack type classification list, identify and track the paths of multiple cracks in the tunnel, analyze and predict the expansion direction of the cracks, and obtain the crack path prediction results;

[0063] S4: Based on the crack path prediction results, the fractal dimension of the crack pattern is calculated to evaluate the complexity of the cracks, analyze the impact on the tunnel structure stability, and generate structural stability analysis data;

[0064] S5: Based on the structural stability analysis data, the pH value and chloride ion concentration data of the concrete are collected using chemical sensors, and the relationship between the target chemical data and the concrete structure damage is analyzed to identify the corrosion process of the concrete and obtain the corrosion process identification results;

[0065] S6: Based on the corrosion process identification results, by evaluating the impact of multiple factors on the durability of concrete, simulating the long-term performance response of concrete, combining with actual monitoring data, analyzing the durability of concrete structures, and generating tunnel concrete inspection results.

[0066] The crack position identification results include crack coordinate data sets, crack length information, and crack direction feature information. The crack type classification list includes crack width range, crack depth classification results, and crack morphology type information. The crack path prediction results include expansion direction prediction results, expansion speed prediction information, and impact area prediction data. The structural stability analysis data includes the calculation results of the crack fractal dimension, the distribution density of cracks, and the threat level of cracks to structural integrity. The corrosion process identification results include the pH level of concrete, the chloride ion concentration level, and the corrosion rate assessment data. The tunnel concrete inspection results include the concrete durability score, the weight distribution information of influencing factors, and the predicted life of concrete.

[0067] See also Figure 2 , based on the image acquisition device, collect and process the concrete image data of multiple areas of the tunnel, identify the location and distribution of cracks, and the steps for obtaining the crack location identification result are as follows:

[0068] S101: Based on the image acquisition device, adjust the device parameters according to the ambient light information, including light sensitivity and exposure time, collect concrete image data of multiple areas of the tunnel, adjust the brightness and contrast to optimize the image quality, and generate image data processing results;

[0069] In sub-step S101, based on the image acquisition device, the light sensitivity and exposure time of the device are automatically adjusted through the ambient light information to match the light conditions of different areas in the tunnel, optimize the brightness and contrast of the image, ensure the clarity and availability of the concrete image data, record the image characteristics under different lighting conditions, adjust the image parameters through the image processing algorithm, and perform image preprocessing steps, including grayscale conversion and edge enhancement of the image. The generated image data processing results are used for subsequent crack analysis and identification.

[0070] S102: Analyze and identify contour information of multiple cracks in the image based on the image data processing result, record the position coordinates of the cracks, and generate crack coordinate information;

[0071] In sub-step S102, based on the image data processing results, by using edge detection and image segmentation technology, the contour information of the cracks in the image is identified and recorded, including the analysis of the color and grayscale in the image, and the Canny edge detection and Sobel operator are used to identify the position and contour of the cracks, determine the position coordinates of the cracks, record the coordinate information, and track the position and shape of the cracks to provide basic data for subsequent crack distribution analysis. The generated crack coordinate information is a digital description of the crack characteristics in the image, providing a data basis for subsequent distribution pattern analysis.

[0072] S103: Based on the crack coordinate information, the distribution pattern of the cracks on the concrete surface is analyzed, the length, direction and density of the cracks are evaluated, the uniformity and aggregation trend of the crack distribution are calculated, and the crack position identification result is generated;

[0073] In the above content, based on the crack coordinate information, the formula , calculate the uniformity of crack distribution ;

[0074] In the formula, , represents the coordinates of the crack, is the distance from the crack to the center, is the average distance from all cracks to the center, is the number of cracks;

[0075] Detailed explanation of the formula and the process of formula calculation and derivation:

[0076] By using the coordinates of each crack, calculate the average distance from each crack to the center , calculate the sum of the squares of the deviations of each crack distance from the average distance and divide it by the total number of cracks Get the uniformity index , assuming that there are three cracks with coordinates (1,2), (3,4), and (5,6), and the center coordinate is (3,4), then:

[0077] ,

[0078] ;

[0079] The results show that the crack distribution has a certain aggregation trend.

