Classifying the shape of aggregate using hybrid multilayered perceptron network

Authors:
Ariffuddin Joret;Nor Ashidi Mat Isa;Ahmad Nazri Ali;Kamal Zuhairi Zamli;Khairun Azizi Azizli;M. Subhi M. Al Batah
Affiliations:
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia;School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia;School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia;School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia;School of Mineral and Material Resources Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia;School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia
Venue:
ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
Year:
2005

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Abstract

In concrete production, shape of aggregate reflects the quality of concrete produced. The well-shaped aggregates are said to produce high quality concrete by reducing water to cement ratio. On the contrary, poor-shaped aggregates often require higher water to cement ratio in concrete production. Conventionally, the quality of concrete is determined by calculating the ratio of well-shaped aggregate to poor-shaped aggregate contained in concrete. This procedure is slow, highly subjective and laborious, which is inefficient and expensive. In order to decrease the problems, this paper proposed an intelligent classification system for the aggregates using neural network. The system uses Zernike moments, Hu's moment invariants, area and perimeter of the aggregate's mass and boundary as input data for the neural network. The HMLP which is trained using MRPE algorithm, has been used as the classification system. The system produced 85.53% accuracy. This shows that the HMLP network has high capability to be used as intelligent shape classification system for aggregates.