Abstract
Concrete possesses inherently durable characteristics due to having chemical and dimensional stability in most environments. This leads to the perception that reinforced concrete structures are expected to be relatively maintenance free. Unfortunately this is not the case as recent years have shown increased emphasis on the repair and refurbishment of all types of concrete structures, in preference to demolition and rebuilding.
New concrete repair methods and repair materials have been developed in order to keep up with the growing demand of the concrete repair industry. Diagnostic techniques are constantly upgraded in the hope of quantifying the extent and nature of the repair work to be undertaken. However, contract documents for concrete rehabilitation contracts are currently drawn up with a flexible approach, which is in favour of the contractor and not the client, as the volume and cost of the contract could escalate to unacceptable levels.
This dissertation investigates the development of a new technique to accurately predict concrete repair volumes. Artificial neural networks, digital image processing and software creation is combined to achieve what can be seen as the first step towards a quicker and more accurate concrete repair volume estimation. Once implemented, this could result in a revolution of current quantity surveying techniques used for the estimation of quantities in concrete repair projects.
Prof. P.C. Pretorius