[0080] See also Figure 3 Based on the crack position recognition results, the image data of multiple concrete cracks are analyzed, the geometric characteristics of the cracks are identified, and the cracks are classified. The specific steps for generating a crack type classification list are as follows:

[0081] S201: Based on the crack position recognition result and crack image information, the width, length and depth of multiple cracks are analyzed and recognized to generate crack size data;

[0082] In sub-step S201, based on the crack position identification results, the width, length and depth of the crack are measured through image analysis technology, including analysis of the pixel resolution of the crack image, edge detection using the image processing software tool OpenCV, and measurement of the physical dimensions of the cracks, including the longest and widest points of each crack. The depth of the crack is estimated through image comparison technology. The data is accurately recorded and converted into crack size data, providing the necessary input data for subsequent three-dimensional feature analysis. The generated crack size data includes the three-dimensional physical properties of each crack, providing data support for structural analysis and risk assessment.

[0083] S202: Based on the crack size data, the three-dimensional characteristics of the crack are evaluated by calculating the crack volume and surface roughness, and the crack geometric characteristic data is generated;

[0084] In the above content, based on the crack size data, according to the formula , calculate the volume of the crack ;

[0085] In the formula, represents the crack width, represents the crack length, Represents the crack depth;

[0086] Detailed explanation of the formula and the process of formula calculation and derivation:

[0087] By obtaining the size data of each crack , , , calculate the product of the width, length and depth of the crack to get the volume of the crack. Assuming the crack width is 0.02 meters, the length is 1.00 meters, and the depth is 0.01 meters, the formula is used to calculate: The result represents the volume of the crack. The surface roughness of the crack is analyzed by the irregularity of the crack edge and the degree of concavity and convexity inside the crack, providing data on the three-dimensional structural characteristics of the crack.

[0088] S203: Based on the crack geometric feature data, the cracks are classified according to shape and length information, including surface cracks and structural cracks, and a crack type classification list is generated;

[0089] In sub-step S203, based on the crack geometric feature data, a morphological analysis method is used to classify the cracks according to their shape and length information, distinguishing between surface cracks and structural cracks, including analyzing the shape characteristics of the cracks, using the classification algorithm support vector machine to automatically identify the crack types, recording the distribution of each type of cracks, and generating a crack type classification list. The list explains the characteristics and causes of each type of crack, providing a scientific basis for subsequent maintenance and reinforcement.

[0090] See also Figure 4 , according to the crack type classification list, identify and track the paths of multiple cracks in the tunnel, analyze and predict the expansion direction of the cracks, and obtain the crack path prediction results in the following steps:

[0091] S301: identifying the start and end coordinates of multiple concrete cracks in the tunnel according to the crack type classification list, analyzing the directionality and length of the multiple cracks, and generating crack direction data;

[0092] In sub-step S301, based on the crack type classification list, image acquisition and analysis technology is used to identify the starting and ending coordinates of each concrete crack in the tunnel. Geographic information system technology and image processing software are used to detect the direction and length of the cracks by utilizing the image recognition algorithm Hough transform, including direct measurement of crack length and analysis of directionality, which is used to understand the distribution characteristics of cracks in the tunnel structure. The pattern of crack direction is shared through coordinate data to provide data support for crack risk assessment. The generated crack direction data records the spatial location and extension direction of each crack, which is used for subsequent structural health monitoring and maintenance decisions, helping engineers predict the diffusion path of cracks, take appropriate reinforcement measures at an early stage, and avoid the risks brought by crack development.

[0093] S302: Based on the crack direction data, by identifying the expansion paths of multiple cracks in the image, analyzing the change trend of the crack length, measuring the distance and angle changes between key points, and generating expansion path information;

[0094] In sub-step S302, based on the crack direction data, the expansion path of the crack is tracked and recorded through image analysis technology. By identifying the dynamic changes in the expansion direction and length of multiple cracks in the image, the behavior and development trend of the cracks in the structure are evaluated. Image segmentation and pattern recognition are used to identify the expansion speed and direction of the cracks in the image sequence. By measuring the changes in the distance and angle between key points along the length of the crack, the expansion rate and direction of the crack are calculated. The generated expansion path information will provide a scientific basis for the risk assessment and prevention strategy formulation of the cracks, which will be used to maintain the safety of the structure and extend its service life, ensuring the safety of personnel and property.

