The technical orientation, pavement structure and material design scheme of the first full-scale test track (RIOHTRACK) in China, and the composition of the testing platform, including vehicle loading and data collection, etc., are introduced. RIOHTRACK has 19 kinds of designed pavement structure putting on the straight and transition curve, these pavement structures cover domestic and foreign engineering application cases, including flexible base course, semi-rigid base course and rigid base course, and different asphalt structural layers and material combination modes. The 19 kinds of pavement are classified according to the design idea and evaluation objective, and the design standard, verification objective, structure character and material technique requirement, etc. of each pavement structure are discussed separately. Using the multi-axle loading vehicle with a total weight of 100 t, the all-weather accelerated loading on the above typical pavement structures is conducted, and the performance of these pavement structures is quantified and compared. The result can be used for research of equivalent load conversion method, pavement damage prediction model, the method of structure and material optimal design, and maintenance technical standard.

In view of heavy traffic situation of asphalt pavement and the project needs in China,a kind of mixdesign method of coarse graded high modulus asphalt concrete(HMAC25)with skeleton embedded structurewas proposed. A kind of low labeled hard asphalt with penetration degree of 30(0.1 mm)(AH-30)wasused as the cementing material of high modulus asphalt concrete,and the nominal maximum aggregate size isno smaller than 26.5 mm. The asphalt-aggregate ratio of the asphalt mixture was determined by using thedesign method of asphalt mixture based on aggregate closest compact condition. The result of laboratory testindicates that(1)compared with the required value in the related specification,the high temperaturestability of high modulus asphalt concrete made with the proposed method improved by more than 7 times;(2)compared with the maximum value recommended in the related specification,the compression resilientmodulus at 20℃ increased by nearly 30%;(3)the low temperature anti-cracking performance and moisturestability can also meet the requirements of the specification.

Low-noise pavement can efficiently reduce the tire-road noise and improve the driving experience of passengers and life quality of residents along the line. Porous elastic road surface (PERS) is a type of low-noise pavement with excellent performance. This study summarizes the research status and analyzes the development trend of PERS. The research results show that PERS has a large elastic deformation capacity and a large porosity, thereby achieving elastic vibration damping and porous sound absorption. The effect is an effective way to fundamentally solve the current traffic noise problem. However, the lack of relevant research in the design theory of composite material composition, noise reduction function evaluation test method, and index system is a major factor that restricts the breakthrough of PERS in technical difficulties and the achievement of large scale engineering applications.

This study introduces the system research and technical demonstration of electrified highway and electric freight vehicle technology worldwide. In combination with China's highway freight traffic, overload of some vehicles, and the actual electrified technological situation of different traction locomotives in China, a Chinese electrified highway technical solution about dual or three source (petrol-electric hybrid)-trolley electric truck based on the overhead contact line power supply is proposed. This solution includes the electrification topological structure, highly efficient drive technology, energy intelligent management technology of online charging, intelligent control technology of pantograph, and high voltage power supply technology meeting the charging demand of heavy duty trucks on the electrified highway. These technologies have solved the key technical problems, such as large flow rate of electric trucks, small actual carrying capacity of the power supply network, and easy trip of wayside substations in China. A small-scale road test has been carried out in Suzhou, China. The study results have important guiding significance for the further research and developmental test and demonstration of electrified highway technology in China.

This study focuses on determining the electrical resistivity under acid and alkali contaminated loess and establishing a correlation between electrical resistivity with water content, composition, and concentration of contamination. Then, acid- and alkali-polluted intact loess were taken as research objects for the experiment of electrical resistivity by using the self-developed voltammetry resistivity test device. Test results show that the influencing characteristics of voltage, water content, pollution composition, and concentration on the resistivity of remolded loess were analyzed. The results reveal the major influence of acid and alkali contamination on the electrical resistivity characteristics of compacted loess. The resistivity of compacted loess decreases with the increase in water content. The resistivity is significantly reduced after being contaminated with hydrochloric acid, nitric acid, and sodium hydroxide. The rate of the decrease reduces with the increase in the concentration of pollutants. After the sulfuric acid pollution, the resistivity test result of the compacted loess sample increases with the increase in pollution concentration. A good correlation can be observed between the resistivity and the pollution degree of the Tongchuan loess used in the experiment, which is helpful for the establishment of an engineering quality evaluation system under the acid- and alkali-contaminated loess.

