This paper investigated the impact of repeated bushfire exposure on the properties of four different types of lightweight aggregate (i.e., expanded perlite, pumice, diatomite and expanded glass) masonry blocks for use in the external walls of bushfir...
Given the importance of light construction in terms of better performance of structure, in case of earthquake, reduction of life and financial casualties, as well as shortage of studies in this field, the present research studied building non-structu...
During bushfires, one of the building elements that is directly exposed to embers, radiant heat and direct flames is the “wall” element. This study investigated the feasibility of using expanded perlite aggregate in masonry (i.e., cement)...
The use of waste materials as alternative aggregates in cementitious mixtures is one of the most investigated practices to enhance eco-sustainability in the civil and construction sectors. For specific applications, these secondary raw materials can...
Currently, natural resources are rapidly depleting as a result of increasing construction facilities. Increasing energy consumption with increasing construction is another serious issue. In addition, many problems that threaten the environment and hu...
This work discusses the applicability of lightweight aggregate-encapsulated n-octadecane with 1.0 wt.% of Cu nanoparticles, for enhanced thermal comfort in buildings by providing thermal energy storage functionality to no-fines concrete. A straightfo...
The development of sustainable lightweight materials is a promising field solution in this era. The production of sustainable materials by replacing coarse aggregates with some lightweight alternative provides a good quality construction material. In...
Bora is a granular volcanic pumice with a certain degree of hardness. In this study, we investigated the possibility of using fired Bora as a substitute for fine sand in mortar. The objective was to develop a lightweight pavement block with extremely...
Combining eco-sustainability and technological efficiency is one of the “hot” topics in the current construction and architectural sectors. In this work, recycled tire rubber aggregates and acoustically effective fractal cavities were com...
Baked clay bricks (Impunyu) is the dominant wall construction material in Rwanda. Clay deposits in the country’s lowlands are utilized for baked clay bricks. Despite the ongoing campaign, the use of wood by some local brick producers is unfriendly to...
A very challenging task for action recognition concerns how to effectively extract and utilize the temporal and spatial information of video (especially temporal information). To date, many researchers have proposed various spatial-temporal convoluti...
Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Bes...
In order to remove speckle noise from original synthetic aperture radar (SAR) images effectively and efficiently, this paper proposes a hybrid dilated residual attention network (HDRANet) with residual learning for SAR despeckling. Firstly, HDRANet e...
This research is focused on the influence of carbonate minerals on the properties of ceramic blocks as a replacement for commercial stabilizers, using three types of clays and sawdust as raw materials extracted from Chiapas, Mexico, for their applica...
Currently, vision transformers (ViTs) have rivaled comparable performance to convolutional neural networks (CNNs). However, the computational demands of the transformers’ self-attention mechanism pose challenges for their application on edge de...
Ensuring the integrity of aviation plug components is crucial for maintaining the safety and functionality of the aerospace industry. Traditional methods for detecting surface defects often show low detection probabilities, highlighting the need for...
Lightweight convolutional neural networks are widely used for face detection due to their ability to learn local representations through spatial induction bias and translational invariance. However, convolutional face detectors have limitations in de...
Hollow concrete masonry blocks made of low strength self-compacting concrete with recycled crushed brick and ground polystyrene as an aggregate (RBC-EP blocks), and their expected structural role as masonry infill in steel frames, has been confirmed...
Weed detection technology is of paramount significance in achieving automation and intelligence in weed control. Nevertheless, it grapples with several formidable challenges, including imprecise small target detection, high computational demands, ina...
Chimneys are structures designed to convey exhaust gases from heating devices to the outside of buildings. The materials from which they are made have a great impact on their fire safety, as well as on the safety of the whole building. As current tre...
This study investigates the prediction of the thermal conductivity of lightweight earth and raw earth blocks incorporating plant aggregates. Given the high variability of raw materials, it is not currently possible to predict the thermal performance...
