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Thermal ProPerTies of PolyProPylene fibrillaTed fibre reinforced lighTweighT foamed concreTe

Archives of Metallurgy and Materials 2024年第4期69卷 1519-1527页

作者： Mydin, A.O. Al Bakri Abdullah, M.M. Majeed, S.S. Omar, R. Ahmad, R. Garus, S. UNIVERSITI SAINS MALAYSIA SCHOOL OF HOUSING BUILDING AND PLANNING PENANG11800 Malaysia PERLIS Malaysia NAWROZ UNIVERSITY COLLEGE OF ENGINEERING DEPARTMENT OF CIVIL ENGINEERING KURDISTAN REGION Iraq FACULTY OF TECHNOLOGY MANAGEMENT AND BUSINESS DEPARTMENT OF CONSTRUCTION MANAGEMENTPARIT RAJA BATU PAHAT JOHOR86400 Malaysia FACULTY OF MECHANICAL ENGINEERING & TECHNOLOGY UNIVERSITI MALAYSIA PERLIS PERLIS01000 Malaysia CZESTOCHOWA UNIVERSITY OF TECHNOLOGY FACULTY OF MECHANICAL ENGINEERING AND COMPUTER SCIENCE CZESTOCHOWA42-201 Poland

The global demand for energy has experienced a significant rise. The thermal performance of conventional concrete has been extensively studied, but there is still a significant amount of research needed to understand the effects of synthetic fibres on the thermal properties of lightweight foamed concrete (LFC). LFC popularity in the construction industry has increased rapidly due to the many benefits it offers over conventional concrete. Therefore, the goal of this study is to investigate the feasibility of incorporating polypropylene fibrillated fibre (PFF) into LFC to improve its thermal properties. The PFF was integrated into the LFC in diverse weight fractions, between 1% to 4%. Density of 1080 kg/m3 was made and evaluated. Spreadability, permeable porosity, conductivity, diffusivity and specific heat capacity were determined. The findings demonstrate that spreadability of fresh mix decreased when the weight fractions of PFF were increased from 0% (250 mm) to 4% (239 mm). The incorporation of 3% PFF to LFC greatly improves its conductivity and diffusivity. Specifically, the thermal conductivity decreases from 0.3307 W/mK (in the control) to 0.2643 W/mK, and the diffusivity decreases from 0.4426 m2/s (in the control) to 0.3843 m2/s. Compared to the control specimen, LFC’s porosity increased slightly from 43.6% to 44.9% when 3% PFF was added. The findings revealed that the PFF possesses significant underlying potential for utilisation in cementitious materials, potentially serving a pivotal function in either reducing the thermal-inducing property or improving the heat transfer of the concrete. The utilisation of LFC-PFF composites may also result in significant energy savings. © 2024. The Author(s).

关键词： Light weight concrete

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Airflow Simulation Techniques Comparison for Architectural Design

Airflow Simulation Techniques Comparison for Architectural D...

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2021 International Conference on Computing in Civil Engineering, I3CE 2021

作者： Zhao, Yuanpei Ren, Ran Nicholas Dib, H. McGraw, Tim Chen, Yingjie Dept. of Computer Graphics Technology West LafayetteIN United States School of Construction Management Technology West LafayetteIN United States Knoy Hall of Technology West LafayetteIN United States

ISBN: (纸本)9780784483893

Simulation of indoor airflow in heating, ventilation, and air conditioning (HVAC) system in construction design is critical in order to control spread of volatile organic compounds (VOC) or germs and viruses in the case of a pandemic, as we witnessed during 2019-2021. This paper presents the findings from comparing two types of fluid simulations: grid-based and particle-based methods. The former is widely used for scientific computation due to its precision;however, it is time consuming and requires designers to do pretreatment of the building model for airflow simulations in construction design using grid-based methods. The particle-based method is used in visual effects, games, and other applications requiring real-time simulation. This paper presents a review of the literature on different methods of fluid simulation like finite volume method (FVM), smoothed-particle hydrodynamics (SPH), position-based dynamics (PBD), and provides a comparison of heat transfer between particle-based and grid-based methods in indoor airflow simulations with HVAC system. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.

