Application of Soft Computing Methods for the Estimation of Roadheader Performance from Schmidt Hammer Rebound Values

نوع مقاله: مقاله پژوهشی

نویسنده

Dept. of Mining, Arak University of Technology, Iran

چکیده

Estimation of roadheader performance is one of the main topics in determining the economics of underground excavation projects. The poor performance estimation of roadheader scan leads to costly contractual claims. In this paper, the application of soft computing methods for data analysis called adaptive neuro-fuzzy inference system- subtractive clustering method (ANFIS-SCM) and artificial  neural  network  (ANN) optimized  by  hybrid  particle  swarm  optimization  and  genetic  algorithm  (HPSOGA) to estimate roadheader performance is demonstrated. The data to show the applicability of these methods were collected from tunnels for Istanbul’s sewage system, Turkey. Two estimation models based on ANFIS-SCM and ANN-HPSOGA were developed. In these models, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, and net cutting rates constituted the output parameter. Various statistical performance indices were used to compare the performance of those estimation models. The results indicated that the ANFIS-SCM model has strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of Soft Computing Methods for the Estimation of Roadheader Performance from Schmidt Hammer Rebound Values

نویسنده [English]

  • Hadi Fattahi
Dept. of Mining, Arak University of Technology, Iran
چکیده [English]

Estimation of roadheader performance is one of the main topics in determining the economics of underground excavation projects. The poor performance estimation of roadheader scan leads to costly contractual claims. In this paper, the application of soft computing methods for data analysis called adaptive neuro-fuzzy inference system- subtractive clustering method (ANFIS-SCM) and artificial  neural  network  (ANN) optimized  by  hybrid  particle  swarm  optimization  and  genetic  algorithm  (HPSOGA) to estimate roadheader performance is demonstrated. The data to show the applicability of these methods were collected from tunnels for Istanbul’s sewage system, Turkey. Two estimation models based on ANFIS-SCM and ANN-HPSOGA were developed. In these models, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, and net cutting rates constituted the output parameter. Various statistical performance indices were used to compare the performance of those estimation models. The results indicated that the ANFIS-SCM model has strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

کلیدواژه‌ها [English]

  • Roadheader performance
  • Schmidt hammer rebound values
  • ANFIS-Subtractive clustering method
  • Artificial neural network
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