Permafrost region highway distress prediction method based on uncertainty cloud theory

Abstract

The invention discloses a permafrost region highway distress prediction method based on an uncertainty cloud theory. According to the implementation process, (1) data is collected, and an original database is established; (2) evaluation parameters are selected, and initial concept division is performed; (3) parameter cloud model concept zooming is evaluated, and feature values are extracted; (4) highway distress concept division is performed, and feature values are extracted; (5) inference rules are determined, a quantitatively expressed inference rule parameter list is obtained, and a rule base is constructed; (6) a qualitative inference generator of a three-dimensional multi-rule cloud model of permafrost region highway distress is established; (7) a rule generation base antecedent is accessed, and the rule with the highest activation degree is found; and (8) a rule generation base consequent is activated, and the highway distress degree is output. Through the method, the cloud modeltheory is applied to permafrost region highway distress prediction, fuzziness and randomness are comprehensively considered, and the method has higher adaptability.

Description

technical field [0001] The invention belongs to the technical field of road disease and risk prediction, and relates to a road disease prediction method in permafrost regions based on uncertainty cloud theory. Background technique [0002] The cloud model has the characteristics of macro precision, micro fuzziness, macro control, and micro uncontrollability. Its essential unit is the concept cloud composed of cloud droplets. The idea is to take into account both randomness and fuzziness. The cloud model is a two-way cognitive model between a qualitative concept represented by language value and its quantitative representation. It is used to reflect the uncertainty of concepts in natural language. It can not only be explained by classical probability theory and fuzzy mathematics, but also It reflects the relationship between randomness and fuzziness, especially the method of probability to study fuzziness, which constitutes the mutual mapping between qualitative and quantitat...

AI Technical Summary

Problems solved by technology

However, the prediction of road damage in permafrost regions is affected by many uncertain factors such as environment, engineering, and man-made, and the information expressing these factors itself has uncertainty and ambiguity. At present, most prediction methods are based on deterministic methods. The results cannot guide engineering practice very well, and it is necessary to explore a method that can deal with uncertainty and ambiguity in order to achieve simple and practical prediction results with high stability

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