In this paper, a novel numerical method for random generation of aggregates in concrete is presented. Compared with some other models, the aggregates model generated by this method offers a better approximation to the...
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In this paper, a novel numerical method for random generation of aggregates in concrete is presented. Compared with some other models, the aggregates model generated by this method offers a better approximation to the geometric shape of real aggregates and the target gradation. The research incudes that (1) a surfacereconstruction method is developed first on the basis of the implicit T-spline surfacealgorithm through defining a modified knot vector, determining the off-set point locations and their sign distances, which aims at avoiding occurrence of unclosed curved surface or spurious sheets and reconstructing accurately the complex surface according to a set of scattered surface points of a gravel aggregate;(2) a sinusoidal generatrix function, of which the amplitude represents the aggregate size range and the period reflects the aggregate flatness, is proposed to auto-generate a set of scattered points on the surface of a gravel aggregate. Further modifications are made to improve the quality of the scattered points that agree well with the real crushed aggregate surface;(3) An efficient aggregate packing method is proposed by combining and modifying the "occupation and removal method" and the "layering disposition method" to improve the efficiency of aggregate packing;and (4) a MATLAB computing program for auto-generating aggregates in concrete is developed and validated by examples. The simulation results have demonstrated that the method presented in this paper can generate aggregates for any given mix proportions and gradations and the method can serve as an effective tool for numerical evaluation of mechanical properties of concrete materials. (C) 2019 Elsevier Ltd. All rights reserved.
Detection of traffic signs and light poles using light detection and ranging (LiDAR) data has demonstrated a valid contribution to road safety improvements. In this study, the authors propose a fast and reliable metho...
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Detection of traffic signs and light poles using light detection and ranging (LiDAR) data has demonstrated a valid contribution to road safety improvements. In this study, the authors propose a fast and reliable method, which can identify various traffic signs and light poles in mobile LiDAR data. Specifically, they first use the surface reconstruction algorithm to extract the normal vectors of the points as one of the characteristic features and apply k-means on the characteristic features of the points to automatically segment the data into road or non-road points. They then employ sliding cuboids to search for high-elevated objects that are located near the borders and on top of the road points. They further employ the random sample consensus algorithm to remove outliers and keep the points that fall on the perpendicular planes to the road trajectory. Finally, they introduce a modified seeded region growing algorithm to remove noisy points and incorporate the shape information to reject the false objects. A set of extensive experiments have been carried out on the datasets that are captured by Utah Department of Transportation from I-15 highway. The results demonstrate the robustness of the proposed method in detecting almost all traffic signs and light poles.
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