[0095] S303: Based on the expansion path information, combined with the crack starting position, trend, and expansion direction prediction data, the crack expansion area is evaluated, and combined with the crack state and tunnel environmental conditions, a crack path prediction result is generated;

[0096] In the above content, according to the expansion path information, according to the formula Calculate the area of ​​crack expansion ;

[0097] In the formula, , represents the coordinates of the crack starting position, , Represents the coordinates of the crack end point.

[0098] Detailed explanation of the formula and the process of formula calculation: By obtaining the starting and ending coordinates of the crack, the Euclidean distance between the two points is calculated to obtain the total expansion area of ​​the crack. Assuming that the starting coordinates of the crack are (1,1) and the ending coordinates are (4,5), the formula is used to calculate:

[0099] ;

[0100] The results represent the expansion area of ​​the crack, which is used to evaluate the potential spread of the crack and, combined with the environmental conditions of the tunnel, generate crack path prediction results.

[0101] See also Figure 5 Based on the crack path prediction results, the fractal dimension of the crack pattern is calculated to evaluate the complexity of the cracks and analyze their impact on the tunnel structure stability. The specific steps for generating structural stability analysis data are as follows:

[0102] S401: According to the crack path prediction result and the geometric information of the crack, the fractal dimension of the crack pattern is calculated, the complexity of the crack is quantitatively evaluated, and the crack complexity data is generated;

[0103] In the above content, based on the geometric information of the crack, according to the formula , calculate the fractal dimension of the crack pattern ;

[0104] In the formula, represents the minimum number of spheres covering the crack, represents the diameter of the sphere;

[0105] Detailed explanation of the formula and the process of formula calculation and derivation:

[0106] By choosing a small scale , calculate the minimum number of spheres required to cover the crack at this scale , the fractal dimension is calculated by logarithmic function relationship, assuming the scale m, 1000 spheres are needed to cover the crack, calculated according to the formula ;

[0107] It shows that the crack pattern has a moderate complexity level. The fractal dimension is used to quantify the complexity of the cracks and reflects the space filling ability and complexity of the crack pattern.

[0108] S402: Based on the crack complexity data, analyze the nonlinear characteristics and random distribution characteristics of the cracks, evaluate the impact on the stability of the tunnel structure, and generate influencing factor analysis data;

[0109] In sub-step S402, based on the crack complexity data, the nonlinear characteristics and random distribution features of the cracks are analyzed, and statistical methods and geometric analysis are used to evaluate the potential impact of various characteristics on the stability of the tunnel structure. By studying the behavior and distribution patterns of cracks at different scales, the uncertainty and randomness of crack growth are analyzed, and the future development trend of cracks is predicted, providing a scientific basis for formulating repair and reinforcement strategies. The generated influencing factor analysis data illustrates the impact of each crack characteristic on the overall stability of the structure, providing key information for tunnel maintenance and safety assessment.

[0110] S403: Analyze the data based on the influencing factors, analyze the risk levels of multiple cracks by comparing with the engineering safety standards, and generate structural stability analysis data;

[0111] In sub-step S403, based on the influencing factor analysis data and by comparing with the current engineering safety standards, a risk level analysis of multiple cracks in the tunnel is conducted, including a systematic assessment of the structural stability of the area where the cracks are located, taking into account the size, shape, distribution and impact of the cracks on load transfer, using quantitative analysis methods such as finite element analysis to simulate the impact of cracks on structural performance, and combining crack complexity and expansion trends to evaluate potential structural failure risks. The generated structural stability analysis data records the location, size and predicted future behavior of each crack. The information is used to formulate emergency response plans and long-term maintenance strategies to ensure that the project can continue to operate under safe conditions and that necessary reinforcement and reconstruction can be carried out in a timely manner.

[0112] See also Figure 6 Based on the structural stability analysis data, chemical sensors are used to collect the pH value and chloride ion concentration data of concrete, and the relationship between the target chemical data and the concrete structure damage is analyzed to identify the corrosion process of concrete. The specific steps for obtaining the corrosion process identification results are as follows:

[0113] S501: Based on the structural stability analysis data, chemical sensors are used to collect the pH value and chloride ion concentration of concrete in multiple areas of the tunnel to obtain real-time chemical monitoring data;

[0114] In sub-step S501, based on the structural stability analysis data, chemical sensors installed in the tunnel are used to systematically collect the pH value and chloride ion concentration of concrete in each area, obtain real-time chemical monitoring data, and evaluate the impact of environmental factors on tunnel structural materials. Sensors are arranged in key parts of the structure to continuously track changes in chemical properties, including in areas near cracks. By analyzing a variety of chemical data, potential corrosion problems caused by chemical reactions are explored. Monitoring data include periodic readings of pH values ​​and cumulative concentrations of chloride ions, which reflect the acidity and salt content of the concrete environment and are key factors in evaluating material durability. Analysis of real-time data is used to identify the current corrosion state, provide a scientific basis for formulating subsequent anti-corrosion measures, and ensure the reliability and sustainability of safe operation of the tunnel.

