Autogenous shrinkage of zeolite cement pastes with low water-binder ratio

Self-desiccation is one common phenomenon of high-performance cementitious materials characterized by low water to cementitious material ratio (w/c). Autogenous shrinkage is closely related to the internal relative humidity (RH) drop and capillary pressure induced by self-desiccation in the cement pastes. However, there is debate about the determination of time-zero, the time at which autogenous shrinkage begins to develop. The objective of this study is to provide an accurate determination of time-zero based on the relationship between the internal RH and autogenous shrinkage of low w/c ratio cement pastes. And according to the time-zero, cement pastes blended with zeolite were prepared to investigate the potential of zeolite as internal curing agent. The autogenous shrinkage was conducted according to the standard method ASTM C1698. Internal RH was performed on the sealed cement pastes at very early age by conventional method of hygrometer. Setting time was determined by the Vicat needle apparatus according to the standard method ASTM C191. Experimental results revealed that no internal RH drop was observed around the final setting time determined by the Vicat method. Besides, a knee point was observed in the shrinkage curve at the time when the internal RH began to decrease. This is the so-called time-zero. And zeolite was found to be a potential internal curing agent according to the autogenous shrinkage tests measured from the new time-zero

Autogenous shrinkage of low water-binder ratio cement pastes with supplementary cementitious materials

High-performance cementitious materials are sensitive to early age cracking, mainly due to the large magnitude of autogenous shrinkage, which is closely related to the internal relative humidity (RH) decrease and capillary pressure induced by self-desiccation in the cement matrix. However, there is debate about the determination of time-zero, the time at which autogenous shrinkage begins to develop, which causes great difficulty in comparing the results provided in the exiting researches. This study presents an accurate determination of time-zero based on the relationship between the internal RH and autogenous shrinkage of cementitious materials. According to the time-zero, the effect of replacements of cement by supplementary cementitious materials on the autogenous shrinkage was investigated for the early age cement pastes with low water/binder ratio. The autogenous shrinkage was conducted according to the standard method ASTM C1698. Internal RH was performed on the sealed cement pastes at very early age by conventional method of hygrometer. Setting time was determined by the Vicat needle apparatus according to the standard method ASTM C191. The results could potentially explain the mechanism of autogenous shrinkage at early age in mixtures with supplementary cementitious materials

Deep Learning for Semantic Segmentation of UAV Videos

As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention. Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application. Previous methods extended single image segmentation approaches to multiple frames. The temporal dependencies are ignored in these methods. This paper proposes a novel segmentation method to solve this problem. Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately. FCN serves as the frame-based segmentation method. Conv-LSTM makes use of the temporal information between consecutive frames. Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV.</p

Real-time Semantic Segmentation with Context Aggregation Network

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing dual branch architectures for high-speed semantic segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. We evaluate our method on two semantic segmentation datasets, namely Cityscapes dataset and UAVid dataset. For Cityscapes test set, our model achieves state-of-the-art results with mIOU of 75.9%, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed network achieves mIOU score of 63.5% with high execution speed (15 FPS).Comment: extended version of v

LIP:Learning instance propagation for video object segmentation

In recent years, the task of segmenting foreground objects from background in a video, i.e. video object segmentation (VOS), has received considerable attention. In this paper, we propose a single end-to-end trainable deep neural network, convolutional gated recurrent Mask-RCNN, for tackling the semi-supervised VOS task. We take advantage of both the instance segmentation network (Mask-RCNN) and the visual memory module (Conv-GRU) to tackle the VOS task. The instance segmentation network predicts masks for instances, while the visual memory module learns to selectively propagate information for multiple instances simultaneously, which handles the appearance change, the variation of scale and pose and the occlusions between objects. After offline and online training under purely instance segmentation losses, our approach is able to achieve satisfactory results without any post-processing or synthetic video data augmentation. Experimental results on DAVIS 2016 dataset and DAVIS 2017 dataset have demonstrated the effectiveness of our method for video object segmentation task.</p