Highway engineering quality detection method based on big data analysis

Abstract

The invention discloses a highway engineering quality detection method based on big data analysis, and the method comprises the following steps: collecting the structural response data, environmental data and traffic load data of highway engineering in real time through a sensor network, and collecting the high-definition image data of a highway pavement and a bridge structure part through an image collection device; road use feedback data are collected, driving experience feedback of vehicle users is covered, displacement monitors and other auxiliary monitoring instruments are deployed for tunnels, road landslide collapse road sections and high slope road sections, and data of high slope surface deformation, underground deformation, strain, hydrology, environment, vehicle operation and the like in the construction stage and the operation stage are collected; the beneficial effects of the invention are that through multi-source data acquisition, information of the structure, the environment, the traffic and the like of the highway engineering is comprehensively covered, and compared with single image data, the highway engineering quality can be evaluated more accurately, and the detection accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of highway engineering quality detection, and specifically relates to a highway engineering quality detection method based on big data analysis. Background Art

[0002] As a core component of national infrastructure construction, the quality of highway projects is directly related to traffic safety, economic benefits and social development; quality inspection, as a key link in ensuring project quality, runs through the entire life cycle of highway construction and has irreplaceable importance.

[0003] Defects in highway projects may cause vehicles to lose control, rear-end collisions, or even overturn; quality inspections can detect and repair potential hidden dangers in advance, reducing the risk of accidents; highway projects need to withstand long-term vehicle loads and erosion from the natural environment; quality inspections can ensure that material strength and structural stability meet design standards, extending the service life of the highway; through construction process inspections, quality problems can be discovered and rectified in a timely manner, avoiding large-scale rework at a later stage; quality inspections provide a performance verification platform for new materials and accelerate technological innovation; inspection data provides a basis for the revision of industry standards and promotes technological progress; quality inspections can optimize material use and avoid resource consumption caused by excessive construction; high-quality highway projects can reduce frequent repairs due to diseases and reduce the interference of construction on the surrounding ecology.

[0004] Patent publication number CN119671947A discloses a highway engineering quality inspection method and system based on image recognition. This patent primarily collects pavement images, performs crack identification and edge detection, and obtains the spatiotemporal evolution characteristics and potential expansion points of cracks, thereby determining the crack risk level. However, this method focuses solely on image analysis of pavement cracks. For highway engineering quality inspection, the data source is single, making it difficult to fully reflect the overall quality status of highway engineering. Furthermore, there is room for improvement in inspection efficiency and accuracy, and the method lacks comprehensive analysis and timely warning capabilities based on big data. Summary of the Invention

[0005] The purpose of the present invention is to provide a highway engineering quality inspection method based on big data analysis, which improves the efficiency and accuracy of highway engineering quality inspection by integrating multi-source data for big data analysis and realizes timely early warning.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting highway engineering quality based on big data analysis, comprising the following steps:

[0007] Utilize sensor networks to collect real-time structural response data, environmental data, and traffic load data for highway projects. Image acquisition equipment is also used to collect high-definition image data of highway pavements and bridge structures. Road user feedback data is collected, including driving experience feedback from vehicle users. Displacement monitors and auxiliary monitoring instruments are deployed in tunnels, sections of highway landslides and collapses, and sections with high side slopes to collect surface deformation, underground deformation, strain, hydrological, environmental, and vehicle operation data for high side slopes during the construction and operation phases.

[0008] Use deep learning anomaly detection algorithms to clean the collected multi-source data, accurately remove outliers, noise data, and duplicate data; dynamically select standardization methods based on data feature distribution; use feature fusion technology to deeply fuse different types of data features to construct composite features;

[0009] A highway engineering quality inspection model is constructed by integrating support vector machines, random forests, and deep neural networks. The model input is the fused multi-source data features, and the output is the quality status assessment results of various parts of the highway project. Dropout and Early Stopping regularization techniques are combined with the Adaboost model integration method.

[0010] Through in-depth mining and analysis of massive historical and real-time data, we can discover potential connections and patterns between data and construct complex characteristic relationships that reflect the impact of environmental factors on structures.

[0011] Set quality indicator thresholds. When the quality status assessment results output by the detection model exceed the threshold range, a multi-channel early warning mechanism is triggered, and early warning information is sent to management personnel via SMS, email, and instant messaging platforms. The early warning information includes a detailed description of the quality problem, the scope of impact, and development trend forecasts, and the early warning location is intuitively displayed in conjunction with the geographic information system.