[0115] S502: Based on the real-time chemical monitoring data, by analyzing the pH value and chloride ion concentration of concrete samples with various corrosion degrees, the correlation between various chemical index data and concrete structure damage is calculated to generate corrosion damage correlation information;

[0116] In sub-step S502, based on real-time chemical monitoring data, the pH values ​​and chloride ion concentrations in concrete samples with different corrosion degrees are compared and analyzed, and the correlation between various chemical indicators and concrete structure damage is evaluated using statistical methods and data analysis techniques. By collecting data on concrete samples with various corrosion degrees, a mathematical model is established to analyze the quantitative relationship between the decrease in pH value and the increase in chloride ion concentration and the degree of concrete corrosion. By analyzing the influence of various chemical reactions of steel bar corrosion and carbonization in concrete, chemical analysis and experimental verification are used to generate corrosion damage correlation information for predicting corrosion trends and formulating effective corrosion prevention and control strategies, helping engineers optimize maintenance plans and ensure the long-term stability and safety of structures.

[0117] S503: According to the corrosion damage association information and various chemical data of the concrete, the current corrosion state of the concrete is evaluated, key corrosion areas and corrosion processes are identified, and a corrosion process identification result is generated;

[0118] In sub-step S503, based on the corrosion damage association information, various chemical data of concrete are evaluated, key corrosion areas are identified, and the corrosion process is analyzed. Data analysis tools and chemical detection techniques are used to analyze various chemical indicators in concrete samples, including pH value, chloride ion concentration, and sulfate content. Chemical indicators are used to analyze the mechanism and speed of concrete corrosion. The severity and speed of the corrosion state are identified through comparative analysis, and risk areas are discovered in a timely manner. The generated corrosion process identification results provide a basis for subsequent repair work and preventive measures, ensuring the health of the structure and extending the service life, reducing the economic cost of future maintenance and potential safety risks.

[0119] See also Figure 7 According to the corrosion process identification results, by evaluating the impact of multiple factors on the durability of concrete, simulating the long-term performance response of concrete, and combining the actual monitoring data, the durability of the concrete structure is analyzed. The specific steps for generating tunnel concrete test results are as follows:

[0120] S601: Based on the corrosion process identification results, by analyzing the corrosion rate and affected area, evaluate the impact of various environmental factors on concrete corrosion, including temperature and humidity, calculate the weight coefficients of various influencing factors, and generate environmental impact assessment data;

[0121] The specific formula for calculating the weight coefficients of various influencing factors is: ;

[0122] in, The number representing the total environmental factors, including those considered to affect concrete corrosion, It is the index number of the target environmental factor, which is used to distinguish different environmental factors. and It is the index used for iteration in the sum operation, representing the column number and row number in the judgment matrix, and is used to traverse the elements in the judgment matrix for calculation. is the first Line Elements of the column, representing environmental factors Relative to environmental factors The relative importance of is an element in the judgment matrix, located at Line Columns are used for normalization in the weight calculation formula to ensure that the weight of each factor is reasonably distributed. Representative The weight coefficients of the environmental factors are calculated by the hierarchical analysis method, which reflects the relative importance of the target factor among the considered environmental factors and evaluates and quantifies the influence of various environmental factors on the concrete corrosion process.

[0123] formula Detailed explanation and formula calculation derivation process:

[0124] The formula is used to calculate the weight coefficient of each environmental factor , by normalizing the values ​​in the judgment matrix to ensure the reasonable distribution of weights, the result is Used to indicate the relative importance of each factor in the corrosion process.

[0125] Parameter meaning and setting value:

[0126] is the total number of environmental factors, assuming it is 4, which means considering temperature, humidity, pH value and chloride ion concentration.

