In this paper, the distributed optical fiber sensing technology is introduced in response monitoring of asphalt pavement under loading. The corresponding immersion monitoring scheme is proposed. To study the change rule of optical fiber sensing signal in asphalt concrete at different loadings and temperatures, a distributed optical fiber sensing testing system is established. The system includes the light source, sensing element, asphalt concrete structure, a photoelectric detection module, and a data acquisition module. The encapsulated optical fiber sensor is embedded in the asphalt concrete rutting specimen, and the indoor Hamburg rutting test method is employed. Under different temperatures and different size loads, the rutting specimens are loaded with periodically moving rubber wheels to simulate actual asphalt road traffic loads. Taking the optical fiber signal sensitivity and signal strength as evaluation indicators, the acquired optical fiber signal is defined in two stages:no-load stage and loading stage. The quantitative relationship between fiber signal characteristics, load magnitude, and ambient temperature in two stages are studied. The results show that under the Hamburg rutting test, the fiber signal is steady, and information like the loading characteristics and period characteristic can be obtained from the fiber signal curve. Under 30℃, 45℃and 60℃, the fiber optic signal sensitivity and load magnitude are linearly correlated. Under the conditions of 45℃ and 60℃, the increasing speed of the fiber signal sensitivity with loading is larger than that at 30℃. Compared with high temperature (45-60℃), the fiber signal sensitivity value increases faster at low temperatures (30-45℃). The higher the temperature, the more dispersed the optical fiber signal sensitivity value distribution. The research results are of great significance to verify and improve the distributed optical fiber technology system in applications of road engineering.

In order to the prevent and control construction dust, and to avoid secondary pollution, on the basis of single factor experiment, the viscosity, surface tension, determination of water retention, and water absorption rate test were carried out in this paper, hydroxyethyl cellulose and hydroxypropyl methyl cellulose were selected as adhesive and water retention agent, Y1 and J1 were selected as surfactant and water absorption from straw extract of a variety of functional raw materials. Taking the viscosity of solution, pH value, water loss rate at high temperature, shell compressive strength and infiltration time as the assessment indexes, a four-factor and three-level orthogonal test was designed to optimize each component of the dust suppressant. A green, environmentally friendly and degradable straw dust suppressant was prepared, and the pilot test was carried out in the construction site of expressway. The orthogonal test results show that the influence degree of mass concentration of the four factors on the performance of straw utilization type dust depressant is as follows:hydroxyethyl cellulose has the greatest influence, followed by J1, and hydroxypropyl methyl cellulose and Y1 have the least influence. When hydroxyethyl cellulose, hydroxypropyl methyl cellulose, J1, Y1 mass concentration 0.35%, 0.03%, 0.05% and 0.03% respectively, the straw utilization type dust suppression agent viscosity value is 106.3mpa·s, and water-retention rate is 22.51%, pH value is 7.23,and has the compressive strength of 79.02 KPa and the infiltration time of 4.21 min, its density is 1.030 g/cm, Product viscosity is moderate, it spray easy, surface tension is small, meet the requirements of the current class of coal dust suppression agent products indicators. Highway field pilot test shows that, the surface have the shaw hardness of 70HA of shell in four hours after the construction site is sprayed biological dust suppressant in accordance with the dosage of 3 L/m², the shell can effectively stop the rain and wind erosion, PM2.5 and PM10 dust suppression rate are 91.2%, 91.4% and 79.2% and 77.7% in the 1 d and 7 d respectively, have very good inhibition effect on construction dust.

To obtain the action force between the reinforcing material and filler in reinforced soil, many scholars at home and abroad have conducted pullout tests, which are limited by the testing apparatus. Most of them have studied the pullout force between a biaxial geogrid and sand. The pullout force formula is generally believed to be composed of the surface friction and lateral resistance of transverse ribs, and uniaxial geogrids have rarely been researched experimentally on the basis of the pullout force between them and the expansive soil filler due to the large distance among transverse ribs. This study draws on existing methods to determine the pullout force, conducts a preliminary analysis, proposes a formula for calculating the maximum pullout force, uses a self-developed large-scale CNC pullout test system, and utilizes its large size, bidirectional airbag loading, and ability to eliminate side wall friction. The design of the test scheme is optimized to obtain two important calculation parameters in the formula:one is to cut grids of different lengths to provide lateral resistance and then implement them to obtain the coefficient of sliding friction between grid and fill; the other is to develop different numbers of horizontal ribs in the pullout test of a grille, and the lateral resistance of the end bearing of a single transverse rib is measured. The results of the lateral resistance of the end bearing based on the analysis of the three mechanisms of shear, punching, and Prandtl failures are compared. The lateral resistance of the lateral rib end bearing measured from the test is proven reasonable. Two types of expansive soils reinforced by different types and sizes of grids are analyzed with the calculation formula of the maximum pullout force, and the coefficient of pullout friction is determined to be rational. The research results can be used to guide the design and construction of uniaxial geogrid-reinforced expansive soil and the modification and upgrading of products by grid manufacturers.

