Ashac: An alternative towards classifying the shape of aggregate
Ariffuddin Joret, Mohammad Subhi Al-Batah, Ahmad Ali, Nor Ashidi Mat Isa, Muhammad Suhaimi Sulong · 2007
A number of image analysis applications are already available to classify the shape of aggregate. These applications were evaluated and compared with our new alternative application called Aggregate SHApe Classification system (ASHAC). This newly developed direct measurement methods have the potential to objectively classify two types of aggregate known as well-shaped (fine) aggregate and poor-shaped (coarse) aggregate. The shape properties of these aggregates used in hot-mix asphalt, hydraulic cement concrete, and unbound base and subbase layers are very important to the performance of the pavement system in which they are used in. In terms of its functionality and features, ASHAC provides more accuracy and more reliable system than others. It is an only system based on neural network using digital image processing technique. This methodology offers several advantages over current methods used in practice. Based on the overall performance, ASHAC has successfully classified the two categories of aggregate by 89.00%.