A concrete crack detection method suitable for various scene conditions is proposed on the basis of image recognition to complete the classification and the identification of the cracks of the collected crack images in the bridge health monitoring work conveniently and reliably. Moreover, this method can improve the crack recognition effect, which is greatly affected by the selection of the initial clustering center of the extraction algorithm, and has high environmental dependence on the image background. Convolutional neural networks (CNNs) is a representative deep learning algorithm that can characterize learning and classify input information according to its own hierarchical structure. After collecting image data on the spot by relying on the Baoxie River Bridge inspection project, Gaoxin 4th Road, Donghu High-tech Zone, Wuhan City based on the CNN, an image classification model suitable for concrete crack image classification is established. This model realizes the collection of the inspection project image classification of concrete structures in complex scenes while considering that the local cluster density and Euclidean distance of the clustering center in the traditional K-means algorithm are both large. The traditional K-means algorithm is improved by combining the use of statistical principles and morphological methods. Finally, the improved K-means algorithm completes the crack skeleton segmentation extraction and crack width calculation of crack images in complex scenes. The effectiveness of the proposed method under concrete surface peelings, stains, mosses, or other noise conditions was verified according to the successful identification of 600 crack on-site images photographed from a bridge surface. Results also show that the efficiency of the proposed crack detection method is higher than that of traditional methods. The proposed approach can also provide a reference for the in depth research on crack identification on the surface of concrete structures in complex background in the future.

The K0+080-K0+275 bid section of the underground comprehensive pipe corridor on Wanxin Road in Binhai New City located in Fuzhou was considered in this work. The mechanical properties and optimal design of the soil mixing wall (SMW) construction method piles (steel-cement mixing piles) in the comprehensive pipe corridor in the water-rich, weak stratum were studied. In the K0+080-K0+275 bid section of the comprehensive pipe gallery, different construction parameters of the steel-cement-soil mixing piles were tested through pile tests. The construction parameters of the SMW construction piles were as follows:water-cement ratio of 1.3 and cement content of 20%. The finite element software MIDAS-GTS NX was used to establish a mechanical model of the SMW construction method piles. The variations in mechanical characteristics, such as the horizontal displacement of SMW construction method piles under different supporting pile diameters, steel layouts, steel sizes, and supporting axial forces, were analyzed. The optimal design of the SMW construction method piles was also determined. Results revealed that when the pile diameter of the SMW construction method increased from 0.55 m to 0.85 m, the horizontal displacement of the pile body decreased by 28.9%. The horizontal displacement of the pile body could be effectively controlled by increasing the pile diameter. Increasing the size of the flange and web of the section steel had little effect on the deformation of the foundation pit. The flange was sensitive to the deformation of the supporting structure because it was on the stressed side. The different layout forms of the steel section exerted a great influence on the deformation of the foundation pit. The maximum horizontal displacement of the pile body for the type of steel close insertion was 18.4% less than that for the plug-one-jump-one insertion type. Increasing the supporting pre-added axial force effectively reduced the deformation of the envelope structure, and adopting the value of 50%-80% of the designed axial force was reasonable. The K0+080-K0+275 bid section of the Wanxin Road underground comprehensive corridor was determined to be supported by φ650@450 cement-soil five-axis mixing pile interpolated with HM500×300×11×18 section steel, and the layout of the section steel adopts the plug-one-hop-one type. The on-site monitoring data of the optimal construction plan were in accordance with the change law of the numerical simulation and were successfully used in the underground comprehensive pipe corridor project. Ideal results were achieved.

Urban road traffic congestion is caused by various factors, such as dynamic and real-time, complexity, and changeability, and existing methods are subjective, have low accuracy and poor real-time, and unable to automatically identify. This study proposes a multicause automatic real-time identification of urban road traffic congestion on the basis of the Bayesian network. First, the method performs systematic mechanism analysis and simulation verification on the relationship between the dynamic observable variables of urban road traffic and the multiple congestion causes to construct the Bayesian network structure. Second, the obtained measured historical data are used for parameter learning and training to obtain the complete Bayesian network model. Finally, multiple causes of traffic congestion can be automatically and simultaneously identified in real time when inputting the observable variables of traffic under road working conditions into the Bayesian network model. This method has high flexibility that can help express the correlation of nodes better and has strong interpretability, which can fully use expert experience and knowledge and achieve automatization in real time. The results in the research on Quanxiu Street, Quanzhou City show that the construction of the multicause automatic real-time identification of urban road traffic congestion based on the Bayesian network is reasonable. Five congestion causes have higher recognition accuracy rates than contrast methods, which include pedestrian influence, peak traffic, parking occupied road, unreasonable signal timing, and impact of traffic crossing the road.

