Case study on deformation control of upper-soft and lower-hard large span tunnel station using combined control technology and monitoring demonstration

A large number of shallow buried tunnels are built in the city nowadays and the special strata such as large upper-soft and lower-hard ground often encountered. Deformation control of strata is the focus issue related to the construction safety. Based on Dalian metro Hing Street station with the classical geological condition of upper-soft and lower-hard ground, this paper fully used a combined control method including six different support measures to control the deformation of surrounding rock. 3D finite element model was setup to analyze the construction effect of combined control measures and the monitoring in-site was carried out to verify the deformation control effect of combined control method. It shows that the maximum surface subsidence value is gradually reduced with the support measures gradually increasing. In the case of various supports the maximum sedimentation value is 2.67 cm, which is 42. 1% lower than that of not using control method and the control effect is obvious. In addition, it can be seen that the two-layer initial support and additional large arch foot have the best effect on controlling the ground surface settlement with reduction of 11.7% and 20.2%, respectively. The research results can provide practical experience for the construction of such tunnels, and guide the design and construction of the tunnel in the future

Study on the relation between mineral compositions of rock and construction characteristics of tunnel in cold regions: a case

Mineral composition of rock has a very important influence on the physical and mechanical properties of tunnel surrounding rock. Take Dangjianshan tunnel in cold regions for example, the rock specimens in different parts of tunnel were taken to carry out the detection test of mineral composition. By the detail qualitative and quantitative analysis, the relationship between mineral composition and surrounding rock engineering properties was explored. First of all, the composition and content of minerals contained in in the rock specimens were detected by X ray fluorescence spectrometer and X ray powder diffraction. The detection results show that rock of tunnel contains high hardness minerals such as quartz and feldspar which were proven by initial engineering geological investigation report, in addition, it also contains several kinds of low hardness minerals including inclined chlorite and illite which may exhibit large deformation characteristic of soft rock after the tunnel excavation in case of meeting water and weathering conditions. The total content of inclined chlorite and illite accounted for a considerable component in main tunnel, inclined shaft and parallel pilot respectively and the influence on surrounding rock engineering properties cannot be ignored. Therefore, mineral composition detection must be paid attention to after tunnel excavation. Secondly, the effects of mineral composition on surrounding rock were analyzed in aspects of rock strength, weathering resistance, water softening property and excavation deformation through comparing the rock samples in different parts of tunnel. The comparative results showed that when the mineral contents is high with high hardness and poor hydrophilicity, tunnel surrounding rock plays a better performance of physical and mechanical properties, vice versa. Finally, according to the specific geological and construction parameters of the tunnel, the correlation analysis was studied about the vault settlement after tunnel excavation and the hydrophilicity mineral content in main cave. The logarithmic relationship between them was found and the correlation coefficient was 0.98. It can provide a useful reference for the settlement prediction of Dangjinshan tunnel construction

Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images

Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society. Advanced image semantic segmentation models, such as DeepLabv3+, have achieved astonishing performance for semantically labeling very high resolution (VHR) remote sensing images. However, it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation results. Consequently, this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model (DeepLabv3+) and object-based image analysis (OBIA), wherein DSM is employed to provide geometric information to enhance the interpretation of VHR images. The proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+ network on spectral image and a random forest (RF) classifier on hand-crafted features, respectively. These two predictions are then integrated by Dempster-Shafer (D-S) evidence theory to be fed into an object-constrained higher-order conditional random field (CRF) framework to estimate the final semantic labeling results with the consideration of the spatial contextual information. The proposed method is applied to the ISPRS 2D semantic labeling benchmark, and competitive overall accuracies of 90.6% and 85.0% are achieved for Vaihingen and Potsdam datasets, respectively

Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs

Numerical Investigation of the Undrained Compression and Pull-Out Capacity of Suction Foundations in Clay

