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dc.contributor.authorAn Noor, Akib
dc.date.accessioned2024-06-25T10:25:12Z
dc.date.available2024-06-25T10:25:12Z
dc.date.issued2023-05
dc.identifier.otherID 22173007
dc.identifier.urihttp://hdl.handle.net/10361/23588
dc.descriptionThis project is submitted in partial fulfillment of the requirements for the degree of Masters of Science in Computer Science, 2023.en_US
dc.description.abstractThe system named ‘UBERA’ is a web application which is undertaken as a system which has the ability to assess buildings and produce an output result if they are vulnerable to earthquake or not. It is an automatic decision making system that evaluates the risk of buildings during an Earthquake. It utilizes modern Artificial Intelligence techniques along with civil engineering concepts to make the decision. A detailed structural evaluation is recommended to any building flagged as by the system. For building evaluations an effective system should be introduced in order to address this alarming earthquake risk. And with the help of this system anyone can access the system to know about any information about any types of buildings, its structural condition, awareness related information, evaluation facilities, automatic image processing result etc. This web application has several features, from google map API integration to the image processing facility of getting proper building evaluation results through the system. Moreover, generating a complete report page, maintaining the FEMA standard to calculate the threshold automatically are being developed as features of the system to make it an all in one building assessment and evaluation platform. The aim of the application is to develop awareness amongst general people about their building for any structural defects that might make the building vulnerable to earthquake and provide information with a proper report about the total building conditions.en_US
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.subjectEarthquakeen_US
dc.subjectDeep learningen_US
dc.subjectImage processingen_US
dc.subjectConvolutional neural networken_US
dc.subjectStructural health monitoringen_US
dc.subjectMachine learningen_US
dc.titleFull stack development of UBERA systemen_US
dc.typeOtheren_US


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