Abstract:
Excessive deflections of reinforced concrete slabs can cause severe serviceability
problems. In recent years, realistic estimation of slab deflection under service loads
has become more important due to the increasing use of high strength materials and
due to the ultimate limit state design, which generally result in thinner members.
Deflection calculations of slabs using nonlinear Finite Element (FE) analysis are
complicated and time consuming due to the fact that it is affected by cracking, creep
and shrinkage etc. The main objective of this work is to develop an easy method of
deflection estimation using Artificial Neural Network (ANN), which will be useful in
estimat;'1g deflection of edge supported slabs.
Hossain (1999) developed a nonlinear FE module, which was incorporated in the
finite element software FE-77 (Hitchings, 1994) to model the effect of cracking using
ACVBranson's equation. This has been used in the current work after proper
validation against experimental results. A large number of FE analysis has been
carried out on slabs with varying support condition, span, aspect ratio, loads, material
properties etc. and a database has been created for training the ANN prediction tool.
The purpose-built ANN program has been trained using the database until the
amount of error become very small. Once the network has been trained, the
prediction tool was validated against experimental and numerical results from
previous FE analysis. Use of the trained ANN software to estimate short-and long term
deflections has been demonstrated with example. Now, a designer will be able
to estimate deflection of an edge-supported slab for any span, aspect ratio, support
condition etc. easily by using the developed ANN prediction tool.