[0012] As new data is continuously collected and accumulated, the quality inspection model is incrementally learned and optimized using the new data.

[0013] As a preferred technical solution of the present invention, the sensor network includes stress sensors, strain sensors, temperature and humidity sensors buried in the highway subgrade and pavement structure layer, as well as traffic flow monitoring equipment and meteorological monitoring equipment installed along the highway.

[0014] As a preferred technical solution of the present invention, a deep learning anomaly detection algorithm is used to clean numerical data, and a deep learning image enhancement algorithm combined with a histogram equalization method is used to preprocess image data.

[0015] As a preferred technical solution of the present invention, the machine learning algorithm is a combination of one or more of support vector machines, random forests, and deep neural networks.

[0016] As a preferred technical solution of the present invention, a data mining algorithm is used to mine association rules on historical data, and at the same time, a composite feature relationship reflecting the impact of environmental factors on the structure is constructed.

[0017] As a preferred technical solution of the present invention, the early warning information is sent to management personnel via SMS, email, and instant messaging platforms, and the early warning location and warning level are highlighted on the monitoring platform in combination with the geographic information system, while displaying a detailed description of the quality problem, the scope of impact, and the forecast of development trends.

[0018] As a preferred technical solution of the present invention, when constructing a quality inspection model, historical data of highway projects, including multi-source data samples under different quality conditions and road usage feedback data, are collected, the data are labeled, and the labeled data are used to train the machine learning model.

[0019] As a preferred technical solution of the present invention, the thresholds of various quality indicators are dynamically set according to highway engineering design standards, historical experience and road use feedback data; the thresholds are adjusted in a timely manner as the highway engineering ages and environmental factors change.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] This invention uses multi-source data collection to comprehensively cover information on the structure, environment, and traffic of highway projects. Compared with single image data, it can more accurately assess the quality of highway projects and improve detection accuracy.

[0022] Using big data analysis to mine potential patterns in data, the quality inspection model built based on machine learning can quickly process massive amounts of data, greatly improving the efficiency of quality inspection;

[0023] The dynamic update model ensures that the detection method can adapt to changes in highway projects at different stages of use and environmental conditions, and continuously provide reliable quality detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the quality detection method of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Example 1

[0027] See also Figure 1 , which is the first embodiment of the present invention, provides a highway engineering quality detection method based on big data analysis, comprising the following steps:

[0028] A highway engineering quality detection method based on big data analysis includes the following steps:

[0029] Utilize sensor networks to collect real-time structural response data, environmental data, and traffic load data for highway projects. Image acquisition equipment is also used to collect high-definition image data of highway pavements and bridge structures. Road user feedback data is collected, including driving experience feedback from vehicle users. Displacement monitors and auxiliary monitoring instruments are deployed in tunnels, sections of highway landslides and collapses, and sections with high side slopes to collect surface deformation, underground deformation, strain, hydrological, environmental, and vehicle operation data for high side slopes during the construction and operation phases.

[0030] Use deep learning anomaly detection algorithms to clean the collected multi-source data, accurately remove outliers, noise data, and duplicate data; dynamically select standardization methods based on data feature distribution; use feature fusion technology to deeply fuse different types of data features to construct composite features;

[0031] A highway engineering quality inspection model is constructed by integrating support vector machines, random forests, and deep neural networks. The model input is the fused multi-source data features, and the output is the quality status assessment results of various parts of the highway project. Dropout and Early Stopping regularization techniques are combined with the Adaboost model integration method.

[0032] Through in-depth mining and analysis of massive historical and real-time data, we can discover potential connections and patterns between data and construct complex characteristic relationships that reflect the impact of environmental factors on structures.

[0033] Set quality indicator thresholds. When the quality status assessment results output by the detection model exceed the threshold range, a multi-channel early warning mechanism is triggered, and early warning information is sent to management personnel via SMS, email, and instant messaging platforms. The early warning information includes a detailed description of the quality problem, the scope of impact, and development trend forecasts, and the early warning location is intuitively displayed in conjunction with the geographic information system.

[0034] As new data is continuously collected and accumulated, the quality inspection model is incrementally learned and optimized using the new data.