关键词： Volatile organic compounds

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Machine Learning Segmentation and Classification Algorithm to Support Simulated Point Cloud As-Built Model Applications

Machine Learning Segmentation and Classification Algorithm t...

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2021 International Conference on Computing in Civil Engineering, I3CE 2021

作者： Shen, Junzhe Ren, Ran Nicholas Dib, H. McGraw, Tim Habib, Ayman Dept. of Computer Graphics Technology West LafayetteIN United States School of Construction Management Technology West LafayetteIN United States School of Construction Management Technology Knoy Hall of Technology West LafayetteIN United States Lyles School of Civil Engineering West LafayetteIN United States

ISBN: (纸本)9780784483893

management and maintenance of existing buildings remains a major problem due to the lack of existing three-dimensional (3D) models and accurate as-built representation. In this paper the authors propose to use 3D scanner technology to capture the as-built and existing conditions of the buildings combined with building information modeling (BIM) as the underlying technology, which is a 3D semantic representation of all the life cycle phases of a building. This paper presents the results from creating as-built BIM models of existing buildings, using point cloud (a set of points in 3D space) and machine learning as an intermediate medium. Machine learning methodologies are used to speed up the computation of segmentation and classification of point clouds from a 3D virtual indoor environment using procedural modeling, which focused on two attributes, point density and the level of random errors. In this paper we will present findings on the evaluation of the performance of machine learning segmentation and classification algorithm based on the comparison of ten different point cloud data sets. Different sets of segmentation and classification models with comparison between models and within themselves were provided, which included the mean loss and accuracy between models with different point density. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.

关键词： Life cycle

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis

PhishGuard: A Convolutional Neural Network-Based Model for D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Md Robiul Islam Md Mahamodul Islam Mst. Suraiya Afrin Anika Antara Nujhat Tabassum Al Amin Dept. of Computer Science and Engineering Uttara University Dhaka Bangladesh Dept. of Informatics University of Oslo Oslo Norway Dept. of Building Engineering and Construction Management Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Dept. of Electrical and Electronics Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science American International University Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Abstract-Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models’ decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a $1 D$ Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.

关键词： Uniform resource locators Deep learning Analytical models Accuracy Phishing Organizations Feature extraction

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A comprehensive review of international renewable energy growth

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Energy and Built Environment 2024年

作者： Hassan, Qusay Algburi, Sameer Sameen, Aws Zuhair Al-Musawi, Tariq J. Al-Jiboory, Ali Khudhair Salman, Hayder M. Ali, Bashar Mahmood Jaszczur, Marek Department of Mechanical Engineering University of Diyala Diyala Iraq College of Engineering Al-Kitab University Kirkuk Iraq College of Medical Techniques Al-Farahidi University Baghdad Iraq Building and Construction Techniques Engineering Department College of Engineering and Engineering Techniques Al-Mustaqbal University Babylon51001 Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Department of Construction Engineering and Project Management Al-Noor University College Nineveh Iraq Faculty of Energy and Fuels AGH University of Science and Technology Krakow Poland

The study meticulously reviews international growth trends in renewable energy from 2010 to 2022, across various global regions. Utilizing a comprehensive methodology, the study systematically analyzes academic articles, policy documents, and industry reports to offer a holistic understanding of the progression and distribution of renewable energy practices. It scrutinizes the principal drivers propelling the adoption of renewable resources and identifies the prevalent challenges that impede their maximization. The study critically evaluates existing policies, infrastructural advancements, and technological innovations, assessing their effectiveness across diverse socio-economic landscapes. It delves into the environmental and economic impacts of transitioning to renewable energy, underlining the intricate balance between sustainable development and ecological conservation. The role of renewable energy as a pivotal player in climate change mitigation is explored, providing a balanced perspective of its potential to transform energy systems while recognizing the complexities in its widespread adoption. Additionally, the study outlines potential future trajectories for renewable energy growth, offering invaluable insights for policymakers, researchers, and investors. It underscores the necessity of evidence-based decision-making to navigate the intricacies of renewable energy adoption and capitalize on its opportunities. In essence, the research encourages an active and informed approach, guiding the international community towards a more sustainable and environmentally responsible energy future. © 2024

关键词： Climate change

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An experimental investigation of the integration of smart building components with building information model (BIM) 36

An experimental investigation of the integration of smart bu...