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data offer complementary spectral and structural information; however, the integration of these high-dimensional, heterogeneous modalities poses significant challenges. We propose a Gl...
Diatomitic and clastic-volcanoclastic marly samples from the Paranisia area of Limnos Island, were studied mineralogically by X-ray diffraction (XRD), chemically by X-ray fluorescence spectroscopy (XRF) and morphologically by scanning electron micros...
The paper deals with the use of vibro-compressed units with bio-natural components on construction. The proposed mix design of vibrated blocks consist of cork granules and/or hemp shives, with the aim to substitute polymeric elements or expanded clay...
Accurate and efficient cloud detection is critical for maintaining the usability of optical remote sensing imagery, particularly in large-scale Earth observation systems. In this study, we propose FEMNet, a lightweight dual-branch network that combin...
The introduction of an attention mechanism in remote sensing image segmentation improves the accuracy of the segmentation. In this paper, a novel multi-scale KAN-based linear attention (MKLA) segmentation network of MKLANet is developed to promote a...
To address the issue of the computational intensity and deployment difficulties associated with weed detection models, a lightweight target detection model for weeds based on YOLOv8s in maize fields was proposed in this study. Firstly, a lightweight...
In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learn...
Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require hig...
In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it ofte...
Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively. How...
This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. The proposed method addresses the challenge of accu...
Remote sensing image (RSI) super-resolution plays a critical role in improving image details and reducing costs associated with physical imaging devices. However, existing super-resolution methods are not applicable to resource-constrained edge devic...
Awareness of sustainability in construction has led to the utilization of waste material such as oil palm shell (OPS) in concrete production. The feasibility of OPS as alternative aggregates in concrete has been widely studied at the material level....
Real-time building damage detection effectively improves the timeliness of post-earthquake assessments. In recent years, terrestrial images from smartphones or cameras have become a rich source of disaster information that may be useful in assessing...
In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing pe...
With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risk...
Semantic scene completion is a crucial outdoor scene understanding task that has direct implications for technologies like autonomous driving and robotics. It compensates for unavoidable occlusions and partial measurements in LiDAR scans, which may o...
At present, ResNet and DenseNet have achieved significant performance gains in the field of finger-vein biometric recognition, which is partially attributed to the dominant design of cross-layer skip connection. In this manner, features from multiple...
Hyperledger Fabric (HLF) is a leading permissioned blockchain platform designed for enterprise applications. However, it faces significant security risks from Denial-of-Service (DoS) attacks targeting its core components. This study systematically in...
Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneou...
In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However,...
With the rapid acceleration of urbanization and the increasing volume of road traffic, emergency vehicles frequently encounter congestion when performing urgent tasks. Failure to yield in a timely manner can result in the loss of critical rescue time...
In recent years, numerous advanced lightweight models have been proposed for salient object detection (SOD) in optical remote sensing images (ORSI). However, most methods still face challenges such as performance limitations and imbalances between ac...
With the large scale deployment of multihomed mobile computing devices in today’s Internet, the Multipath TCP (MPTCP) is being considered as a preferred data transmission technology in the future Internet due to its promising features of bandwi...
Accurate traffic forecasting underpins intelligent transportation systems. We present PTSTG, a compact spatio-temporal forecaster that couples a patch-based Transformer encoder with a data-driven adaptive adjacency and lightweight node graph blocks....
The widespread use of wearable Internet of Things (IoT) devices has transformed modern healthcare through the real-time monitoring of physiological signals. However, real- time responsiveness and data privacy are big challenges. Federated Learning (F...
This research investigates the influence of incorporating perlite aggregate and silica fume on the properties of cement mortar, with a focus on compressive strength, flexural strength, density, water absorption, and thermal conductivity. The results...
Underwater debris detection plays a crucial role in marine environmental protection. However, existing object detection algorithms generally suffer from excessive model complexity and insufficient detection accuracy, making it difficult to meet the r...
Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, espec...
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