[0035] In this embodiment, preferably, the sensor network includes stress sensors, strain sensors, temperature and humidity sensors buried in the roadbed and pavement structure layer, as well as traffic flow monitoring equipment and meteorological monitoring equipment installed along the highway.

[0036] In this embodiment, preferably, a deep learning anomaly detection algorithm is used to clean the numerical data, and a deep learning image enhancement algorithm combined with a histogram equalization method is used to preprocess the image data.

[0037] In this embodiment, preferably, the machine learning algorithm is a combination of one or more of support vector machines, random forests, and deep neural networks.

[0038] In this embodiment, preferably, a data mining algorithm is used to mine association rules on historical data, and a composite feature relationship reflecting the impact of environmental factors on the structure is constructed.

[0039] In this embodiment, preferably, the warning information is sent to management personnel via SMS, email, and instant messaging platforms, and the warning location and warning level are highlighted on the monitoring platform in combination with the geographic information system, while displaying a detailed description of the quality problem, the scope of impact, and the development trend forecast.

[0040] In this embodiment, preferably, when constructing a quality inspection model, historical data of highway projects are collected, including multi-source data samples under different quality conditions and road usage feedback data, the data are labeled, and the labeled data are used to train the machine learning model.

[0041] In this embodiment, preferably, the thresholds of various quality indicators are dynamically set according to highway engineering design standards, historical experience, and road use feedback data; and the thresholds are adjusted in a timely manner as the highway engineering ages and environmental factors change.

[0042] Example 2

[0043] See also Figure 1 , which is the second embodiment of the present invention, is based on the previous embodiment, except that:

[0044] The specific method of constructing a highway engineering quality inspection model is as follows: obtaining data from a sensor network, such as stress sensors buried at different depths in the roadbed, collecting stress data transmitted to the roadbed during vehicle driving, and recording the value every 5 minutes; strain sensors in the pavement structure layer continuously monitor the strain changes of the road surface under the action of vehicle loads, collecting data at intervals of 10 seconds; temperature and humidity sensors record ambient temperature and humidity information every 15 minutes; traffic flow monitoring equipment along the highway counts the number, type, and speed of passing vehicles 24 hours a day; meteorological monitoring equipment collects meteorological data such as wind speed, rainfall, and sunshine duration in real time; through image acquisition equipment, such as high-definition cameras installed on inspection vehicles, according to the weekly inspection cycle, comprehensive photography of highway pavements, bridge beams, piers and other parts is carried out to obtain high-resolution image data; the resolution of each acquired image is not less than 300dpi to ensure that the subtle features of the surface of highway facilities can be clearly presented;

[0045] Establish a professional data annotation team, whose members include experts in highway engineering, experienced engineers, and technicians familiar with image processing;

[0046] For the collected image data, annotators mark pavement cracks, potholes, loose areas, and other defective areas on the images, and classify them according to the severity of the defect. For example, a slight crack with a width of less than 3mm is marked as a slight defect, 3-5mm is a moderate defect, and greater than 5mm is a severe defect. For structural response data and environmental data, combined with highway engineering design standards and actual engineering experience, the data is divided into four quality status levels: normal, slight defect, moderate defect, and severe defect. For example, when the roadbed stress exceeds the design allowable stress by 10-20%, it is marked as a slight defect, 20-50% is a moderate defect, and more than 50% is a severe defect. The annotated data is organized into a data set in preparation for subsequent model training.

[0047] For stress and strain data, statistical characteristics such as mean, variance, maximum, minimum, and peak factor are calculated. For example, by calculating the mean of stress data over a period of time, the average stress level of the roadbed during that period can be reflected. The variance reflects the degree of dispersion of the stress data and the stability of stress changes. For traffic flow data, characteristics such as daily changes in traffic volume and the proportion of traffic volume during peak hours are extracted. For meteorological data, characteristics such as monthly average temperature and humidity and the frequency of extreme meteorological conditions are extracted. These characteristics can describe the operating environment and structural stress conditions of highway projects from different perspectives.

[0048] Image feature extraction is performed using a convolutional neural network (CNN) in deep learning. Taking the VGG16 network as an example, collected images of road surfaces and bridge structures are input into the VGG16 network. After the network's multi-layer convolution and pooling operations, the image's texture features, edge features, and shape features are extracted. For example, the convolution layer learns the edge line features of road cracks, while the pooling layer reduces the dimensionality of the features to retain key feature information. In addition, traditional image feature extraction methods, such as the scale-invariant feature transform (SIFT), can be combined to extract stable local feature points in the image, further enriching the image's feature description.

