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dc.contributor.advisorAshour, Ashraf F.
dc.contributor.advisorKhan, Amir
dc.contributor.advisorDai, Xianghe
dc.contributor.authorEl-Khoja, Amal M.N.
dc.date.accessioned2021-02-18T11:01:33Z
dc.date.available2021-02-18T11:01:33Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10454/18351
dc.description.abstractVery limited research studies have been conducted to examine the behaviour of rubberised concrete (RuC) with nano silica (NS) and addressed the acoustic benefits of rubberised concrete. The current research investigates the effect of incorporating colloidal nano silica on the mechanical, thermal and acoustic properties of Rubberised concrete and compares them with normal concrete (NC). Two sizes of rubber were used RA (0.5 – 1.5 mm) and RB (1.5 – 3 mm). Fine aggregate was replaced with rubber at a ratio of 0%, 10%, 20% and 30% by volume, and NS is used as partial cement replacement by 0%, 1.5% and 3%. A constant water to cement ratio of 0.45 was used in all concrete mixes. Various properties of rubberised concrete, including the density, water absorption, the compressive strength, the flexural strength, splitting tensile strength and the drying shrinkage of samples was studied as well as thermal and acoustic properties. Experimental results of compressive strength obtained from this study together with collected comprehensive database from different sources available in the literature were compared to five existing models, namely Khatib and Bayomy- 99 model, Guneyisi-04 model, Khaloo-08 model, Youssf-16 model, and Bompa-17 model. To assess the quality of predictive models, influence of rubber content on the compressive strength is studied. An artificial neural network (ANN) models were developed to predict compressive strength of RuC using the same data used in the existing models. Three ANN sets namely ANN1, ANN2 and ANN3 with different numbers of hidden layer neurons were constructed. Comparison between the results given by the ANN2 model and the results obtained by the five existing predicted models were presented. A finite element approach is proposed for calculating the transmission loss of concrete, the displacement in the solid phase and the pressure in the fluid phase is investigated. The transmission loss of the 50mm concrete samples is calculated via the COMSOL environment, the results from the simulation show good agreement with the measured data. The results showed that, using up to 20% of rubber as fine aggregate with the addition of 3% NS can produce a higher compressive strength than the NC. Experimental results of this research indicate that incorporating nano silica into RuC mixes enhance sound absorption and thermal conductivity compared to normal concrete (NC) and rubberised concrete without nano silica. This work suggests that it is possible to design and manufacture concrete which can provide an improvement to conventional concrete in terms of the attained vibro-acoustic and thermal performance.en_US
dc.description.sponsorshipLibyan Ministry of Higher Educationen
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectRubberised concreteen_US
dc.subjectNano silicaen_US
dc.subjectMechanical propertiesen_US
dc.subjectThermal conductivityen_US
dc.subjectAcoustic propertiesen_US
dc.subjectPredictionen_US
dc.subjectArtificial neural networksen_US
dc.subjectAcoustic performanceen_US
dc.subjectThermal performanceen_US
dc.titleMechanical, thermal and acoustic properties of rubberised concrete incorporating nano silicaen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2019
refterms.dateFOA2021-02-18T11:02:04Z


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