Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher

• Published in: International journal of approximate reasoning 1997, Vol.16 (1), p.43-66
• Main Authors: den Hartog, M.H., Babuška, R., Deketh, H.J.R., Alvarez Grima, M., Verhoef, P.N.W., Verbruggen, H.B.
• Format: Article
• Language: English
• Subjects: decision support systems; knowledge-based fuzzy modeling; mechanical excavation; performance prediction; rock cutting trencher; trenching
• ISSN: 0888-613X; 1873-4731
• DOI: 10.1016/S0888-613X(96)00118-1

A knowledge-based fuzzy model for performance prediction of a rock-cutting trencher has been developed. A trencher is a machine that uses a rotating cutting chain equipped with bits to excavate trenches in rock and soil. The performance of a trencher, and consequently the cost of a specific excavation project, is determined by its production rate and by the bit consumption (due to wear and breakage). Both these factors depend on the properties of the excavated rock material and on the trencher characteristics. Mathematical modeling of the trencher performance is difficult, since the interactions between the machine tool and the environment are dynamic, uncertain, and complex. The number of available measurements is too small to use statistical methods. Hence, an approach based on expert knowledge was applied to develop a rule-based fuzzy model. The use of fuzzy logic allows for smooth interfacing of the qualitative information involved in the rule base with the numerical input data. The developed model uses six input variables [rock strength, spacing of three joint (discontinuity) sets in the rock mass, joint orientation, and trench dimensions] to predict the production rate and bit consumption in terms of qualitative linguistic values. Numerical predictions are obtained by using a modified fuzzy-mean defuzzification which allows for straightforward adaptation of the consequent membership functions in order to fine-tune the model performance to the data. The expert knowledge is coded as if-then rules, hierarchically organized in four rule bases. The model was validated both qualitatively using dependency analysis and quantitatively using the available data. The results obtained so far are satisfactory.