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  • 1
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    Application of Deep Learning on UAV-Based Aerial Images for Flood Detection (2021)
    Molecular Diversity Preservation International
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    Publication Date: 2021-09-18
    Description: Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.
    Electronic ISSN: 2624-6511
    Topics: Architecture, Civil Engineering, Surveying
    Published by Molecular Diversity Preservation International
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  • 2
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    Chapter Building Information Modelling (BIM) for Construction Supply Chain: A Scientometric Analysis (2024)
    Firenze University Press
    Details
    Publication Date: 2024-04-07
    Description: The automation and innovation have impacted Architecture, Engineering and Construction Industry, particularly when transitioning from traditional or conventional methods of construction to modular or Industrialized Building System (IBS). Thus, to ameliorate the processes surrounding built environment, researchers have been interested in the BIM’s integration into construction industry. To ensure BIM’s adoption and integration into construction supply chain, supply chain’s management and procurement, we need to have an extensive comprehensive research base regarding global outlook of BIM’s relation with supply chain. The purpose of this study is to identify global scientific research patterns and trends related to BIM’s role in supply chain, by performing scientometric analysis. The scientometric analysis will help us analyze the work being done in this field and whether a significant literature exists that supports or helps in adoption of this idea. Most of the already existing research on BIM is performed on various other aspects of BIM like infrastructure sustainability, green buildings, design, framework, management of facilities and other BIM related managerial aspects. Thus, it is highly imperative to systematize and analyze the existing global scientific literature research to identify the global trends and frontiers on current BIM’s relation with construction supply chain. Not only this would pave the way towards identification of current relevant literature but would also lay down the foundations for digital transformation in construction
    Keywords: Building information modelling (BIM) ; construction ; digitalisation ; procurement ; scientometric ; supply chain management ; thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization
    Language: English
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