[0049] Considering that the highway engineering quality inspection model needs to process multi-source data and has high requirements for classification accuracy, a method combining support vector machines (SVM) and random forests (RF) was selected. SVM performs well in small sample and nonlinear classification problems and can effectively handle situations with high data dimensions and complex features. Random forests have good anti-interference ability and generalization performance. By constructing multiple decision trees and combining their prediction results, the risk of overfitting of the model can be reduced.

[0050] The multi-source data after feature engineering is divided into a ratio of 70% as a training set, 20% as a validation set, and 10% as a test set. For the SVM model, the training set data is used to train the model, and the classification performance of the model is improved by adjusting hyperparameters such as the kernel function type (such as linear kernel, Gaussian kernel), penalty parameter C, and kernel parameter γ. For the random forest model, hyperparameters such as the number of decision trees, maximum depth, and minimum number of sample splits are determined. Multiple decision trees are trained using the training set, and these decision trees are combined into a random forest model. During the training process, the performance of the model is evaluated using the validation set, and the model hyperparameters are adjusted based on the evaluation results to achieve the best performance on the validation set. For example, through multiple experiments on the validation set, it was found that when the SVM uses the Gaussian kernel function, C = 10, and γ = 0.1, the model has a higher classification accuracy for quality status. When the number of decision trees in the random forest is 100, the maximum depth is 10, and the minimum number of sample splits is 5, the model performance is better.

[0051] The model is evaluated using the following metrics: accuracy, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Accuracy reflects the proportion of samples correctly predicted by the model to the total number of samples; recall measures the proportion of positive samples correctly identified by the model (such as samples with quality defects) to the actual positive samples. The F1-score comprehensively considers both accuracy and recall, providing a more comprehensive assessment of model performance. The AUC-ROC curve visually demonstrates the model's classification capability by depicting the relationship between the true positive rate and the false positive rate at different classification thresholds.

[0052] Optimize the model based on the results of the evaluation indicators. If the model performs well on the training set but has low accuracy on the validation set and test set, there may be an overfitting problem. In this case, regularization methods, such as L1 and L2 regularization, can be used to constrain the model to prevent it from over-learning the noise and details in the training data. At the same time, you can also try to increase the amount of training data so that the model can learn a wider range of data features and improve the model's generalization ability. If the recall rate of the model on various samples is uneven, for example, the recall rate of samples with severe defects is low, it may be caused by an uneven distribution of various samples in the dataset. Oversampling (such as the SMOTE algorithm) or undersampling methods can be used to process the dataset to make the proportion of various samples relatively balanced, thereby improving the model's recognition ability for minority samples (such as samples with severe defects). By continuously adjusting model parameters and optimizing data processing methods, the model can achieve high accuracy, recall rate, and F1 value on the test set, and the area under the AUC-ROC curve is close to 1, ensuring that the model can accurately and reliably detect and evaluate the quality status of highway projects.

[0053] The following is a comparative experimental data and visualization chart designed to verify the beneficial effects of the technical solution of this application. Through comparative analysis with the traditional image recognition method (CN119671947A), the technical advantages of the present invention are demonstrated:

[0054] Comparison of detection accuracy (%)

[0055] Detection method road cracks roadbed settlement Material aging Comprehensive accuracy Traditional image recognition methods 82.5 - - 82.5 The present invention (multi-source data + SVM) 94.2 89.7 91.3 91.7 The present invention (multi-source data + RF) 93.8 92.1 90.5 92.1

[0056] Analysis: Traditional methods can only detect pavement cracks, and their accuracy is low (82.5%). This invention, through multi-source data fusion, can cover comprehensive quality indicators such as roadbed settlement and material aging, and improve the overall accuracy by more than 10%.

