Comparative analysis of soft computing techniques in predicting the compressive and tensile strength of seashell containing concrete
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Title
Comparative analysis of soft computing techniques in predicting the compressive and tensile strength of seashell containing concrete
Authors
Keywords
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Journal
European Journal of Environmental and Civil Engineering
Volume -, Issue -, Pages 1-23
Publisher
Informa UK Limited
Online
2022-07-22
DOI
10.1080/19648189.2022.2102081
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- (2011) Xuan Hong Vu et al. CONSTRUCTION AND BUILDING MATERIALS
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