[0127] Assumptions , used to indicate temperature, is the weight coefficient of temperature, For the Line The judgment matrix value of the column reflects the environmental factors Relative to The importance of For the Line The judgment matrix value of the column is used for normalization, assuming the importance of temperature to humidity is 1.2, the importance of humidity to temperature 1 / 1.2, the importance of temperature to pH value The importance of pH value to temperature 1 / 1.5, the importance of humidity to chloride ion concentration is 1.3, the importance of chloride ion concentration to humidity is 1 / 1.3;

[0128] Substitute the parameters into the formula for calculation: .

[0129] result It shows that among the environmental factors considered, the importance of temperature factor accounts for about 26.87%, indicating that temperature is a relatively important factor.

[0130] S602: Based on the environmental impact assessment data, simulate the long-term performance response of concrete, conduct durability analysis in combination with actual monitoring data, predict the state change trend of the concrete structure, and generate concrete durability simulation results;

[0131] In sub-step S602, the long-term performance response of concrete is evaluated based on the environmental impact assessment data. This includes using simulation software and data analysis technology, combined with data from actual on-site monitoring, to analyze the durability of concrete. The simulation takes into account a variety of factors, including temperature, humidity, chemical concentrations and various variables that affect the performance of concrete in the environment, and predicts the state change trend of concrete in the next few years or even decades. The simulation results are used to determine the structural areas that need special attention or reinforcement so that maintenance or reinforcement measures can be taken in advance. The generated concrete durability simulation results provide a scientific basis for the tunnel maintenance plan, ensuring the long-term safety and functionality of the structure, while providing key information for budget planning and resource allocation.

[0132] S603: Based on the concrete durability simulation results, combined with the concrete crack data and corrosion conditions, the durability and risk of the concrete structure in the tunnel are evaluated, the remaining life of the concrete is predicted, and the tunnel concrete inspection results are generated;

[0133] In sub-step S603, based on the concrete durability simulation results, combined with the concrete crack data and corrosion conditions, the durability and risk assessment of the concrete structure in the tunnel is carried out. By analyzing the distribution, size and severity of the cracks and evaluating the impact of the target factors on the overall durability of the concrete structure, the interaction between cracks and corrosion and the potential threat to the structural stability are calculated, the remaining life of the concrete is predicted, and the risk areas are identified. The generated tunnel concrete inspection results provide guidance for repair and reinforcement work, ensure personnel safety and long-term availability of the structure, and help the engineering management team make decisions between preventive maintenance and emergency repairs.

[0134] See also Figure 8 , a tunnel concrete detection and analysis system, the tunnel concrete detection and analysis system is used to perform the above tunnel concrete detection and analysis method, the system includes:

[0135] The crack recognition module is based on the image acquisition device, which collects image data of the concrete surface in the tunnel, identifies the crack location and records the coordinate information, analyzes the geometric characteristics of the crack and classifies the crack to obtain concrete crack classification data;

[0136] The crack tracking module identifies and tracks the paths of multiple cracks in the tunnel based on concrete crack classification data, analyzes the crack direction and predicts the crack expansion direction, and generates crack direction analysis results;

[0137] The stability assessment module is based on the crack trend analysis results. By calculating the fractal dimension of the crack pattern, the complexity of the cracks and their impact on the stability of the tunnel structure are analyzed to generate the crack impact assessment results.

[0138] The corrosion state analysis module uses chemical sensors to monitor the pH value and chloride ion concentration of concrete based on the crack impact assessment results, analyzes the relationship between chemical data and concrete structure damage, identifies the development stage of corrosion, and generates corrosion stage assessment data;

[0139] The durability analysis module evaluates the impact of various environmental factors on the durability of concrete based on the corrosion stage assessment data, analyzes the durability of the concrete structure in combination with the actual monitoring data, and generates tunnel concrete test results.

