Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/33054
DC FieldValueLanguage
dc.contributor.authorMarkou, George-
dc.contributor.authorBakas, Nikolaos P.-
dc.contributor.authorvan der Westhuizen, Ashley Megan-
dc.date.accessioned2024-10-09T06:50:56Z-
dc.date.available2024-10-09T06:50:56Z-
dc.date.issued2023-01-01-
dc.identifier.citationArtificial Intelligence and Machine Learning Techniques for Civil Engineeringen_US
dc.identifier.isbn9781668456439-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/33054-
dc.description.abstractThe design and analysis of structures is performed with the use of national and international design codes that usually suggest the use of semi-empirical formulae. Often the formulae are oversimplified, in some cases are not available to engineers, or are time-consuming and challenging to implement. The objective of this chapter is to demonstrate the use of artificial intelligence and machine learning to develop more accurate formulae for different types of applications related to structural design. The applications that are discussed in this work include predicting the shear capacity of reinforced concrete slender and deep beams without stirrups, calculating the fundamental period of reinforced concrete and steel structures, and predicting the deflection of horizontally curved steel I-beams.en_US
dc.language.isoenen_US
dc.titleUse of AI and ML Algorithms in Developing Closed-Form Formulae for Structural Engineering Designen_US
dc.typeBook Chapteren_US
dc.collaborationUniversity of Pretoriaen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.subject.categoryENGINEERING AND TECHNOLOGYen_US
dc.subject.categoryCivil Engineeringen_US
dc.subject.categoryOther Engineering and Technologiesen_US
dc.journalsSubscriptionen_US
dc.countryGreeceen_US
dc.countrySouth Africaen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.4018/978-1-6684-5643-9.ch004en_US
dc.identifier.scopus2-s2.0-85167680572-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85167680572-
cut.common.academicyearemptyen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.openairetypebookPart-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-6891-7064-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Κεφάλαια βιβλίων/Book chapters
CORE Recommender
Show simple item record

Page view(s)

18
Last Week
3
Last month
checked on Nov 24, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.