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36th International Symposium on Automation and Robotics in construction, ISARC 2019

作者： Afsari, K. Florez, L. Maneke, E. Afkhamiaghda, M. School of Construction Management Technology Purdue University United States Department of Architecture and Built Environment Northumbria University United Kingdom Department of Computer Graphics Technology Purdue University United States

building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed. © 2019 International Association for Automation and Robotics in construction I.A.A.R.C. All rights reserved.

关键词： Visualization

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Correction: Modeling of concrete-filled PVC tube columns confined with CFRP strips under uniaxial eccentric compression: machine learning and finite element approaches

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Journal of Big Data 2025年第1期12卷 1-2页

作者： Wang, Yan Elmeligy, Mohamed A. Isleem, Haytham F. Hamed, Asmaa Y. Qader, Diyar N. Sharaf, Mohamed Jangir, Pradeep Arpita Tejani, Ghanshyam G. School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin China Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia Jadara University Research Center Jadara University Irbid Jordan Department of Computer Science University of York York UK Construction and Building Department The Higher Institute of Engineering and Technology Luxor Luxor Egypt Civil Engineering Department University of Kirkuk Kirkuk Iraq Industrial Engineering Department College of Engineering King Saud University Riyadh Saudi Arabia University Centre for Research and Development Chandigarh University Gharuan Mohali India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of CSE Graphic Era Hill University Dehradun India Graphic Era Deemed Department of CSE To Be University Dehradun India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan Applied Science Research Center Applied Science Private University Amman Jordan

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Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ji, Chen Xu, Wenyang Zheng, Xiangtian Ahmed, Yasmeen Jamal, Saad Ahmed Imam, Fakhar Ali, Mohammed Saleh Muthanna, Maha Ibrahim Ullah, Sajid Kucher, Dmitry E. Nanjing University School of Geography and Ocean Science Nanjing210046 China Nanjing Institute of Technology Nanjing211167 China Mississippi State University Department of Building Construction Science College of Architecture Art & Design MS39762 United States Paris Lodron Universitat Salzburg Institute for Research and Advanced Studies Universidade de Evora Department of Computer Science University of South Brittany Department of Geoinformatics PortugalZ_GIS France Austria University of the Punjab Lahore Pakistan Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan Nangarhar University Department of Water Resources and Environmental Engineering Nangarhar 2600 Afghanistan RUDN University Department of Environmental Management Institute of Environmental Engineering Moscow117198 Russia

Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems' energy transfer, nutrients, and biogeochemical cycles. These eddies' precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely Swin Transformer U-Net (Swin-Unet) and SegFormer, to categorize ocean eddies from Sea Surface Temperature (SST) maps sourced from the Copernicus Marine Environment Monitoring Service (CMEMS). In contrast to traditional convolutional neural networks (CNNs) that have prevailed in the domain, Transformer-based models provide superior global attention mechanisms, facilitating greater feature extraction and segmentation precision. The models are trained on labeled SST datasets and assessed using Intersection over Union (IoU), Dice coefficient, precision, recall, and F1-score. Experimental findings demonstrate that Transformer-based designs surpass conventional CNN-based techniques, yielding enhanced generalization and superior accuracy in classifying cyclonic and anticyclonic eddies. This study illustrates the efficacy of attention based segmentation algorithms for resilient oceanographic applications. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Strength and Deformability of Cement Composites of Various Types

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IOP Conference Series: Materials Science and Engineering 2021年第5期1079卷