[0140] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A tunnel concrete detection and analysis method, characterized in that: The following steps are involved: Based on the image acquisition equipment, the concrete image data of multiple areas of the tunnel are collected and processed to identify the location and distribution of cracks and obtain the crack location identification results; Based on the crack position identification result, the image data of multiple concrete cracks are analyzed to identify the geometric features of the cracks, and the cracks are classified to generate a crack type classification list; According to the crack type classification list, the paths of multiple cracks in the tunnel are identified and tracked, the expansion directions of the cracks are analyzed and predicted, and crack path prediction results are obtained; Based on the crack path prediction results, by calculating the fractal dimension of the crack pattern, the complexity of the cracks is evaluated, the impact on the tunnel structure stability is analyzed, and structural stability analysis data is generated; Based on the structural stability analysis data, a chemical sensor is used to collect pH value and chloride ion concentration data of the concrete, and the relationship between the target chemical data and the concrete structure damage is analyzed to identify the corrosion process of the concrete and obtain the corrosion process identification result; Based on the corrosion process identification results, by evaluating the impact of multiple factors on the durability of concrete, simulating the long-term performance response of concrete, and combining with actual monitoring data, analyzing the durability of the concrete structure, and generating tunnel concrete test results; Among them, the crack position identification result includes the crack coordinate data set, crack length information, and crack direction feature information; the crack type classification list includes the crack width range, crack depth classification results, and crack morphology type information; the crack path prediction result includes the expansion direction prediction result, expansion speed prediction information, and impact area prediction data; the structural stability analysis data includes the calculation result of the crack fractal dimension, the distribution density of the cracks, and the threat level of the cracks to the structural integrity; the corrosion process identification result includes the pH level of the concrete, the chloride ion concentration level, and the corrosion rate assessment data; the tunnel concrete detection result includes the concrete durability score, the weight distribution information of the influencing factors, and the predicted life of the concrete.

2. The tunnel concrete detection and analysis method according to claim 1 is characterized in that: Based on the image acquisition equipment, the concrete image data of multiple areas of the tunnel are collected and processed to identify the location and distribution of cracks. The steps for obtaining the crack location identification result are as follows: Based on the image acquisition device, the device parameters, including light sensitivity and exposure time, are adjusted according to the ambient light information to collect concrete image data in multiple areas of the tunnel, and the brightness and contrast are adjusted to optimize the image quality to generate image data processing results; Based on the image data processing result, analyzing and identifying the contour information of multiple cracks in the image, recording the position coordinates of the cracks, and generating the crack coordinate information; Based on the crack coordinate information, the distribution pattern of cracks on the concrete surface is analyzed, the length, direction and density of the cracks are evaluated, the uniformity and aggregation trend of the crack distribution are calculated, and the crack position identification result is generated.

3. The tunnel concrete detection and analysis method according to claim 1 is characterized in that: Based on the crack position recognition result, the image data of multiple concrete cracks are analyzed, the geometric features of the cracks are identified, and the cracks are classified. The steps of generating a crack type classification list are specifically as follows: Based on the crack position identification result, according to the crack image information, the width, length and depth of multiple cracks are analyzed and identified to generate crack size data; Based on the crack size data, by calculating the crack volume and surface roughness, the three-dimensional characteristics of the crack are evaluated to generate crack geometric characteristic data; Based on the crack geometric feature data, the cracks are classified according to shape and length information, including surface cracks and structural cracks, and a crack type classification list is generated.

4. The tunnel concrete detection and analysis method according to claim 1 is characterized in that: According to the crack type classification list, the paths of multiple cracks in the tunnel are identified and tracked, and the expansion directions of the cracks are analyzed and predicted to obtain the crack path prediction results in the following steps: According to the crack type classification list, identifying the starting and ending coordinates of multiple concrete cracks in the tunnel, analyzing the directionality and length of the multiple cracks, and generating crack direction data; Based on the crack direction data, by identifying the expansion paths of multiple cracks in the image, analyzing the change trend of the crack length, measuring the distance and angle changes between key points, and generating expansion path information; According to the expansion path information, combined with the crack starting position, trend, and expansion direction prediction data, the area of ​​crack expansion is evaluated, and the crack path prediction result is generated in combination with the crack state and tunnel environmental conditions.

5. The tunnel concrete detection and analysis method according to claim 1 is characterized in that: Based on the crack path prediction results, by calculating the fractal dimension of the crack pattern, evaluating the complexity of the cracks, analyzing the impact on the tunnel structure stability, and generating the structural stability analysis data, the specific steps are: According to the crack path prediction result and the geometric information of the crack, the fractal dimension of the crack pattern is calculated, the complexity of the crack is quantitatively evaluated, and crack complexity data is generated; Based on the crack complexity data, the nonlinear characteristics and random distribution characteristics of the cracks are analyzed, and the impact on the stability of the tunnel structure is evaluated to generate influencing factor analysis data; According to the influencing factor analysis data, by comparing with the engineering safety standards, the risk levels of multiple cracks are analyzed and the structural stability analysis data is generated.

