China’s terrain is complex, both plain, microhill (heavy-hill) and mountainous terrain; the hidden dangers of highway construction are prominent. Construction site management, production safety management, and construction personnel management are difficult, and it is necessary to borrow advanced technology to establish information, and it is necessary to borrow advanced technology to establish information system to realize the visualization of safety monitoring. In the construction of highways, mountainous terrain is often complicated due to complex terrain, high mountains, and deep valleys. Excavation of the mountain mass is required to form high and steep slopes. For successful projects, safety monitoring is particularly important. Multisource data fusion is one of the computer application technologies. It is an information processing technology that is automatically analyzed and synthesized under certain criteria to complete the required decision-making and evaluation tasks. This paper analyzes high-speed data in the context of multisource data fusion.
At present, general highway construction project companies and higher-level units cannot implement real-time, comprehensive, and image-based safety monitoring of construction sites due to traffic and environmental reasons: construction management efficiency is not high and managers are struggling with high-load and high-intensity inspections on construction sites or unannounced inspections; many safety management actions are ex post facto. Generally, it is not until a hidden safety hazard appears or a safety accident occurs after some time that it is discovered. It may even take some time for the safety problem to be effectively corrected. The existing methods are difficult to implement real-time command on the construction site, which requires the personnel of all parties to be concentrating on the site. It lacks intuitive on-site video data, and it is difficult to supervise the work of the supervision station, which will inevitably affect the analysis of the cause of the accident and the division of responsibilities. Safety production management is difficult and has also gradually increased. Under some occasions, certain environments, and other major dangerous sources, there is a dire need to use advanced science and technology to establish real-time traceable dynamic engineering archives and information-oriented construction site safety monitoring systems, to provide powerful technical means to improve the work efficiency of on-site management personnel, reduce the probability of accidents in highway construction, and create a “safe construction site” through visualization and information technology, which can provide a strong guarantee for the safe production and emergency rescue of engineering projects.
The highway construction project has the characteristics of many construction points, long lines, remote geographical locations, inconvenient transportation, and many units participating in the construction. The hidden safety hazards are particularly prominent [
HUANG Yong used the support mechanism of the high slope of the roadway in the highway reconstruction project and optimized the support scheme. Taking the left side of the K1415 + 200 high slope on the Liunan (Liuzhou to Nanning) highway as an example, the FLAC3D numerical method was first used. The influence of slope excavation on the stability of the slope and then an orthogonal test was designed to analyze the influence of anchor angle, anchor length, and anchor spacing on the safety factor of the slope during anchor support. In another study, Huang Yong changed slope safety factor under the conditions of antislide piles and excavation before the original slope construction. Monitoring points were set by the slope height. The author analyzed the horizontal and vertical displacement, and the stress of slope soil was studied with a strain increment cloud map. The results indicate that although the author’s method is difficult to construct, it is economical and safe and its coefficient is high. Finally, the support effects of the two support schemes were compared in his study [
The present study uses the BP neural network data fusion method in multisource data fusion technology to monitor and study slope safety in highway construction safety. In this paper, the data of field monitoring points are substituted into the inversion system for parameter inversion. The slope model at the monitoring points is established based on the inversion results. The analysis and calculation are compared with the measured data of the monitoring points. It is 7.53% and less than 10%. It indicates that the mechanical parameters of breccia-bearing silty clay obtained through inversion are reasonable and can be used as predictive analysis parameters of the slope stability.
Multisource data fusion is also called multisensor information fusion [
Multisource data fusion is divided into data-level fusion, feature-level fusion, and decision-level fusion according to the level of fusion [
Feature-level fusion refers to the extraction of a set of feature information from the original information provided by each sensor to form a feature vector, and the fusion of each group of information before classifying or other processing of the target, sometimes called intermediate-level fusion, a commonly used method. There are cluster analysis methods, artificial neural networks, and K-order nearest neighbor methods.
Data layer fusion structure.
From Figure
Feature layer fusion structure.
Figure
Decision-level fusion structure.
Figure
With the development of sensor technology, information fusion technology is also developing rapidly. Many scholars proposed a variety of effective information fusion methods, which are summarized as weighted average method, D-S evidence reasoning algorithm, Bayesian reasoning algorithm, Mann filter method, fuzzy logic reasoning algorithm, artificial neural network algorithm [
The process of information fusion mainly includes the following five categories:
At present, the most commonly used method for preprocessing the raw data is the weighted average method [
For the measurement equation of the sensor, the mean square error is often used as the evaluation criterion for the fusion result. The optimal weighted fusion is to find the minimum mean square error. Multiply the measurement data of each sensor by a certain weight coefficient and add them. The results of data fusion are in [
Among them,
The mean square error of the weighted estimate is
It can be known from the above formula that the smaller the measurement noise variance is, the larger the sensor weight is, and the proportion of the corresponding measurement data in the weighted estimation value is higher; on the contrary, the larger the sensor noise value is, the smaller the weight is, and the measurement data is in the weighted estimation. The proportion in the value is low. The mean square error of the optimal weighted estimate is less than the variance of the measurement noise of any sensor in the system.
