Dynamic Responses of Continuous Girder Bridges with Uniform Cross-Section under Moving Vehicular Loads

To address the drawback of traditional method of investigating dynamic responses of the continuous girder bridge with uniform cross-section under moving vehicular loads, the orthogonal experimental design method is proposed in this paper. Firstly, some empirical formulas of natural frequencies are obtained by theoretical derivation and numerical simulation. The effects of different parameters on dynamic responses of the vehicle-bridge coupled vibration system are discussed using our own program. Finally, the orthogonal experimental design method is proposed for the dynamic responses analysis. The results show that the effects of factors on dynamic responses are dependent on both the selected position and the type of the responses. In addition, the interaction effects between different factors cannot be ignored. To efficiently reduce experimental runs, the conventional orthogonal design is divided into two phases. It has been proved that the proposed method of the orthogonal experimental design greatly reduces calculation cost, and it is efficient and rational enough to study multifactor problems. Furthermore, it provides a good way to obtain more rational empirical formulas of the DLA and other dynamic responses, which may be adopted in the codes of design and evaluation

Single image 3D shape retrieval viaCross-Modal instance and category contrastive learning

In this work, we tackle the problem of single image-based 3D shape retrieval (IBSR), where we seek to find the most matched shape of a given single 2D image from a shape repository. Most of the existing works learn to embed 2D images and 3D shapes into a common feature space and perform metric learning using a triplet loss. Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning. We note that adopting such cross-modal contrastive learning between 2D images and 3D shapes into IBSR tasks is non-trivial and challenging: contrastive learning requires very strong data augmentation in constructed positive pairs to learn the feature invariance, whereas traditional metric learning works do not have this requirement. Moreover, object shape and appearance are entangled in 2D query images, thus making the learning task more difficult than contrasting single-modal data. To mitigate the challenges, we propose to use multi-view grayscale rendered images from the 3D shapes as a shape representation. We then introduce a strong data augmentation technique based on color transfer, which can significantly but naturally change the appearance of the query image, effectively satisfying the need for contrastive learning. Finally, we propose to incorporate a novel category-level contrastive loss that helps distinguish similar objects from different categories, in addition to classic instance-level contrastive loss. Our experiments demonstrate that our approach achieves the best performance on all the three popular IBSR benchmarks, including Pix3D, Stanford Cars, and Comp Cars, outperforming the previous state-of-the-art from 4% - 15% on retrieval accuracy