کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
260328 503657 2009 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Neural networks for predicting compressive strength of structural light weight concrete
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
پیش نمایش صفحه اول مقاله
Neural networks for predicting compressive strength of structural light weight concrete
چکیده انگلیسی

Neural networks procedures provide a reliant analysis in several science and technology fields. Neural network is often applied to develop statistical models for intrinsically non-linear systems because neural networks behave the advantages of simulating complex behavior of many problems. In this investigation, the neural networks (NNs) are used to predict the compressive strength of light weight concrete (LWC) mixtures after 3, 7, 14, and 28 days of curing. Two models namely, feed-forward back propagation (BP) and cascade correlation (CC), were used. The compressive strength was modeled as a function of eight variables: sand, water/cement ratio, light weight fine aggregate, light weight coarse aggregate, silica fume used in solution, silica fume used in addition to cement, superplasticizer, and curing period. It is concluded that the CC neural network model predicated slightly accurate results and learned very quickly as compared to the BP procedure. The finding of this study indicated that the neural networks models are sufficient tools for estimating the compressive strength of LWC. This undoubtedly will reduce the cost and save time in this class of problems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Construction and Building Materials - Volume 23, Issue 6, June 2009, Pages 2214–2219
نویسندگان
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