Application of Acoustic Emission Technique in the Monitoring of Masonry Structures

The application of acoustic emission (AE) technique in monitoring the safe condition is a useful technique in steel and concrete structures, whereas its application is restrained in masonry structures due to the layered property. Qualitative and quantitative analyses were investigated in this research to improve the AE application in masonry structures. For quantitative analysis, an improved localization method is proposed to give more reliable crack localization results. In the proposed method, the parameter ξ on the behavior of inhomogeneity of the monitored structure could minimize the unavoidable propagation delay caused by the layers in the masonry structure. The rest results approved the reliability of the proposed method in masonry structures. For qualitative analysis, the parameter analysis, including the cumulative AE event, frequency distribution, time-scaling exponent, and b-value, was adopted to monitor one historical church and was approved to be useful

Fire Performance of Steel Reinforced Concrete (SRC) Structures

AbstractThis paper summarizes some of the recent research published on steel reinforced concrete (SRC) structures under or after exposure to fire. The contents include: 1) Fire resistance and post-fire behavior of SRC columns; 2) Fire performance of SRC column to beam joints, by adopting a loading sequence including initial loading, heating, cooling and post-fire loading; 3) Fire resistance and post-fire behavior of SRC composite frames

Noisy Correspondence Learning with Meta Similarity Correction

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions.Comment: Accepted at CVPR 202

Analysis of concrete-filled stainless steel tubular columns under combined fire and loading

[EN] In fire scenarios, concrete-filled stainless steel tubular (CFSST) columns undergo initial loading at ambient temperature, loading during the heating phase as the fire develops, loading during the cooling phase as the fire dies out and continual loading after the fire. CFSST columns may fail some points during this process under combined fire and loading. In this paper, the failure modes and corresponding working mechanism of CFSST columns subjected to an entire loading and fire history are investigated. Sequentially coupled thermal-stress analyses in ABAQUS are employed to establish the temperature field and structural response of the CFSST column. To improve the precision of the finite element (FE) model, the influence of moisture on the thermal conductivity and specific heat of concrete during both the heating and cooling phases is considered using subroutines. Existing fire and post-fire test data of CFSST columns are used to validate the FE models. Comparisons between predicted and test results confirm that the accuracy of the FE models is acceptable; the FE models are then extended to simulate a typical CFSST column subjected to the entire loading and fire history. The behaviour of the CFSST column is explained by analysis of the temperature distribution, load versus axial deformation curves and failure response.The research reported in the paper is part of the Project 51308539 supported by the National Natural Science Foundation of China. The financial support is highly appreciated.Tan, Q.; Gardner, L.; Han, L.; Song, D. (2018). Analysis of concrete-filled stainless steel tubular columns under combined fire and loading. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 825-833. https://doi.org/10.4995/ASCCS2018.2018.7206OCS82583

Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification

Classifying incomplete multi-view data is inevitable since arbitrary view missing widely exists in real-world applications. Although great progress has been achieved, existing incomplete multi-view methods are still difficult to obtain a trustworthy prediction due to the relatively high uncertainty nature of missing views. First, the missing view is of high uncertainty, and thus it is not reasonable to provide a single deterministic imputation. Second, the quality of the imputed data itself is of high uncertainty. To explore and exploit the uncertainty, we propose an Uncertainty-induced Incomplete Multi-View Data Classification (UIMC) model to classify the incomplete multi-view data under a stable and reliable framework. We construct a distribution and sample multiple times to characterize the uncertainty of missing views, and adaptively utilize them according to the sampling quality. Accordingly, the proposed method realizes more perceivable imputation and controllable fusion. Specifically, we model each missing data with a distribution conditioning on the available views and thus introducing uncertainty. Then an evidence-based fusion strategy is employed to guarantee the trustworthy integration of the imputed views. Extensive experiments are conducted on multiple benchmark data sets and our method establishes a state-of-the-art performance in terms of both performance and trustworthiness.Comment: CVP

Semantic Equivariant Mixup

Mixup is a well-established data augmentation technique, which can extend the training distribution and regularize the neural networks by creating ''mixed'' samples based on the label-equivariance assumption, i.e., a proportional mixup of the input data results in the corresponding labels being mixed in the same proportion. However, previous mixup variants may fail to exploit the label-independent information in mixed samples during training, which usually contains richer semantic information. To further release the power of mixup, we first improve the previous label-equivariance assumption by the semantic-equivariance assumption, which states that the proportional mixup of the input data should lead to the corresponding representation being mixed in the same proportion. Then a generic mixup regularization at the representation level is proposed, which can further regularize the model with the semantic information in mixed samples. At a high level, the proposed semantic equivariant mixup (sem) encourages the structure of the input data to be preserved in the representation space, i.e., the change of input will result in the obtained representation information changing in the same way. Different from previous mixup variants, which tend to over-focus on the label-related information, the proposed method aims to preserve richer semantic information in the input with semantic-equivariance assumption, thereby improving the robustness of the model against distribution shifts. We conduct extensive empirical studies and qualitative analyzes to demonstrate the effectiveness of our proposed method. The code of the manuscript is in the supplement.Comment: Under revie

Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. Datasets and codes are available at: \url{https://shaunyuan22.github.io/SODA}