Predicting rolling shear failure of cross-laminated panels using a multiscale domain decomposition approach

In the presence of strong heterogeneities, it is well known that the use of explicit schemes for the transport of species in a porous medium suffers from severe restrictions on the time step. This has led to the development of implicit schemes that are increasingly favored by practitioners for their computational efficiency. The transport equation requires knowledge of the velocity field, which results from an elliptic problem (Darcy problem) that is the most expensive part of the computation. When considering large reservoirs, a cost-effective way of approximating the Darcy problems is using multiscale domain decomposition (MDD) methods. They allow for the pressure and velocity fields to be computed on coarse meshes (large scale), while detailed basis functions are defined locally, usually in parallel, in a much finer grid (small scale). In this work we adopt the Multiscale Robin Coupled Method (MRCM, Guiraldello et al., 2018; Rocha et al., 2020), which is a generalization of previous MDD methods that allows for great flexibility in the choice of interface spaces. In this article we investigate the combination of the MRCM with implicit transport schemes. A sequentially implicit strategy is proposed, with different trust-region algorithms ensuring the convergence of the transport solver. The method is assessed on several very stringent 2D two-phase problems, demonstrating its stability even for large time steps. It is also shown that the best accuracy is achieved by considering recently introduced non-polynomial interface spaces, since polynomial spaces are not optimal for high-contrast channelized permeability fields.

The contrast and colors of images acquired in bad weather are usually poor. This paper proposed a simple method to remove haze based on a multi-scale decomposition method. Through the Fourier spectrum and Hough transform we obtain the directional images which are then used to decompose the image to IMFs. By a linear mapping, we manipulated the detail, luminance as well as color to enhance the degraded images. Experiments show the efficiency of the proposed method.

The current SAR and optical image fusion methods still face the challenge of detail loss and low contrast. Therefore, this paper proposes a Sentinel-1/2 image fusion framework based on multiscale transform (MST) and generative adversarial network (GAN). The fusion framework based on MST and GAN is called MST-GAN, which can fully synthesize the information of source images and improve the contrast of fusion results. In this framework, a GAN low-frequency fusion strategy is proposed to fuse the low-frequency coefficients of multiscale decomposition, and the high-frequency coefficients are fused using the traditional “max-absolute” rule. In this paper, three multiscale decomposition methods are selected to verify the effectiveness of the proposed fusion framework. The qualitative and quantitative evaluation of the experimental results shows that the fused images under this framework fully integrate spectral, scattering and texture information, and have better ground objects differentiation.

Cross-laminated solid timber panels represent an interesting technical and economical product for modern timber structures. The use of large prefabricated cross-laminated solid timber panels for load-bearing wall and floor assemblies has become increasingly popular in particular for residential timber buildings. The fire behaviour of cross-laminated solid timber panels has been experimentally and numerically studied during two different ongoing research projects carried out at the Institute of Structural Engineering of ETH Zurich, Switzerland and the Trees and Timber Institute CNR-IVALSA in Trento, Italy. The paper presents the main results of the experimental and numerical analyses. Particular attention is given to the comparison of the fire behaviour of cross-laminated solid timber panels with homogeneous timber panels. The results of the analysis have shown that the fire behaviour of cross-laminated solid timber panels depends on the behaviour of the single layers. If the charred layers fall off, an increased charring rate needs to be taken into account. The same effect is observed for initially protected timber members after the fire protection has fallen off. Thus the fire behaviour of cross-laminated solid timber panels can be strongly influenced by the thickness and the number of layers. Further vertical structural members (walls) may show a better fire behaviour in comparison to horizontal members (slabs).

Influence of inter-panel connections on vibration response of CLT floors due to pedestrian-induced loading

The vibration behavior of cross-laminated timber components in the low-frequency range can be predicted with high accuracy by the finite element method. However, the modeling of assembled cross-laminated timber components has been studied only scarcely. The three-dimensional p-version of the finite element method, which is characterized by hierarchic high-order shape functions, is well suited to consider coupling and support conditions. Furthermore, a small number of degrees of freedom can be obtained in case of thin-walled structures using p-elements with high aspect ratios and anisotropic ansatz spaces. In this article, a model for cross-laminated timber assemblies made of volumetric high-order finite elements is presented. Two representative types of connections are investigated, one with an elastomer between the cross-laminated timber components and the other without. The model is validated, and suitable ranges for the stiffness parameters of the finite elements which represent the junctions are identified.