An artificial neural network, which is also referred to as a neural network, is a research area used to simulate the structure and intelligence of the human brain. An important feature of it is that the output of the network is consistent with expectations through network learning ability [
Structure of BP neural network.
The first step defines network initialization. According to the network input and output sequence (
Hidden layer output calculation: according to the input variable
In the formula,
Output layer output calculation: according to the output
Error calculation: according to the predicted output
Weight update: update the network connection weights
Threshold update: update the network node thresholds
According to the requirements of the condition, determine whether the algorithm iteration ends or not. If not, return to the second step.
In the formula,
Expressway slopes are permanent slopes that are in service during operation. To ensure the safety of the slope, prevent slope instability and damage, and ensure the smooth passage of high-speed sections, the slope of the highway must be monitored for a long period. Obtain the stability of the slope for timely construction control and remediation [ Monitoring purpose: the slope monitoring can be mainly used for the following two points: First, data monitoring during the construction period will guide the data results to the construction and feedback of the construction design. Secondly, based on the monitoring data of the longer observation period of the construction period, relevant geotechnical parameters, protection measures design plan, and other data, the slope stability is analyzed and calculated, and relevant reasonable suggestions are provided for maintenance and repair work during the operation period. The monitoring points are based on the actual geological conditions of the slopes of the Chang’an Expressway and the characteristics of the existing structures and carry out horizontal displacement monitoring of the slope surface, deep displacement monitoring of the slope body, vertical displacement (subsidence) monitoring of the slope surface, groundwater level monitoring, and so forth four items. This monitoring plan is designed to arrange a total of 12 observation points for horizontal displacement of the slope, 6 monitoring points for vertical displacement (settling) on the slope surface, and 6 monitoring points for the groundwater level.
Inclinometer can measure the internal lateral displacement of rock and soil with high accuracy and is widely used for in situ monitoring of slope engineering [
When measured with an inclinometer, the error caused by the instrument itself and external influences should be reduced as much as possible. The forward displacement and reverse measurement should be used to measure the deep soil displacement of the slope once, and the measured value should be taken twice, mean of algebraic differences.
The MCU remote monitoring system can realize the automatic monitoring of surface displacement. Both the surface displacement monitoring data and the slope water level monitoring data can be transmitted to the computer through the instrument, and then the data can be summarized and processed for slope stability analysis and research.
Obtain soil deformation monitoring data through a borehole inclinometer and then arrange the deformation process curve. Demonstrate the changes and current status of deformation traits. The monitoring results are comprehensively analyzed from space, time, and environmental factors to find the cause of curve deformation, accurately explain the deformation curve, identify the stability of the soil, provide a basis for design, construction, and engineering treatment, and provide reference data for forecasting.
In slope monitoring, the occurrence of errors will inevitably affect the reliability of the inclinometer monitoring results, analyze the errors of each link, and establish a scientific and reasonable error elimination method, which will directly affect the scientificity and feasibility of the monitoring results [
Before the BP neural network performs prediction, the input and output data need to be normalized. The data is transformed to [0,1] or [-1,1]. The purpose is to eliminate the order of magnitude difference between the data in each dimension, so that each component of the neural network has the same important status, which can effectively prevent the network prediction error from being too large due to the large order difference. The formula for transforming [0, 1] interval of data is as follows:
In the formula,
BP network training once runs the training sample forward for one round and reverses the network weight. During the training process, each round of data is best selected in different orders and used repeatedly, usually training up to 10,000 times. The sample set of the network can generally be divided into two parts: one part is used as the training set, which is used for network training; the other part is used as the test set, which is used to test the neural network. To determine whether the neural network has a good generalization ability, it is tested by using a test set. If the generalization ability of the neural network is poor, it expresses that the error is small for the training set but large for the test set. When the number of nodes in the hidden layer of the neural network is constant, there exists an optimal number of times to train, at which time the network generalization ability is optimal. When the number of training times does not reach
Important functions and functions of some BP Networks.
Function name | Corresponding function |
---|---|
tansig | S-type transfer function |
purelin | Linear transfer function |
logsig | Logarithmic S-type transfer function |
deltatan | Delta function of tansig neurons |
deltalog | Delta function of logsig neurons |
learnbp | BP learning rules |
The measured deep displacement of the slope is monitored at intervals of 0.5 m. Model analysis is performed to calculate the displacement value of the number. 3 oblique tube as a network input sample set. During the trial calculation, it was found that the amount of input caused the network structure. It is too large, the network analysis time is long, and the convergence effect of the network is not obvious. This article improves the network design process and simplifies the network design. Considering that the inputs of each soil layer do not affect each other, the input layer is divided into 9 units, which is denoted as Ii (
The multisource data fusion BP neural network model is used to monitor the safety of the highway slope construction, and the monitoring data is analyzed and arranged. The test object is the construction section of a certain highway slope. The date of monitoring data is selected from mid-October to December.
