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Improving Building Information Modeling (BIM) Implementation throughout the Construction Industry
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Building Information Modeling (BIM) is extensively used in the construction industry due to its benefits throughout the Project Life Cycle (PLC). BIM can simulate buildings throughout PLC, detect and resolve problems, and improve building visualization that contributes to the representation of actual project details in the construction stage. BIM contributes to project management promotion by detecting problems that lead to conflicts, cost overruns, and time delays. This work aims to implement an effective BIM for the Iraqi construction projects’ life cycle. The methodology used is a literature review to collect the most important factors contributing to the success of BIM implementation, interview the team of the Central Bank of Iraq (CBI) building, and strive to improve the BEP of the CBI building. However, previous studies indicate collaborative work and communications enhance effective BIM implementation, which can improve BIM use by applying a BEP and an AEC (UK) BIM protocol that leads to positive BIM impact. BEP comprises important information and goals related to the intended project, including the BIM collaborative process (process map), information exchange requirements, BIM data management, BIM model management, and quality control, which are considered essential for enhancing BIM collaboration during PLC. This paper concludes that implementing BIM effectively requires overcoming obstacles faced by Iraqi construction projects. Effective BIM implementation requires improving collaboration and communication throughout the construction process, which could be achieved by depending on the BIM Execution Planning Guide(BEP Guide) and the AEC (UK) BIM Protocol 2012 V2.0

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Publication Date
Mon Oct 30 2023
Journal Name
Iraqi Journal Of Science
Transfer Learning Based Traffic Light Detection and Recognition Using CNN Inception-V3 Model
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Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on

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Publication Date
Sat Oct 01 2016
Journal Name
Journal Of Theoretical And Applied Information Technology
Factors affecting global virtual teams’ performance in software projects
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