A PDF file should load here. If you do not see its contents
the file may be temporarily unavailable at the journal website
or you do not have a PDF plug-in installed and enabled in your browser.
Alternatively, you can download the file locally and open with any standalone PDF reader:
https://link.springer.com/content/pdf/10.1007%2Fs40069-015-0097-4.pdf
Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN
International Journal of Concrete Structures and Materials
DOI 10.1007/s40069
1976-0485
Residual Strength of Corroded Reinforced Concrete Beams Using an Adaptive Model Based on ANN
Ashhad Imam
Fatai Anifowose
Abul Kalam Azad
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 (Cf) and through the residual strength (Mres). 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 Cf-based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.
corrosion; reinforced concrete beam; flexural strength; artificial neural networks
1. Introduction
Corrosion of reinforcement steel has been proved to be a
major cause of deterioration of reinforced concrete (RC)
structures, resulting in the reduction of the service life of
concrete structures. A substantial amount of research related
to reinforcement corrosion has been carried out in the past,
addressing various issues related to the corrosion process, its
initiation and damaging effects. Assessment of the flexural
strength of corrosion-damaged RC members has been
studied (Azad et al. 2010; Cabrera 1996; Huang and Yang 1997;
Rodriguez et al. 1997; Uomoto and Misra 1988). A number
of studies have also been conducted on the prediction of
residual flexural strength of corroding concrete beams (Azad
et al. 2007; Mangat and Elgarf 1999; Nokhasteh and Eyre
1992; Ravindrarajah and Ong 1987; Tachibana et al. 1990;
Wang and Liu 2008; Jin and Zhao 2001). Some of these
studies had been conducted in the laboratory. They involve
the casting of concrete beam specimens sometimes in large
scale, in the order of meters in dimension (Ou et al. 2012),
and sometimes in small scale, in the order of millimeters
(Azad et al. 2007; Mangat and Elgarf 1999; Nokhasteh and
Eyre 1992; Revathy et al. 2009; Tachibana et al. 1990; Wang
and Liu 2008; Jin and Zhao 2001). The specimens are then
subjected to various degrees of corrosion damage after
which the samples are tested for their bending or flexural
performances. These procedures take a lot of time as some of
the specimens need to be left for several days to attain their
required degree of corrosion. They also require the use of
expensive and specialized laboratory equipment, exorbitant
man-hours and concerted effort. An average experiment can
take up to 6 months to complete. Though, experiments are
the best sources of real data but the associated costs often
make them prohibitive.
In order to reduce the completion time and avoid the cost
associated with such studies without compromising on
accuracy, some attempts have been made on the use of
numerical modeling methods (Azad et al. 2007, 2010; Cabrera
1996; Coronelli and Gambarova 2004; Ou et al. 2012).
These methods are however static and cannot be generalized
well on datasets outside those for which they were designed.
Most of them do not consider the non-linearity of the
attributes of the natural phenomena involved in the corrosion
process. Since corrosion is a natural process, it is expected
that its attributes be non-linearly related to the corrosion
property being studied. Hence, modeling the process with
linear relations is inadequate. To make such models more
generalized, they need to be recalibrated with new sets of
data. Doing this will result in re-generating new sets of
coefficients to evolve a new model, which requires
considerable time and effort.
With the limitations in the experimental and theoretical
methods, the quest for cost-effective, easy to use and
adaptive models that offer scalability and efficient
generalization capability to new cases continues. With the huge
amount of data generated from various (...truncated)