Search

A full-scale hybrid beam consisting of a UHPC slab and FRP truss girder was fabricated. The novel side plate FRP joint characterized with improved load-carrying capacity, stiffness, and preferred failure mode along with the tailored shear connector validated in the previous studies of the authors were adopted. Its flexural performance was characterized and compared with that of hybrid beams employing NC or UHPC slab but I-profile girder. The failure of the proposed hybrid beam subjected to bending was pseudo ductile whereas those of the other two hybrid beams were brittle. The load-carrying capacity and stiffness of the proposed hybrid beam outperformed the other two hybrid beams with comparable dimensions and material properties.

AbstractPrevious studies on carbon storage simulation had ignored the difference of carbon intensity among various vegetation types inner the same land use. In this paper, The PLUS model was used to predict the land use change under multi-scenarios from 2030 to 2060, and the vegetation type data were supplemented by CA model to obtain the land cover-vegetation datasets from 2030-2060. Combined with the carbon density table of vegetation type, the future land use carbon storage during 2030-2060 under multi-scenarios in Beijing-Tianjin-Hebei region were analyzed. The main conclusions were as follows: (1) The spatial distribution of carbon storage in Beijing-Tianjin-Hebei region showed a pattern of ‘high in northeast-southwest and low in southeast-northwest’; (2) The carbon storage in Beijing-Tianjin-Hebei region during 1990-2020 showed a decreasing trend; (3) During 2030-2060, the carbon storage in Beijing-Tianjin-Hebei region showed a continuous decreasing trend in the absence of policy intervention, while that under the ecological protection and farmland protection scenarios showed an increasing trend; (4) Under different development scenarios, there were obvious significances of carbon storage in spatial distribution.

Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km2, including an increase of 222.63 km2 in natural wetlands and 87.27 km2 in artificial wetlands. In the conversion between wetland types, 218.73 km2 of artificial wetlands were converted into natural wetlands, and 75.18 km2 of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon’s diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management.

This study optimized the slope and slope length factor (LS) and crop management factor (P) of the RUSLE model and then introduced the gravity centre model to analyze the spatial–temporal variation patterns of soil erosion in Yellow River Basin from a new perspective. Results showed that: (1) The improved model of RUSLE with optimized factors of LS and P had better applicability in Yellow River Basin; (2) The average erosion intensity was 2777.5 t/a, which belonged to moderate erosion. The soil erosion intensity of the Yellow River Basin showed an overall trend of increasing firstly (1990–2005) and then decreasing (2005–2015). (3) During 1990–2015, the gravity centre of soil erosion moved to the southwest, indicating that the increment and increasing rate of soil erosion in the southwest parts of the Yellow River Basin were greater than that in the northeast parts. (4) The intensity of soil erosion aggravated with the increasing slope. The sandy soil, chestnut soil, light-grey calcium soil and fluvo aquic soil had severe erosion intensity due to the regional climate and their own physical–chemical structure. The woodland and shrubbery land were more susceptible to soil erosion.

Based on the datasets of MODIS NDVI and SPOT VETGETATION NDVI from 1999 to 2018, this study has introduced the gravity centre model and time-lag analysis model to explore the spatial–temporal change patterns of the NDVI in China in different sub-regions and its response to climate factors. Results showed that: (1) In 1999–2018, there was an increasing trend in vegetation coverage in China on the whole, with a larger increase occurring in central south and northeast China. (2) The NDVIs of six land use types showed a similar change trend with temperature and precipitation with different time lags. (3) The maximum correlation coefficients between the NDVIs of different land types and climate factors appeared at Lag 0–1 (current month and previous one month) and Lag 0–2 (current month, previous one and two month), while the correlation coefficients were the lowest at Lag 3 (previous three month). (4) During the past twenty years, the gravity centres of the NDVI, temperature and precipitation moved northward as a whole with similar migration trajectories. These results could provide important decision supports for vegetation restorations and ecological protections in different sub-regions of China.

With the development of information and communication technologies, the current intelligent transportation systems (ITSs) will gradually become automated and connected, and can be treated as autonomous transportation systems (ATSs). Function, which unites cutting-edge technology with ATS services as a fundamental component of ATS operation, should be categorized into function domains to more clearly show how ATS operates. Existing ITS function domains are classified mostly based on the experience of experts or the needs of practitioners, using vague classification criteria. To ensure tractability, we aim to categorize ATS functions into function domains based on text analysis, minimizing the reliance on subjective experience. First, we introduce the Latent Dirichlet Allocation (LDA) topic model to extract text features of functions into distribution weights, reflecting the semantics of the text data. Second, based on the LDA model, we categorize ATS functions into twelve function domains by the k-means method. The comparison between the proposed function domains and the existing counterparts of other ITS framework demonstrates the effectiveness of the LDA-based classification method. This study provides a reference for text processing and function classification of ATS architecture. The proposed functions and function domains reveal the objectives in future transportation systems, which could guide urban planners or engineers to better design control strategies when facing new technologies.

Faced with the rapid growth of vector data and the urgent requirement of low-latency query, it has become an important and timely challenge to effectively achieve the scalable storage and efficient access of vector big data. However, a systematic method is rarely seen for vector polygon data storage and query taking spatial locality into account in the storage schema, index construction and query optimization. In the paper, we focus on the storage and topological query of vector polygon geometry data in HBase, and the rowkey in the HBase table is the concatenation of the Hilbert value of the grid cell to which the center of the object entity’s MBR belongs, the layer identifier and the order code. Then, a new multi-level grid index structure, termed Q-HBML, that incorporates the grid-object spatial relationship and a new Hilbert hierarchical code into the multi-level grid, is proposed for improving the spatial query efficiency. Finally, based on the Q-HBML index, two query optimization strategies and an optimized topological query algorithm, ML-OTQ, are presented to optimize the topological query process and enhance the topological query efficiency. Through four groups of comparative experiments, it has been proven that our approach supports better performance.