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dc.contributor.authorMEROUCHI, Fahima-
dc.contributor.authorBOUGHERBI, Rabah-
dc.date.accessioned2025-01-07T09:22:30Z-
dc.date.available2025-01-07T09:22:30Z-
dc.date.issued2024-
dc.identifier.urihttp://dspace.univ-bouira.dz:8080/jspui/handle/123456789/17720-
dc.description.abstractthis work delves into the realm of ar􀆟ficial intelligence (AI) and its profound impact on civil engineering prac􀆟ces. Through a me􀆟culous review of exis􀆟ng AI algorithms, such as ar􀆟ficial neural networks, gene􀆟c algorithms, random forests, and others, we explore their implementa􀆟on in civil engineering to evaluate and compute various parameters such as bearing capacity, structural health monitoring, and more. Addi􀆟onally mul􀆟ple AI frameworks are presented in this work, encompassing both open-source and commercial frameworks. A case study is included, where the bearing capacity of a shallow founda􀆟on is evaluated using the neural network algorithm. The bearing capacity is computed based solely on the outcomes of the dynamic probing test and soil density.en_US
dc.language.isofren_US
dc.publisheruniversité akli mohand oulhadj bouiraen_US
dc.subjectAr􀆟ficial Intelligence (AI), Civil Engineering, Bearing Capacity, Ar􀆟ficial Neural Networks (ANN), Frameworks of ar􀆟ficial intelligence.en_US
dc.titleThe implementa􀆟on of ar􀆟ficial intelligence (AI) in civil engineering: review and case studyen_US
dc.typeThesisen_US
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