6. The tunnel concrete detection and analysis method according to claim 1 is characterized in that: Based on the structural stability analysis data, the pH value and chloride ion concentration data of the concrete are collected by using a chemical sensor, and the relationship between the target chemical data and the concrete structure damage is analyzed to identify the corrosion process of the concrete. The steps of obtaining the corrosion process identification result are specifically as follows: Based on the structural stability analysis data, chemical sensors are used to collect pH values ​​and chloride ion concentrations of concrete in multiple areas of the tunnel to obtain real-time chemical monitoring data; Based on the real-time chemical monitoring data, by analyzing the pH value and chloride ion concentration of concrete samples with various corrosion degrees, the correlation between various chemical index data and concrete structure damage is calculated to generate corrosion damage correlation information; According to the corrosion damage association information and various chemical data of the concrete, the current corrosion state of the concrete is evaluated, key corrosion areas and corrosion processes are identified, and corrosion process identification results are generated.

7. The tunnel concrete detection and analysis method according to claim 1 is characterized in that: According to the corrosion process identification results, by evaluating the impact of various factors on the durability of concrete, simulating the long-term performance response of concrete, and combining actual monitoring data to analyze the durability of concrete structures, the steps for generating tunnel concrete test results are as follows: Based on the corrosion process identification result, by analyzing the corrosion rate and the affected area, the influence of various environmental factors on concrete corrosion, including temperature and humidity, is evaluated, the weight coefficients of various influencing factors are calculated, and environmental impact assessment data is generated; Based on the environmental impact assessment data, simulate the long-term performance response of concrete, conduct durability analysis in combination with actual monitoring data, predict the state change trend of the concrete structure, and generate concrete durability simulation results; According to the concrete durability simulation results, combined with concrete crack data and corrosion conditions, the durability and risk of the concrete structure in the tunnel are evaluated, the remaining life of the concrete is predicted, and the tunnel concrete detection results are generated.

8. The tunnel concrete detection and analysis method according to claim 7 is characterized in that: The specific formula for calculating the weight coefficients of the multiple influencing factors is: ， in, The number representing the total environmental factors, including those considered to affect concrete corrosion, It is the index number of the target environmental factor, which is used to distinguish different environmental factors. and It is the index used for iteration in the sum operation, representing the column number and row number in the judgment matrix, and is used to traverse the elements in the judgment matrix for calculation. is the first Line Elements of the column, representing environmental factors Relative to environmental factors The relative importance of is an element in the judgment matrix, located at Line Column, used for normalization in the weight calculation formula to ensure that the weight of each factor is reasonably distributed. Representative The weight coefficients of the environmental factors are calculated by the hierarchical analysis method, which reflects the relative importance of the target factor among the considered environmental factors and evaluates and quantifies the influence of various environmental factors on the concrete corrosion process.

9. A tunnel concrete detection and analysis system, characterized in that: According to the tunnel concrete detection and analysis method according to any one of claims 1 to 8, the system comprises: The crack recognition module is based on the image acquisition device, which collects image data of the concrete surface in the tunnel, identifies the crack location and records the coordinate information, analyzes the geometric characteristics of the crack and classifies the crack to obtain concrete crack classification data; The crack tracking module identifies and tracks the paths of multiple cracks in the tunnel based on the concrete crack classification data, analyzes the crack trends and predicts the crack expansion directions, and generates crack trend analysis results; The stability assessment module calculates the fractal dimension of the crack pattern based on the crack trend analysis result, analyzes the complexity of the crack and the impact on the stability of the tunnel structure, and generates a crack impact assessment result; The corrosion state analysis module uses chemical sensors to monitor the pH value and chloride ion concentration of concrete based on the crack impact assessment results, analyzes the relationship between chemical data and concrete structure damage, identifies the development stage of corrosion, and generates corrosion stage assessment data; The durability analysis module evaluates the impact of various environmental factors on the durability of concrete based on the corrosion stage assessment data, analyzes the durability of the concrete structure in combination with the actual monitoring data, and generates tunnel concrete detection results.

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