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The incorporation of carbon nanotubes (CNTs) can enhance the mechanical properties of concrete. The stress–strain curves of CNT-reinforced concrete under uniaxial compression are investigated through an experimental program with different CNT and steel fiber proportions considered. The test results demonstrate that CNTs can increase both peak stress and peak strain, and steel fibers can further enhance the effect of CNTs. Additionally, steel fibers can effectively enhance both the strength and ductility. Theoretical models for the peak strain, initial elastic modulus, toughness index and relative absorbed energy are established. A theoretical model for the uniaxial compressive constitutive relationship of CNT-reinforced concrete considering CNT and steel fiber content is developed. Finite element (FE) modelling is developed to simulate the axial compression behavior of CNT-reinforced concrete.

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named “mode graph” in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods.

Twin-wire and arc additive manufacturing (T-WAAM) has potential advantages in improving deposition efficiency and manufacturing functionally graded materials (FGMs), thus attracting much attention. However, there are few studies on the droplet transfer mode of T-WAAM. This paper analyzes the droplet transfer mode and arc swing in the “TIG + AC” twin-wire cross-arc additive manufacturing by in-situ observation with high-speed photography, revealing what factors influence the T-WAAM on deposition shaping the quality and what are the key mechanisms for process stability. Experiments show that with the main arc current provided by TIG 100 A and the twin-wire AC arc current 10 A, three different droplet transfer modes, namely the “free transfer + free transfer, bridge transfer + free transfer, bridge transfer + bridge transfer,” can be observed with the twin wires under different feeding speeds. The corresponding deposition and arc swing are quite different in quality. Through comparative analysis, it is found that the frequent extinguishment and ignition of the arc between electrode wires is the main factor for the instability in the additive manufacturing process. The “bridge transfer + free transfer” mode can obtain a large arc swing angle and a stable deposition, in which the cross arc has a significant stirring effect on the molten pool, and the deposition shape is well-made.

The rutting depth is an important index to evaluate the damage degree of the pavement. Therefore, establishing an accurate rutting depth prediction model can guide pavement design and provide the necessary basis for pavement maintenance. However, the sample size of pavement rutting depth data is small, and the sampling is not standardized, which makes it hard to establish a prediction model with high accuracy. Based on the data of RIOHTrack’s asphalt pavement structure, this study builds a reliable data-augmented model. In this paper, different asphalt rutting data augmented models based on Gaussian radial basis neural networks are constructed with the temperature and loading of asphalt pavements as the main features. Experimental results show that the method outperforms classical machine learning methods in data augmentation, with an average root mean square error of 3.95 and an average R-square of 0.957. Finally, the augmented data of rutting depth is constructed for training, and multiple neural network models are used for prediction. Compared with unaugmented data, the prediction accuracy is increased by 50%.

Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.

Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods.

Recovery of iron from iron-rich diasporic bauxite ore via reductive roasting followed by magnetic separation has been explored recently. However, the efficiency of alumina extraction in the non-magnetic materials is absent. In this paper, a further study on the digestion of alumina by the Bayer process from non-magnetic material obtained after magnetic separation of reduced iron-rich diasporic bauxite with sodium salts was investigated. The results indicate that the addition of sodium salts can destroy the original occurrences of iron-, aluminum- and silicon-containing minerals of bauxite ore during reductive roasting. Meanwhile, the reactions of sodium salts with complex aluminum- and silicon-bearing phases generate diaoyudaoite and sodium aluminosilicate. The separation of iron via reductive roasting of bauxite ore with sodium salts followed by magnetic separation improves alumina digestion in the Bayer process. When the alumina-bearing material in bauxite ore is converted into non-magnetic material, the digestion temperature decreases significantly from 280 °C to 240 °C with a nearly 99% relative digestion ratio of alumina.