In the early days, the data of 10 monitoring points were selected for analysis. Table
Parameters of silty clay with breccia.
Rock and soil layer | Bulk density (KN/m³) | Modulus of elasticity (MPa) | Cohesive force (KPa) | Internal friction angle (°) | Poisson’s ratio |
---|---|---|---|---|---|
Silty clay with breccia | 19 | 15–27 | 21.5–30 | 20–22 | 0.3 |
The measured displacement values corresponding to the different depths of the third monitoring point in the field are substituted into the trained BP neural network for inversion to obtain the soil parameters. The parameter inversion values are shown in Table
Inversion value of soil parameters.
Inversion parameters | Internal friction angle (°) | Cohesion (KPa) | Modulus of elasticity (MPa) |
---|---|---|---|
Silty clay with breccia | 21.88 | 29.55 | 26.90 |
Strongly weathered marl | 34.96 | 49.70 | 117.15 |
Analysis of the comparison between the calculated value and the measured value of the displacement at different depths at the monitoring point.
Comparison between calculated and measured displacement values at different depths of monitoring points.
Measuring point | I1 | I2 | I3 | I4 | I5 | I6 | I7 | I8 | I9 |
---|---|---|---|---|---|---|---|---|---|
Calculated value (mm) | 0.31 | 1.08 | 1.55 | 2.16 | 2.71 | 3.11 | 3.45 | 3.83 | 4.35 |
Measured value (mm) | 0.29 | 1.14 | 1.51 | 2.03 | 2.92 | 3.31 | 3.65 | 3.90 | 4.06 |
Relative error (%) | 6.70 | 5.26 | 2.65 | 6.40 | 7.53 | 6.04 | 5.48 | 1.79 | 7.14 |
To make the comparison result more intuitive, the slope construction in which the soil parameters were simulated was inverted, and the comparison between the calculated displacement values at different depths and the measured displacement values at the fourth monitoring point is shown in Figure
Analysis chart of comparison result between the calculated value and the measured value.
Combining Table
The surface displacement value changes continuously with the slope excavation. The surface displacement value is measured before the slope excavation as the initial displacement value. As the excavation progresses, the measured value of the slope displacement is compared with the initial displacement value to obtain the change in displacement. Then, select a part of the monitoring data of 10 monitoring points in the surface displacement monitoring data of a highway from mid-October to early December and make a displacement change trend chart. The results are shown in Figures
Surface displacement trend of monitoring points 1–5.
Surface displacement trend of monitoring points 6–11.
The value of the analysis of the surface displacement data of the monitored slope is the maximum value of the current displacement on the day and the analysis of the horizontal surface displacement data. The rainy weather and the infiltration of rainwater cause a significant change in the horizontal displacement. When the weather is fine, the soil surface water evaporates and the internal contraction will cause changes in horizontal displacement shrinkage. Months of August and September were in the rainy season, but due to better drainage facilities and timely drainage, the slope surface displacement did not change significantly. From November to December, weather was mainly sunny. It can be seen from Figures
With the development of large-scale highway engineering construction, under the requirements of safety production and quality supervision, the construction of a construction monitoring system can provide technical support for the project site and safety management informatization construction. It provides a strong guarantee for the safety production and emergency rescue of the engineering construction, strengthens and improves the safety awareness of construction personnel at all levels, and establishes real-time and traceable dynamic engineering archives to bring daily management work to a new level. The information-oriented construction site safety monitoring system provides a powerful technical means for improving the work efficiency of site management personnel and can bring unlimited benefits with limited investment. The present research uses the BP neural network data fusion method based on the multisource data fusion method to build a model to monitor the slope construction of the expressway.
In this paper, the method of multisource data fusion is used to determine the slope parameters based on the slope characteristics of an expressway. In situ monitoring of slope displacement and soil parameter inversion is used to determine the soil parameter values, and then the slope is calculated based on the inverse soil parameter values to research on the influence of slope design parameters and treatment measures on slope stability during construction in unexcavated sections.
This study uses a multisource data fusion method to invert the soil parameters to establish a monitoring point-slope model analyze and calculate the displacement values at different depths at the monitoring points and compare with the measured displacement values. The maximum error is 7.53%, less than 10%. The results of soil parameters are reasonable and can be used for the subsequent analysis of the stability of unexcavated slopes. In addition, this paper also analyzes the horizontal surface displacement data and concludes that the slope stability is in line with the actual situation, and the feasibility of this method is verified again. The research results in this paper play an important paradigm role in the construction safety monitoring of future expressways.
No data were used to support this study.
There are no potential conflicts of interest in the paper. All the authors have read the manuscript and approved to submit it to the journal.
This work was supported by National Key R&D Program of China (no. 2017YFC0805303).