Cable Tower Grouping-Anchorage-System of Cable-Stayed Bridges is a new type of anchorage system, which is only used in Chi-Zhou bridge in An Hui province in domestic. In order to understand the behavior characteristics and the design methods, took Chi-Zhou Bridge as an example. Firstly, the propaedeutics were introduced, and then the finite element models of the whole bridge were built in six different cases and parameters sensitivity were analyzed which included cables layouts, agglomeration forms and steel beams design. Then took introduced the beam-column stiffness ratio, studied the stiffness matching between concrete tower and steel beam and clarified the influence of grouping, agglomeration and stiffness ratio on the whole bridge. Some design suggestions were stated. The results showed that the Grouping-Anchorage-System is more advantageous on the lateral stability and torsion resistance of the whole bridge; The steel beams are the key point of design because of the stress complexity. Changing the length of the steel beam will bring about changes in the local stress and changing the space between steel beams will bring about changes in the whole bridge. Considering the layout spaces and detailing requirements, the length of steel beams should be reduced as much as possible, so as to improve the efficiency of stay cables, which is beneficial to the whole bridge stress and economy.

Artificial method is the main method of tunnel lining structure safety detection at present, which has strong subjectivity, large amount of labor and low efficiency, and the adverse environment of the tunnel is harmful to the health of detection workers. Tunnel machine detection methods can effectively overcome the shortcomings of artificial detection methods, and provide more abundant quantitative data. In this paper, the development of tunnel detection equipment at home and abroad in recent years is introduced, and the deficiencies in technology and function of current tunnel machine detection system are analyzed. Finally, based on the shortcomings of the current machine detection system, the future development of tunnel detection technology is prospected from the aspects of dual-purpose detection technology, full-automatic robot detection technology, dynamic intelligent detection platform and virtual reality detection technology. This review can provide some reference and guidance for the development of tunnel machine detection equipment in the direction of full automation, intelligence, multi-function, big data, high efficiency and high precision.

At present, the "green channel" policy is fully implemented. However, the detection is relatively lagging, and truck drivers are susceptible to fake the toll-free logistics vehicles (TFLVs), causing huge losses to the operation. In order to improve the accuracy and efficiency of TFLVs inspection, this paper establishes a toll evasion prediction model for fake TFLVs based on the historical TFLVs traffic dataset derived from the highway network toll collection system. First, according to the importance and reliability, we used the data mining technology to differentiate and extract the data attributes. And the spatiotemporal characteristics and other characteristics of fake TFLVs through-traffic entering and exiting toll booths are studied and analyzed based on the preprocessed data. Then use the Borderline-SMOTE oversampling method to balance the pass data set, use the ChiMerge algorithm to discretize continuous attributes. In order to ensure the effective matching and the correlation between the large contribution attributes and the results, correlation item test and collinearity test are used for discrete related attributes. Finally, adopting the processed discrete TFLVs data which passed correlation item test and collinearity test, and using the decision tree to establish the prediction model. The classification results of the escape behavior prediction model and other models are compared by using the historical TFLVs data set. The results show that the accuracy of the escape behavior prediction model proposed in this paper is 83.4%, which is higher than that of the Logistic regression model (61.8%) and the Random forest model (81%).This research model can effectively warn of fake TFLVs, and on the basis of simplifying the inspection process of TFLVs, reducing the probability of the occurrence of fake TFLVs evasion, which has practical application significance.

In recent years, with the continuous increase of the number of motor vehicles and non-motor vehicles, in the design of most intersections today, the previous design patterns are not enough to meet the composition characteristics of the current traffic flow. Therefore, the optimization of the existing right of way is particularly important. This paper summarizes the travel mode structure and traffic flow composition characteristics of small and medium-sized cities in southern China, investigates a large number of intersections in Guangxi, takes the intersection of Changhu in Nanning as an example, studies the main problems existing in the current distribution of right of way, and puts forward effective countermeasures for it. After the simulation by using VISSIM and comprehensive analysis, the average vehicle delay at Changhu intersection has been reduced by 19.8%, traffic efficiency has been improved, and the existing traffic problems caused by right-of-way conflicts have been solved.