作者： I V Erofeeva D V Emelyanov D A Svetlov A A Matvievskiy I N Maksimova N I Makridin Department of Engineering and Computer Graphics National Research Ogarev Mordovia State University 68 Bolshevistskaya St. Saransk 430005 Russia Department of Building Materials and Technologies National Research Ogarev Mordovia State University 68 Bolshevistskaya St. Saransk 430005 Russia Soft Protector LLC 28 Himikov St. St. Petersburg 195030 Russia OJSC «MAKSMIR» 11 Novinsky Boulevard Moscow 121099 Russia Department of Quality Management and Construction Production Technologies Penza State University of Architecture and Construction 28 German Titov St. Penza 440028 Russia Department of Technology of Building Materials and Woodworking State University of Architecture and Construction 28 German Titov St. Penza 440028 Russia

It is shown that the construction of buildings and structures requires concrete with universal characteristics, primarily with compressive strength in the range of 35-200 (MPa) with high tensile strength, crack resistance and durability. Strength and elastic-plastic and other properties of concretes and other cement composites are regulated by creating their various formulations. In this regard, it is important to conduct comprehensive studies of the properties of composites in relation to their compositions. Cement composites of various compounding compositions, including fillers, plasticizers, pigments, and biocidal preparations, are considered. Attention is also paid to the technological aspects of preparing mixtures and manufacturing products, namely, the influence of activated water on the properties of concretes of mixing and hardening conditions. Indicators of strength, modulus of elasticity, and brittleness were obtained for compositions that differed in the type of plasticizer ('Melflux 1641 F', 'Melflux 5581 F', 'Fortrace–strong', 'Hidetal-P-5', 'Hidetal GP-9' gamma 'A'), a biocidal preparation (Teflex Antimould, Teflex Disinfectant, Teflex for Industrial sights), a pigment (iron oxide red, iron oxide yellow, green glauconito-vogo, meerkat iron). The influence on the properties of powder – activated concretes, rheologically active (rock flour), reactively active (micro - silica), fillers, as well as quartz sand fractions 0, 16 – 0.63 and 0.63-5.0 mm. The results of research showed an increase in the strength of concrete when using plasticizer 'Melflux 1641F' and activated mixing water in the technology of their preparation. To increase the strength and modulus of elasticity led to the introduction of rock flour and silica. The introduction of plasticizers led to a decrease in the elastic modulus, and the use of activated closing water does not affect this indicator. From the examined compositions, compositions with the addition of pigments are characterize

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Assessment of construction Materials Trends Salt Content from Scanned Images

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IOP Conference Series: Materials Science and Engineering 2021年第5期1079卷

作者： V V Afonin T F Yelchischeva D A Svetlov M M Zotkina I V Erofeeva D V Emelyanov Department of Automated Information Processing and Management Systems National Research Ogarev Mordovia State University 68 Bolshevistskaya Str. Saransk 430005 Russia Department of Architecture and building construction Tambov State Technical University 106 Sovetskaya Str. Tambov 392000 Russia Soft Protector LLC 28 Himikov Str. St. Petersburg 195030 Russia Department of Building Materials and Technologies National Research Ogarev Mordovia State University 68 Bolshevistskaya St. Saransk 430005 Russia Department of Engineering and Computer Graphics National Research Ogarev Mordovia State University 68 Bolshevistskaya St. Saransk 430005 Russia

Composite building materials subjected to such operational influences as high humidity and temperature have been investigated. As a result of the experiments carried out within 45 days of the samples, their scanned images were obtained, which were represented by RGB color models and processed in order to identify changes in their color properties. Samples of materials are ranked by revealing a tendency to salt formation on their surface. Depending on the change in the brightness of the samples during their exposure, the images of the samples are divided into two groups, one of which contains images that at the last exposure area have a higher level of brightness relative to the control sample, approaching white. The second group is characterized by lower brightness values also relative to the control sample. As a result, samples are identified, the images of which have a noticeable tendency to salt formation, which is controlled by the level of white brightness.

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