A real-time approach toward the chemical quality control of rock material (Case study: Gravel mines in Semnan, Iran

Document Type: Research Paper


1 Associate Professor, Faculty of Mining, Petroleum and Geophysics Engineering, University of Shahrood, Iran

2 Assistant Professor, Faculty of Civil Engineering, University of Shahrood, Iran

3 Ph.D. Candidate, Faculty of Civil Engineering, Isfahan University of Technology, Iran

4 Assistant Professor, Faculty of Electrical and Robotic Engineering, University of Shahrood, Iran

5 Mining Engineer, Zaminnegar Pasargad Co., Iran

6 Civil Engineer, Semnan Provincial Government


The quality of concrete is highly dependent on the characteristics of its aggregate, such as the size, minerals, and their chemical properties. Even a small amount of impurities, such as hydrated sulfates, chlorine (salt), and acidic pH of the rock material, can adversely affect the quality of the concrete. Thus, many national codes and standards are developed for testing, selecting, and employing the rock materials in concrete. For instance, Iranian standards 446, 449, 1702, 4978, 4984, 7174, and 86721 are currently serving this purpose. In the present research, a new real-time system was developed in order to replace the customary chemical analysis and size distribution tests. 20 samples were taken from two mines, selected by the Building Material Committee of Semnan Province, in order to determine the dissolved chlorine and sulfate, pH, density as well as size distribution. The new system is constituted of hydraulic jacks and a reservoir, designed to take samples from the conveyer in given time intervals. The samples were washed with distilled water and real-time analyses of dissolved chlorine and pH were performed. The results showed 85% agreement with the results from laboratory analyses. The correct classification rate (CCR) was 92% for 13 samples.


[1]. ISO, (1994), ISO 8402, Quality Management and Quality Assurance – Vocabulary. International Organization for Standardization, Geneva, Switzerland.
[2]. Lomas, S. (2004), QAQC Program. General Discussion. AMEC Internal document.
[3]. Sketchley, D. (1999), Case history guidelines for establishing sampling protocols and monitoring quality control. Proceedings of CIMM Annual General Meeting Symposium on Quality Control of Resource Estimations: an ISO Perspective.
[4]. Chatterjee, S. (2006). Geostatistical and image based quality control models for Indian mineral industry. Unpublished Ph.D. Thesis dissertation, IIT Kharagour, India, 272 pp.
[5]. Jimenez, A.R., Jain, A.K., Ceres, R., Pons, J.L. (2001). Automatic fruit recognition: a survey and new results using range/attenuation images. Pattern Recognition 32. 10: 1719–1736.
[6]. Miller, W., Jaskot, J., McCoy, B., and Schiller, E. (1996). A distributed system for 100% inspection of aluminum sheet products. International Conference on Signal Processing Applications and Technology (ICSPAT’96).

[7]. Khandogin, I., Kummert, A., Maiwald, D. (2000). DSP algorithms for the automatic inspection of fixing devices of railroad lines. International Conference on Signal Processing Applications and Technology (ICSPAT’98).
[8]. Hou, T. H., Pern, M. D. (2001). A computer vision-based shape-classification system using image projection and a neural network. International Journal of Advanced Manufacturing Technology 15: 843–850.
[9]. Yu, H. (2003). Development of vision-based Inferential Sensors for Process Monitoring and control. Ph.D. Thesis, McMaster University, Department of Chemical Engineering, Hamilton, Ontario, Canada. [10] Janannathan, S. (1997). Automatic inspection of wave soldered joints using neural networks. Journal of Manufacturing Systems 16:389–398.
[10]. Oyeleye, O., Lehtihet, E.A. (2000). A classification algorithm and optimal feature selection methodology for automated solder joint inspection. Journal of Manufacturing Systems 17: 251–262.
[11]. Oestreich, J. Tolley, W. and Rice, D. (1995). The development of a color sensor system to measure mineral compositions. Minerals Engineering 1.2:31-39.
[12]. Perez, C., Casali, A., Gonzalez, G., Vallebuona, G., and Vargas, R. (2000). Lithological composition sensor based on digital image feature extraction, genetic selection of features and neural classification. In Proc. of the Int. Con. on Information Intelligence & Systems, IEEE, Bethesda, pp. 236-241.
[13]. Casali, A., Gonzalez, G., Vallebuona, G., Perezq C. and Vargas, R. (2001). Grind ability Soft-Sensors Based on Lithological Composition and On-Line Measurements. Minerals Engineering 14.7:689-700.
[14]. Aydemir, S., Keskin, S., Drees, L.R. (2004). Quantification soil features using digital image processing (DIP) techniques. Geoderma 119.1:1-8.
[15]. Marmo, R., Amodio, S. (2005). Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples. Computers & Geosciences 31:649–659.
[16]. Donskoi, E., Clout, J.M.F. (2005). Recognition – a specialized software package for iron ore characterization. In Iron Ore 2005. Aus. IMM, Fremantle pp. 203–211.
[17]. Singh, V., Singh T.N., Singh V. (2010). Image processing applications for customized mining and ore classification. Int. J. Arab Geosci. DOI: 10.1007/s12517-010-0125-2.[19] Management and Planning Organization of Iran (MPOI). (1990). Iranian Concrete Code (ABA): Technical note # 120.
[18]. Khorram F. Memarian H. Tokhmechi B. Soltanian Zadeh H. (2012) Lithological Classification Using Image Processing Technique, SME Annual Meeting and Exhibition, Washington, USA, 4 pages.
[19]. Khorram F. Memarian H. Tokhmechi B. Soltanian Zadeh H. (2012) Limestone Chemical Component Estimation using Image Processing & Pattern Recognition Techniques, Journal of Mining and Environment, Vol. 2, No. 2, pp. 49-58.