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Abstract
According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean Vtest / VANN =1.00) the Eurocode 2 provisions (mean VE,EC / VR,C =1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures.
Highlights
As the age of existing infrastructures is increasing, the question if existing structures are safe for further operation becomes important
For a more effective estimate of the shear capacity of one-way reinforced concrete slabs under a concentrated load, this paper proposes the use of artificial neural networks (ANN), a popular machine learning technique
Since the focus of this study is the assessment of reinforced concrete slab bridges in Europe, this section demonstrates the improved prediction capability of the ANN-based analytical model proposed in section 3, as compared to the shear capacity of one-way slabs predicted by the provisions of Eurocode 2 (CEN 2005)
Summary
As the age of existing infrastructures is increasing, the question if existing structures are safe for further operation becomes important. ANN’s computing power makes them suitable to efficiently solve small to large-scale complex problems, which can be attributed to their (i) massively parallel distributed structure and (ii) ability to learn and generalize, i.e, produce reasonably accurate outputs for inputs not used during the learning ( called training) phase. Examples or data points are selected to train the neural net, grouped in the so-called training dataset Those examples are said to be ‘labeled’ or ‘unlabeled’, whether they consist of inputs paired with their targets, or just of the inputs themselves – learning is called supervised (e.g., functional approximation, classification) or unsupervised (e.g., clustering), whether data used is labelled or unlabeled, respectively. One of the causes of overfitting might be learning too many input-target examples suffering from data noise, since the network might learn some of its features, which do not belong to the underlying function being modelled (Haykin 2009)
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