Experimental calibration of the bond-slip relationship of different CFRP-to-timber joints through digital image correlation measurements

UIDB/EMS/00667/2020Nowadays, the use of the Digital Image Correlation (DIC) technique has spread and it is being used in several engineering areas to measure displacements. The available data obtained from the DIC measurement to evaluate the bond performance between a Carbon fibre Reinforced Polymer (CFRP) externally bonded to a timber substrate is scarce. From the existing data obtained with other materials, this contactless technique revealed to be quite useful but its accuracy with other well-established techniques, such as the use of electric strain gauges is not well understood yet. Therefore, the current work aims to study the accuracy of 2D DIC measurements with the measurements obtained from the use of strain gauges within a low-cost perspective. To that end, several CFRP-to-timber bonded joints were tested under the single-lap shear test and different bonding techniques were considered as well. Some flaws intrinsically derived from the DIC measurements that complicate the bond assessment, such as the fluctuations in the generated displacements field, are identified, and to bypass this problem a new methodology is proposed. This new methodology is based on two different closed-form solutions that, after defining the local and global bond behaviours of different CFRP-to-timber bonded joints, allowed to eliminate the fluctuations found from the DIC measurements, facilitating the estimation and the comprehension of the full debonding process of the CFRP-to-timber joints, which was achieved with a good proximity to the homologous debonding process derived from the strain gauge measurements.publishersversionpublishe

Effect of raw materials on the performance of 3D printing geopolymer: A review

Traditional construction materials such as cement products release a significant amount of carbon dioxide during their preparation and usage, negatively impacting on the environment. In contrast, 3D printing (3DP) with geopolymer materials utilises renewable and low-carbon emission raw materials. It also exhibits characteristics such as energy efficiency and resource-efficient utilisation, contributing to reduction in carbon emissions and an improvement in sustainability. Therefore, the development of 3DP geopolymer holds great significance. This paper provides a comprehensive review of 3DP geopolymer systems, examining the effect of raw materials on processability, including flowability and thixotropy, and microstructure. The study also delves into sustainability and environmental impact. The evaluation highlights the crucial role of silicon, aluminium, and calcium content in the silicate raw material, influencing the gel structure and microstructural development of the geopolymer. Aluminium promotes reaction rate, increases reaction degree, and aids in product formation. Silicon enhances the mechanical properties of geopolymer, while calcium facilitates the formation and stability of the three-dimensional network structure, further improving material strength and stability. Moreover, the reactivity of raw materials is a key factor affecting interlayer bonding and interface mechanical properties. Finally, considering sustainability, the selection of raw materials is crucial in reducing carbon emissions, energy consumption, and costs. Compared to Portland cement, 3DP geopolymer material demonstrate lower carbon emissions, energy consumption, and costs, thus making it a sustainable material

Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation

Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-invariant features recently. These methods are based on the assumption that the prototypes, which are represented as the central value of the same class in a certain domain, are domain-invariant. Since the prototypes of different domains have discrepancies as well, the class-wise domain-invariant features learned from the source domain by PCL need to be aligned with the prototypes of other domains simultaneously. However, the prototypes of the same class in different domains may be different while the prototypes of different classes may be similar, which may affect the learning of class-wise domain-invariant features. Based on these observations, a calibration-based dual prototypical contrastive learning (CDPCL) approach is proposed to reduce the domain discrepancy between the learned class-wise features and the prototypes of different domains for domain generalization semantic segmentation. It contains an uncertainty-guided PCL (UPCL) and a hard-weighted PCL (HPCL). Since the domain discrepancies of the prototypes of different classes may be different, we propose an uncertainty probability matrix to represent the domain discrepancies of the prototypes of all the classes. The UPCL estimates the uncertainty probability matrix to calibrate the weights of the prototypes during the PCL. Moreover, considering that the prototypes of different classes may be similar in some circumstances, which means these prototypes are hard-aligned, the HPCL is proposed to generate a hard-weighted matrix to calibrate the weights of the hard-aligned prototypes during the PCL. Extensive experiments demonstrate that our approach achieves superior performance over current approaches on domain generalization semantic segmentation tasks.Comment: Accepted by ACM MM'2

SBZ-Monteur : SHK-Magazin für Auszubildende und Gesellen

Two simplified methods are introduced in the paper, in which periods and mode shapes are obtained through solving the story lateral stiffness of frame- shear wall structures, the method of solving the differential equation and the method of substructure. On the basis of assumption for structure, the assembling strategy of mass matrix and stiffness matrix are discussed specially. The periods and mode shapes can be acquired through both methods and the results are compared and analyzed with PKPM and ANSYS. The computation programs are very convenient and can gain the periods and mode shapes quickly. The methods will create some degree of errors, but it is in the scope of acceptance. They are of great reference to structural designers and scientific researchers

A full-reference Image Quality Assessment metric for 3D Synthesized Views

International audienc

Performance comparison of objective metrics on free-viewpoint videos with different depth coding algorithms

International audienceThe popularity of 3D applications has brought out new challenges in the creation, compression and transmission of 3D content due to the large size of 3D data and the limitation of transmission. Several compression standards, such as, Multiview-HEVC and 3D-HEVC have been proposed to compress the 3D content aiding by view synthesis technologies, among which the most commonly used algorithm is Depth-Image-Based-Rendering (DIBR), but the quality assessment of DIBR-synthesized view is very challenging owing to its new types of distortions induced by inaccurate depth map which the conventional 2D quality metrics may fail to assess. In this paper, we test the performance of existing objective metrics on free-viewpoint video with different depth coding algorithms. Results show that all the existing objective metrics perform not well on this database including the full-reference and the no-reference. There is certainly room for further improvement for the algorithms

EDDMF: An Efficient Deep Discrepancy Measuring Framework For Full-Reference Light Field Image Quality Assessment

International audienceThe increasing demand for immersive experience has greatly promoted the quality assessment research of Light Field Image (LFI). In this paper, we propose an efficient deep discrepancy measuring framework for full-reference light field image quality assessment. The main idea of the proposed framework is to efficiently evaluate the quality degradation of distorted LFIs by measuring the discrepancy between reference and distorted LFI patches. Firstly, a patch generation module is proposed to extract spatio-angular patches and sub-aperture patches from LFIs, which greatly reduces the computational cost. Then, we design a hierarchical discrepancy network based on convolutional neural networks to extract the hierarchical discrepancy features between reference and distorted spatio-angular patches. Besides, the local discrepancy features between reference and distorted sub-aperture patches are extracted as complementary features. After that, the angular-dominant hierarchical discrepancy features and the spatial-dominant local discrepancy features are combined to evaluate the patch quality. Finally, the quality of all patches is pooled to obtain the overall quality of distorted LFIs. To the best of our knowledge, the proposed framework is the first patch-based full-reference light field image quality assessment metric based on deep-learning technology. Experimental results on four representative LFI datasets show that our proposed framework achieves superior performance as well as lower computational complexity compared to other state-of-the-art metrics