Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge.

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this article, we propose ConceptThread, a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, speech recognition and topic modeling, to extract core knowledge concepts and construct the hierarchical and temporal relations among them. Then, by using a metaphor of thread, we present a novel visualization to intuitively display the concepts based on video sequential flow, and enable learners to perform interactive visual exploration of concepts. We conducted a quantitative study, two case studies, and a user study to extensively evaluate ConceptThread. The results demonstrate the effectiveness and usability of ConceptThread in providing online learners with a quick understanding of the knowledge content of MOOC videos.

Subspace clustering has attracted much attention because of its ability to group unlabeled high-dimensional data into multiple subspaces. Existing graph-based subspace clustering methods focus on either the sparsity of data affinity or the low rank of data affinity. Thus, the quality of data affinity plays an essential role in the performance of subspace clustering. However, the real-world data are generally high-dimensional, complex, and heterogeneous multi-source data, so that the data affinity learned by these methods cannot be completely dependent. Moreover, since these approaches always ignore the intrinsic structure of data, their grouping effect is relatively low. In this paper, we propose a novel unsupervised algorithm, called Structure-Aware Subspace Clustering (SASC), to address the above issues. SASC considers local and global correlation structures simultaneously to capture the intrinsic structure. Further, it integrates the captured structure into representation learning to gain a relatively precise data affinity. It is powerful to promote an all-around grouping effect and enhances the robustness and applicability of subspace clustering. Experiments on various benchmark datasets, including bioinformatics, handwritten digit, object image, and speech signal, demonstrate the effectiveness of the proposed algorithm.

Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating from user requirements and impede personalized geospatial analysis in specific application scenarios. In this article, we propose a user-driven sampling model for the visual abstraction of the large-scale geographical point data based on convolutional neural networks (CNN). First, a blue noise sampling model is applied to partition the geographical space into local areas, and a set of visual interfaces are designed to present the data features of those points in the local areas, enabling users to visually select representative points according to their requirements. Then, user preferences are quantified with a CNN model based on the eigenvectors of the representative points, which are further utilized to guide the sampling courses of the other local areas. Thus, all the sampled points will retain the spatial distribution of original data points and fulfill the user preference as far as possible. In addition, we implement a visualization framework to integrate manual point selection, CNN training, automatic point sampling, and visual comparison, allowing users to easily obtain and evaluate the sampled points from the perspectives of data analysis and user requirements. Quantitative comparisons and case studies based on real-world datasets are conducted to demonstrate the effectiveness of our sampling model in the preservation of user preferences and visual exploration of large-scale geospatial point data.