[0057] Comparison of early warning response time (hours)

[0058] Detection method Data collection delay Analysis time Total response time Traditional image recognition methods 24 (manual inspection) 2.5 26.5 The present invention (real-time sensor) 0.1 0.3 0.4

[0059] Analysis: This invention reduces the response time from 26.5 hours to 0.4 hours through a real-time sensor network, increasing fault detection efficiency by 66 times;

[0060] Stability comparison under different environments (F1-score)

[0061] Environmental conditions Traditional method (image only) The present invention (multi-source data) sunny 0.83 0.94 rain 0.62 (image blur) 0.89 (sensor compensation) at night 0.55 (insufficient light) 0.87 (infrared + strain)

[0062] Analysis: Traditional methods are greatly affected by environmental interference, and their performance drops by more than 30% on rainy days and at night. This invention significantly improves stability by complementing multi-source data (such as temperature and humidity sensors compensating for rainy day data).

[0063] Long-term model optimization effect (AUC-ROC)

[0064]

[0065]

[0066] Analysis: By continuously learning from historical data, the model's AUC index improved from 0.87 to 0.95, proving the effectiveness of the dynamic update mechanism;

[0067] Conclusion: Multi-source data covers multi-dimensional indicators such as structure, environment, and load, and the detection scope is expanded from single cracks to overall quality assessment; real-time data processing reduces response time from hours to minutes; multi-sensor fusion overcomes the environmental limitations of a single data source; and the dynamic update mechanism enables the model accuracy to continue to improve as data accumulates.

[0068] Although the embodiments of the present invention have been shown and described, as detailed above, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A highway engineering quality detection method based on big data analysis, characterized by: The steps include: Utilize sensor networks to collect real-time structural response data, environmental data, and traffic load data for highway projects. Image acquisition equipment is also used to collect high-definition image data of highway pavements and bridge structures. Road user feedback data is collected, including driving experience feedback from vehicle users. Displacement monitors and auxiliary monitoring instruments are deployed in tunnels, sections of highway landslides and collapses, and sections with high side slopes to collect surface deformation, underground deformation, strain, hydrological, environmental, and vehicle operation data for high side slopes during the construction and operation phases. Use deep learning anomaly detection algorithms to clean the collected multi-source data, accurately remove outliers, noise data, and duplicate data; dynamically select normalization methods based on data feature distribution; Use feature fusion technology to deeply fuse different types of data features and construct composite features; A highway engineering quality inspection model is constructed by integrating support vector machines, random forests, and deep neural networks. The model input is the fused multi-source data features, and the output is the quality status assessment results of various parts of the highway project. Dropout and Early Stopping regularization techniques are combined with the Adaboost model integration method. Through in-depth mining and analysis of massive historical and real-time data, we can discover potential connections and patterns between data and construct complex characteristic relationships that reflect the impact of environmental factors on structures. Set quality indicator thresholds. When the quality status assessment results output by the detection model exceed the threshold range, a multi-channel early warning mechanism is triggered, and early warning information is sent to management personnel via SMS, email, and instant messaging platforms. The early warning information includes detailed description of the quality problem, scope of impact, and development trend forecast, and is combined with the geographic information system to visually display the warning location; As new data is continuously collected and accumulated, the quality inspection model is incrementally learned and optimized using the new data.

2. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: The sensor network includes stress sensors, strain sensors, temperature and humidity sensors buried in the roadbed and pavement structure layer, as well as traffic flow monitoring equipment and meteorological monitoring equipment installed along the highway.

3. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: The deep learning anomaly detection algorithm is used to clean the numerical data, and the image data is preprocessed by using the deep learning image enhancement algorithm combined with the histogram equalization method.

4. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: The machine learning algorithm is one or more combinations of support vector machines, random forests, and deep neural networks.

5. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: Data mining algorithms are used to mine association rules from historical data, and composite feature relationships that reflect the impact of environmental factors on the structure are constructed.

6. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: The early warning information is sent to management personnel via SMS, email, and instant messaging platforms, and the warning location and warning level are highlighted on the monitoring platform in combination with the geographic information system, while also displaying a detailed description of the quality problem, the scope of impact, and the forecast of development trends.

7. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: When building a quality inspection model, historical data on highway projects is collected, including multi-source data samples under different quality conditions and road usage feedback data. The data is labeled and the labeled data is used to train the machine learning model.

8. The method for detecting highway engineering quality based on big data analysis according to claim 1, characterized in that: The thresholds of various quality indicators are set dynamically based on highway engineering design standards, historical experience, and road usage feedback data; the thresholds are adjusted in a timely manner as the highway engineering ages and environmental factors change.

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