The widespread use of multisensor technology and the emergence of big datasets have created the need to develop tools to reduce, approximate, and classify large and multimodal data such as higher-order tensors. While early approaches focused on matrix- and vector-based methods to represent these higher-order data, more recently it has been shown that tensor decomposition methods are better equipped to capture couplings across their different modes. For these reasons, tensor decomposition methods have found applications in many different signal processing problems including dimensionality reduction, signal separation, linear regression, feature extraction, and classification. However, most of the existing tensor decomposition methods are based on the principle of finding a low-rank approximation in a linear subspace structure, where the definition of rank may change depending on the particular decomposition. Since many datasets are not necessarily low-rank in a linear subspace, this often results in high approximation errors or low compression rates. In this paper, we introduce a new adaptive, multi-scale tensor decomposition method for higher-order data inspired by hybrid linear modeling and subspace clustering techniques. In particular, we develop a multi-scale higher-order singular value decomposition (MS-HoSVD) approach where a given tensor is first permuted and then partitioned into several sub-tensors each of which can be represented as a low-rank tensor with increased representational efficiency. The proposed approach is evaluated for dimensionality reduction and classification for several different real-life tensor signals with promising results.

Structural fire engineering considerations for cross-laminated timber walls

Image-domain dual-energy CT (DECT) is practical and critical for medical diagnosis and treatment evaluation. The bottleneck of DECT imaging is the significantly magnified noise after a direct inversion decomposition. A good balance is not readily achievable between noise suppression and spatial resolution maintenance. Noise suppression is conventionally included in DECT decomposition implicitly or explicitly and results in the spatial resolution loss. To tackle this problem, we propose a multiscale framework for DECT imaging. High- and low-energy CT images are decomposed into the scale space to fully explore the flexibility of signal processing in each scale. The scale images are then decomposed into two material-specific images using penalized weighted least-squares (PWLS) algorithm with adjustable penalty parameters in different scales using the statistical information in the DECT decomposition process. The final material-specific images are generated by accumulating the decomposed images throughout all scales. The proposed algorithm is evaluated on two physical phantoms: Catphan©600 evaluation phantom and an anthropomorphic head phantom. Noise standard deviation (STD) is reduced by 93% and 98% using the proposed strategy for bone and soft-tissue images as compared with the one using direct inversion for Catphan©600 evaluation phantom, and by 96% and 98% for the anthropomorphic head phantom. Compared with its single-scale counterpart, the proposed multiscale PWLS-based method increases the spatial resolution of soft-tissue images by 28% using a lower scaling factor (k = 20), and by 51% with a higher scaling factor (k = 80). The noise STDs of the decomposed material images remain at the similar level using single- and multi-scale schemes. The proposed multiscale PWLS-based decomposition method is thus attractive for low-dose DECT imaging and clinical practice.

Floor vibration, although not a safety concern, is a prevalent performance complaint in multi-story structures. With the increasing use of mass timber construction, various types of long-span timber floors (LSTFs), including plain cross-laminated timber (CLT), CLT with secondary beams (ribbed-deck), and hybrid systems such as timber–concrete composite (TCC) and CLT on-steel-support beams, are gaining popularity. However, due to limited knowledge regarding their vibration characteristics and acceptance criteria, these construction types are often overlooked during the design stage by architects, engineers, and builders. Existing standards and guidelines primarily calibrated for steel and concrete floors lack a validated and calibrated method for evaluating the vibration performance of LSTFs. Nonetheless, it is essential for structural engineers to address vibration concerns during the design stage and potentially investigate excessive vibration in existing buildings, providing mitigation solutions. This article provides a comprehensive overview, discussion, and analysis of the measurement, analysis, design, perception, and acceptability of vibration of timber floors as outlined in international standards and commonly used guidelines. Experimental and theoretical case studies, including vibration measurements of a CLT floor and a comparison of vibration acceptability in lightweight timber floors using different methods, are reported. The results highlight discrepancies between simplified equation calculations and modal analysis observations, underscoring the limitations of relying solely on simplified equations. Furthermore, it is observed that current modal superposition methods tend to be conservative in predicting floor acceleration and velocity responses. Recommendations are provided for future research in the field to enhance floor vibration assessment techniques, aiming for improved design optimization and occupant comfort.

