An Efficient Randomized Algorithm for Real-Time Process Scheduling in PicOS Operating System

Helmy, Tarek; Anifowose, Fatai; Sallam, Elsayed A.

doi:10.1007/978-90-481-3660-5_20

Cited by 1 publication

(1 citation statement)

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“…In the latter, there is fairness as each sample in the data has equal chance of being selected for training or testing. This perfectly agrees with the theory of randomization (Arora and Barak 2009;Cormen et al 2001) and an existing study related to the subject (Helmy et al 2010).…”

Section: Resultssupporting

confidence: 91%

Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN

Imam

Anifowose

Azad

2015

International Journal of Concrete Structures and Materials

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Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor (C f ) and through the residual strength (M res ). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a C f -based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.

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Section: Resultssupporting

confidence: 91%