The entries of off-street parking lots are the connecting points of urban dynamic traffic and static traffic. When there are signal control, bus stops, and other traffic facilities in the entrance affected area, the traffic situation is complicated, and traffic jams are likely to occur. Based on probability theory, we establish a probability model for the conflict between different traffic flows in the area affected by the entry of the off-street parking lot under complex conditions. Based on this model, considering the impact of different traffic flows during operation, the queuing model and the stopping delay model were established respectively. Based on this model, the mutual influence of different traffic flows is revealed and the queuing model, along with the stopping delay model, is established. Based on the field survey data, we calibrate the key parameters of the model and verified the validity of the model. The result shows that the model is suitable for the analysis and evaluation of the traffic operation status of the entrance of the parking lot under different conditions, and provides a reference for the actual project.

Ultra wide band (UWB) has emerged as a promising fuction in the field of indoor positioning. The geometric position relationship between UWB base station and the measured target affects positioning accuracy directly. Based on the basic principles of UWB positioning system, this paper takes the horizontal dilution of precision (HDOP) as the evaluation index of positioning accuracy, and proposes the layout optimization method of UWB base station under both ideal condition and actual conditions through simulation and practical experiment verification. The three-stage design method for layout optimization of UWB base station in underground parking lots is developed, which provides a scientific workflow for the practical application.

In extreme bad weather conditions, vehicles can timely adjust the speed and distance according to the road information, which will effectively reduce the occurrence of traffic accidents. Road type and road adhesion coefficient are the main factors affecting road condition. The traditional identification method of road adhesion coefficient has the characteristics of high cost and low reliability. At the same time, the road surface information recognition technology based on machine vision has become a hot spot in the current research, but the low accuracy and poor robustness are always the difficulties in the research. Based on the semantic segmentation model which is popular in machine vision, a new pavement type recognition technology is proposed by improving the model output network. According to the existing literature, vehicle road surface can be divided into the following 9 categories, including wet asphalt road, dry asphalt road, wet concrete road, dry concrete road, wet soil road, dry soil road, gravel road, compacting snow road and icy road. The standard data set is made by collecting road pictures through taking photos, downloading and other ways, and the pre-processed data set is used to train the improved semantic segmentation network model. Through many tests, training parameters that achieve the desired effect are selected for model parameter solidification, and the semantic segmentation model after solidification is used to predict the road image acquired by the camera. According to the prediction results of the model, the categories of the current driving road surface are obtained. The test results of a large number of road images show that the average classification accuracy of the nine road types is about 94%, which effectively improves the identification accuracy and robustness of the current road types. At the same time, the predicted time of a single frame image on a specific test platform is about 0.028 6 s, which meets the real-time requirements.

In order to study the quantitative evaluation of the effectiveness of intelligent vehicle under test conditions, the risk model based on driving risk field was improved, and the complexity degree of each elements of driving environment was defined. Considering risk degree and complexity degree coverage, maximum and distribution of user and test conditions, an evaluation model of user and test conditions based on risk degree and complexity degree was constructed. The validity of three tests was analyzed and evaluated by examples. The results showed that the effectiveness index could be used to evaluate the effectiveness of the test conditions quantitatively.

In order to study the influence of Taihang Mountain Expressway on the tourism industry pattern of Taihang Mountain area in Hebei Province, the vector data of Taihang Mountain Expressway, the data of 43 exit nodes along the Expressway and the data of 76 scenic spots above 3A level are collected and sorted out. The unique association of the scenic spot-expressway exit node is constructed, the sum of the fuzzy contact intensities of the scenic spot-expressway exit nodes and the sum of the fuzzy contact intensities of the associated scenic spot nodes of each expressway exit are calculated, the fuzzy agglomeration of each exit node is calculated, and the spatial tourist grouping scheme based on COP objective function is analyzed and adjusted by the using the improved F-AMST model (designed with twice-splitting and twice-clustering). The result shows that (1) under the influence of Taihang Mountain Expressway, the result of spatial tourist grouping in this area includes 4 primary groups and 8 secondary groups; (2) by comparing with the results of previous studies of the division of mountainous tourism agglomeration areas in Hebei Province, the result of spatial tourist grouping is basically consistent with those of mountainous tourism agglomeration areas, but the fuzzy clustering intensity of spatial tourist grouping is significantly better than that of agglomeration division, that is, the completion and opening of Taihang Mountain Expressway enhances the agglomeration among scenic spots; (3) under the influence of Taihang Mountain Expressway filtration effect,there are 18 "vacuum filtration" nodes without scenic area connection and 3 "secondary filtration" nodes with less fuzzy association intensity; (4) Taihang Mountain Expressway provides opportunities and possibilities for the formation of new tourist destination geospatial patterns, the grouping of cross-administrative region boundary, and the combination of "fast-slow" combined transport network system.