An automatic video traffic state recognition method based on an autoencoder and k-means clustering is proposed to timely and effectively recognize road traffic state. First, candidate autoencoders are established through a reasonable optimization of the structural parameters on the input data dimensions, number of hidden layers, and dimensions of dimension-reduced data through cross-examination. Then, three image data sets are formed with 1500-4500 sample images. On this basis, the candidate autoencoders are trained and tested; thus, the best autoencoder AE^* is proposed according to precision, recall, and F1-value. Lastly, four traffic state recognition models are constructed by combining AE^* with k-means clustering, Support Vector Machine (SVM), Linear Classifier, and DNN Linear Classifier (Deep Neural Network with Linear Classifier), which are named as AE^*-kmeans, AE^*-SVM, AE^*-Linear, and AE^*-DNN_Linear, respectively. The models are trained and tested on the basis of the three image data sets. Results show that the four models' average precision in terms of precision and recall is 91.9%-92.7%, and their average recall is 91.6%-92.6%, while AE^*-kmeans performs best or second to best in terms of precision and recall. With regard to the comprehensive evaluation index F1-value, AE^*-kmeans achieves 92.4%, a little lower than 92.7% of AE^*-SVM, and better than AE^*-DNN_Linear (92.1%) and AE^*-Linear (91.8%). Given that k-means is an unsupervised clustering method, compared with AE^*-SVM, AE^*-Linear, and AE^*-DNN_Linear, AE^*-kmeans can reduce the workload, such as manual data calibration and supervised training and cut down calculation cost. Meanwhile, AE^*-kmeans also obtains a good traffic state recognition result. Therefore, this mechanism has high practical significance for an accurate real-time extraction of a video traffic status.

To build the Spring Festival travel OD matrix of the Spring Festival of 2019 based on Baidu's inter-provincial migration data, China's inter-provincial passenger travel time and space features, the network passenger flow, and the characteristics of inter-provincial travel in different periods of Spring Festival travel in different provinces are analyzed by indicators, such as the volume of inter-provincial travel, inter-provincial migration, inter-provincial migration and net migration, the horizontal contrast of the characteristics of inter-provincial travel scale of 34 provincial administrative units across the country. The research conclusions are as follows. The inter-provincial travel of tourists has significant regular characteristics before and after the Spring Festival. The scale and time distribution characteristics of the rework flow before and after the Spring Festival can be used as an important basis for studying the social and economic activity levels based on the Spring Festival travel rush; 15 provinces with net inflow and 19 provinces with net outflow were identified. Among them, 10 provinces, such as Yunnan and Gansu, had average daily net inflows/outflows of less than 3000 people, which were relatively balanced. The 40-day Spring Festival travel period is divided into five periods according to the law of inter-provincial passenger flow. Different periods have different characteristics of passenger travel scale, providing a basis for the prediction of passenger flow and transportation capacity organization. Ten popular lines of inter-provincial travel before and after the festival were identified, indicating that the inter-provincial travel of Spring Festival passengers has an obvious spatial directivity, and the start and end of travel before and after the Spring Festival, the outflow city from the beginning or end of travel, and the inflow of the source city present a symmetric relationship. The analysis of Guangdong, Anhui, and other provinces with a large population mobility shows that they have the closest relationship with the travel of surrounding provinces, and the surrounding provinces with the top three inter-provincial trips account for more than 50% of the inter-provincial trips.

To address the growing pollution problem of vehicle exhaust on expressways, a calculation method for the emission and diffusion of vehicular pollutants in expressway network of Jiangsu province was proposed based on the emission inventory of road vehicular pollutants and the CALINE-4 diffusion model. The characteristics of the spatial-temporal distribution were analyzed. The following conclusions can be drawn:(1) In the province's expressway network with various vehicle styles and structures, the largest contributor of CO and HC emissions was Coach Model I, contributing 41.26% and 44.32%, respectively; the largest contributor of NO_x and PM_2.5 emissions was Truck Model V, contributing 33.32% and 32.52%, respectively. (2) From the vehicle emission level, for Coach Model I, the contribution rate of the national stage I-III vehicle emission standards to the total CO and HC emissions reached 73.56% and 62.05%, respectively; for Truck Models IV and V, the contribution rate of the national stage III vehicle emission standard to the total NO_x and PM_2.5 emissions reached 58.73% and 63.25%, respectively. (3) Except for NO_x, the diffusion concentration of vehicular pollutants (CO, PM_2.5, and PM₁₀) in each section of the predicted points was lower than the 1-hour average limiting value taken as Grade II on the basis of the Ambient Air Quality Standards (GB3095-2012), which is 10 mg/m³, 75 μg/m³, and 150 μg/m³. (4) From the temporal distribution, the amount of various pollutants showed a double-hump trend, which occurred at 9-10 a.m. and 2-3 p.m. The hourly diffusion concentration of various pollutants in all sections of the province was higher at night (10 p.m. to 4 a.m. the next day) than in the daytime. The proportion of sections exceeding the limit of NO_x diffusion concentrations at night was as high as 75.2%, reaching the peak value at 2-3 a.m. (5) From the spatial distribution, the total CO and HC emissions in the North Network were less than that in the South Network, and the NO_x, PM_2.5, and PM₁₀ emissions in the North Network were higher than those in the South Network, while the emission intensity of all kinds of pollutants in the North Network was significantly higher than that in the South Network. The 1-hour average NO_x concentration in Northern Jiangsu was higher than that in Southern Jiangsu and Middle Jiangsu, with the maximum value of all periods of the day appearing in the section of "Duanzhai Hub-Xinyi Dong" and the minimum value mainly distributing in Middle Jiangsu.