The goal of infrared and visible image fusion is to generate an integrated image which can simultaneously preserve more visible detail information and prominent information from the input images. In order to achieve this goal, a novel infrared and visible fusion method based on visual saliency sparse representation and detail injection model (DIM) is presented. The proposed fusion method contains four steps. The first step is to decompose the source images into base layers and detail layers through the proposed multiscale decomposition method, which has the advantages of scale awareness and high-edge-preservation efficiency. Then, we design a novel subfusion rule called visual saliency sparse representation to get the fused base layer. Third, a DIM is developed to fuse the detail layers. In this model, we first employ a "max-absolute" scheme to obtain prefused images, which are then used as the model items for participating in the fusion process of the detail layers. Finally, the target-merged image is achieved through an inverse multiscale decomposition method. The experimental results demonstrate that the proposed method can not only preserve the details and significant information of source images but also enhance the brightness of the fused image, when compared to other state-of-the-art fusion methods.

Infrared and visible image fusion based on visual saliency map and weighted least square optimization

Tennis training requires precise analysis of physical functional movements to enhance athletic performance and prevent injuries. This paper presents a novel framework for the automated detection and analysis of tennis training movements, leveraging deep learning techniques combined with multiscale image decomposition. The proposed method processes video data to extract critical movement patterns and employs multiscale decomposition to enhance spatial and temporal features in the input data. A convolutional neural network (CNN) architecture is then utilized to classify and evaluate the movements based on their functionality and accuracy. Experimental results demonstrate that the proposed system achieves high accuracy in detecting and evaluating functional movements in tennis training. The multiscale decomposition enhances the feature extraction process, leading to more robust and reliable movement analysis across various video resolutions and environmental conditions. This study offers a promising approach for integrating advanced technologies in sports training, providing real-time feedback and valuable insights for athletes and coaches. Future work will focus on expanding the system to support other sports and refining the model for improved adaptability.

Photoacoustic (PA) and ultrasound (US) imaging exhibit strong complementary characteristics in tissue characterization. PA sensitively captures functional information such as vascular distribution but lacks anatomical context, making localization challenging. US provides high-resolution anatomical structures but offers no functional data. Integrating both modalities enable simultaneous acquisition of structural and functional information, holding significant clinical value. However, current research in medical image fusion focuses on conventional modalities such as MRI, CT, and PET, while studies on PA/US fusion remain preliminary. To address this gap, this study proposes an improved PA/US images fusion strategy based on the multi-scale decomposition method. The approach first decomposes PA and US images into high- and low-frequency sub-images at different scales using the Laplacian pyramid to separate detail and contour information. Then, a decision map is used to partition the non-overlapping domains (NOD) and overlapping domains (OD), where the complementary information is emphasized in NOD areas to combine anatomical features from US and vascular characteristics from PA, while the weighted energy fusion is applied in OD areas to integrate redundant information. Finally, the fused image is reconstructed via inverse Laplacian pyramid decomposition. Experimental results demonstrate that the proposed method significantly enhances anatomical clarity and vascular contrast in fused images. Objective evaluations show superior entropy and spatial frequency compared to conventional methods, confirming advantages in informational richness and detail preservation. The NOD/OD regional fusion strategy offers a new direction for handling complementary and redundant information in PA/US imaging and establishes a foundation for future fusion rule design.

Infrared and visible light image fusion combines infrared and visible light images by extracting the main information from each image and fusing it together to provide a more comprehensive image with more features from the two photos. Infrared and visible image fusion has gained popularity in recent years and is increasingly being employed in sectors such as target recognition and tracking, night vision, scene segmentation, and others. In order to provide a concise overview of infrared and visible picture fusion, this paper first explores its historical context before outlining current domestic and international research efforts. Then, conventional approaches for infrared and visible picture fusion, such as the multi-scale decomposition method and the sparse representation method, are thoroughly introduced. The advancement of deep learning in recent years has greatly aided the field of picture fusion. The outcomes of the fusion have a wide range of potential applications due to the neural networks’ strong feature extraction and reconstruction skills. As a result, this research also evaluates deep learning techniques. After that, some common objective evaluation indexes are provided, and the performance evaluation of infrared and visible image fusion is introduced. The common datasets in the areas of infrared and visible image fusion are also sorted out at the same time. Datasets play a significant role in the advancement of infrared and visible image fusion and are an essential component of infrared and visible image fusion testing. The application of infrared and visible image fusion in many domains is then simply studied with practical examples, particularly in developing fields, used to show its application. Finally, the prospect of the current infrared and visible image fusion field is presented, and the full text is summarized.