This paper presents the results of three-dimensional finite difference analysis of suction foundations in uniform and non-uniform clays under undrained conditions. The Tresca criterion was used to simulate the stress-strain response. The bearing capacity of the foundations was investigated, with the degree of nonhomogeneity (kD/sum) of soil varying from 0 to 5, and the embedment depth being up to four times the foundation diameter. The end bearing capacity factor in compression and the reverse bearing capacity factor in tension were both calculated and were compared with each other under different foundation displacements. Numerical results showed that the ultimate bearing capacity factor can have the same value in cases of both compression and tension. The recommended ultimate bearing capacity factor is determined on the basis of the embedment ratio and displacement magnitude, and the displacement is not more than 30% of the foundation diameter. Finally, two equations are proposed to evaluate both the bearing capacity factor and the effective depth factor

Large-scale urban functional zone mapping by integrating remote sensing images and open social data

Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping integrating these two types of data is still not be applied. In this study, a novel approach to mapping large-scale UFZs by integrating remote sensing images (RSIs) and open social data is proposed. First, a context-enabled image segmentation method is improved to generate UFZ units by incorporating road vectors. Second, the segmented UFZs are classified by coupling Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). In the classification framework, physical features from RSIs and social attributes from POI (Point of Interest) data are integrated. A case study of Beijing was performed to evaluate the proposed method, and an overall accuracy of 85.9% was achieved. The experimental results demonstrate that the presented method can provide fine-grained UFZs, and the fusion strategy of RSIs and POI data can distinguish urban functions accurately. The proposed method appears to be promising and practical for large-scale UFZ mapping

Comprehensive Evaluation of Very Thin Asphalt Overlays with Different Aggregate Gradations and Asphalt Materials Based on AHP and TOPSIS

Very thin asphalt overlays (VTAOs) have been widely used as a cost-effective preventive maintenance measure in various countries. However, because of the complex combinations of aggregate gradations and asphalt materials, the selection of VTAOs is an unsolved problem that is extremely important for pavement management authorities. Therefore, this study proposed a comprehensive evaluation method for VTAOs based on the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). Three VTAO mixtures comprising different aggregate gradations (stone mastic asphalt (SMA), open-graded friction course (OGFC), and asphalt concrete (AC)) and different asphalt materials (organic silicon (OS) and styrene-butadiene-styrene (SBS)) were investigated and preliminarily compared in the laboratory. Subsequently, four road performance indicators (pavement condition indicator, British pendulum number, texture depth, and international roughness index) were selected as the evaluation indices, and their weights were calculated using the AHP according to the questionnaires collected from specialists. Finally, the field test data of the road performance indicators with scale confusion were handled using TOPSIS, and the closeness was considered as the final evaluation criterion. The results indicated that the mixture of AC and SBS exhibited the best performance among the three investigated mixtures. Categorizing the evaluation indicators into two aspects—the strength aspect and the structural aspect—it is found that the strength aspect of a VTAO is mainly affected by the asphalt materials, whereas the structural aspect of a VTAO is mainly affected by the aggregate gradation. This study provides a practical method for evaluating the road performance of VTAO with diverse measurement indices, as well as a quantitative scope for the impacts of the aggregate gradation and asphalt materials on the road performance

A Building Extraction Method via Graph Cuts Algorithm by Fusion of LiDAR Point Cloud and Orthoimage

An automatic building extraction method based on graph cuts algorithm fusing LiDAR point cloud and orthoimage is proposed.Firstly,three geometric features are computed from LiDAR points including flatness,distribution of normal vector and GLCM (grey level co-occurrence matrix) homogeneity of normalized height.NDVI is simultaneously calculated from orthoimage.After that,both kinds of features are combined to construct the data term of energy function,then DSM and NDVI is combined to construct smooth term.Thereafter,graph cuts algorithm is applied to obtain the initial building extraction results.Finally,foreground and background segmentation method is employed to optimize the building boundary based on the orthoimage color information in certain range of the initially detected building boundary.ISPRS Vaihingen dataset is used to evaluate the proposed method.The results reveal that the proposed method can obtain high accuracy of the detection building area