Lightweight concrete offers numerous advantages for modular construction, including easier construction planning and logistics, and the ability to offset additional dead loads induced by double-wall and double-slab features. In a previous study, authors proposed incorporating lightweight aggregate into foamed concrete instead of adding extra foam to achieve lower density, resulting in lightweight concrete with an excellent strength-to-density ratio. This paper further investigated the performance aspects of foamed concrete with lightweight aggregate beyond mechanical strength. To evaluate the effect of aggregate type and foam content, three mix compositions were designed for the lightweight concrete. Specimens were prepared for experimental tests on thermal conductivity and drying shrinkage of lightweight concrete. Results showed that while both the increase in foam volume and the incorporation of lightweight aggregate led to higher drying shrinkage, they also contributed to improved insulating properties and reduced potential of cracking. Using typical multi-storey modular residential buildings in Hong Kong and three other Chinese cities as case studies, simulations were performed to assess potential savings in annual cooling and heating loads by employing the proposed lightweight concrete. These findings demonstrate the practical benefits of using foamed concrete with lightweight aggregate in modular construction and provide valuable insights for further optimization and implementation.
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http://dx.doi.org/10.3390/ma17153869 | DOI Listing |
Sensors (Basel)
December 2024
State Grid Tianjin Electric Power Research Institute, Tianjin 300180, China.
Large oil-immersed transformers have metal-enclosed shells, making it difficult to visually inspect the internal insulation condition. Visual inspection of internal defects is carried out using a self-developed micro-robot in this work. Carbon trace is the main visual characteristic of internal insulation defects.
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December 2024
Jiangsu Key Laboratory Environmental Impact and Structural Safety in Engineering, China University of Mining and Technology, Xuzhou 221116, China.
The low hydration degree of fly ash in Fly Ash Unburned Lightweight Aggregate (FULA) is not conducive to the development of the mechanical properties of lightweight aggregates and their concrete. In this paper, FULA was immersed in an alkaline solution with the purpose of improving the mechanical properties of FULA and its concrete. Firstly, FULA was prepared using fly ash as the main raw material.
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January 2025
School of Mathematics and Computer, Wuhan Polytechnic University, Wuhan, 430048, China.
The rapid changes in the global environment have led to an unprecedented decline in biodiversity, with over 28% of species facing extinction. This includes snakes, which are key to ecological balance. Detecting snakes is challenging due to their camouflage and elusive nature, causing data loss and feature extraction difficulties in ecological monitoring.
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January 2025
School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China.
The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor device, and the integrity of its internal structure directly influences both its electrical performance and long-term reliability. However, the precise semantic segmentation of IGBT ultrasonic tomographic images poses several challenges, primarily due to high-density noise interference and visual distortion caused by target warping. To address these challenges, this paper constructs a dedicated IGBT ultrasonic tomography (IUT) dataset using Scanning Acoustic Microscopy (SAM) and proposes a lightweight Multi-Scale Fusion Network (LMFNet) aimed at improving segmentation accuracy and processing efficiency in ultrasonic images analysis.
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December 2024
College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 47100, China.
Tea bud detection technology is of great significance in realizing automated and intelligent plucking of tea buds. This study proposes a lightweight tea bud identification model based on modified Yolov5 to increase the picking accuracy and labor efficiency of intelligent tea bud picking while lowering the deployment pressure of mobile terminals. The following methods are used to make improvements: the backbone network CSPDarknet-53 of YOLOv5 is replaced with the EfficientNetV2 feature extraction network to reduce the number of parameters and floating-point operations of the model; the neck network of YOLOv5, the Ghost module is introduced to construct the ghost convolution and C3ghost module to further reduce the number of parameters and floating-point operations of the model; replacing the upsampling module of the neck network with the CARAFE upsampling module can aggregate the contextual tea bud feature information within a larger sensory field and improve the mean average precision of the model in detecting tea buds.
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