Abstract
Highway in permafrost regions has numerous diseases during operation, due to instability and degradation of permafrost. To predict distress sections of a newly built highway in permafrost regions, we proposed a new method based on the multidimensional and multirules reasoning cloud model. Herein, the evaluation parameters affecting the highway distresses in permafrost regions, i.e., annual average ground temperature, ice content, and frozen-heave factor, were as the data input, whereas the distress degree was as the data output; all of the aforementioned were described by a cloud model. Based on the analysis of distress large data, inference rules and a cloud reasoning prediction model were established. Subsequently, distress degrees of the 10 equidistance highway sections were predicted on the Qinghai-Tibet highway by using the cloud model, and actual distress degree and predicted distress degree were compared by using the regression analysis algorithm. The results showed that the relevance between the actual distress degree and the predicted distress degree was 0.738. The study provides a feasible and effective method to predict the potential distress sections of the newly built highway and better plan infrastructure project on permafrost regions.
Highlights
Owing to the complicated geological conditions and the fragile ecological environment in the permafrost regions, serious highway distresses that lead to significant economic losses can occur [1]
To predict distress sections of a newly built highway in permafrost regions, we proposed a new method based on the multidimensional and multirules reasoning cloud model
The prediction of highway distresses in permafrost regions has profound significance to obtain the potential distress sections and for a rational route planning; it is essential in assisting decision making and improving the quality of engineering in permafrost regions [2]
Summary
Owing to the complicated geological conditions and the fragile ecological environment in the permafrost regions, serious highway distresses that lead to significant economic losses can occur [1]. Some scholars used the fuzzy expert system, based on the division of changing factors of frozen soil, and established a prediction model of highway distresses in permafrost regions [10]. These methods still have many defects; they transform the uncertainty into deterministic formality; the generalization ability is weak and the accuracy is low. A cloud model is useful in the uncertain transforming between qualitative concepts and their expressions, which is proposed by academician De-Yi Li using fuzzy sets and the probability theory [16] It has unique advantages in handling fuzziness and randomness. We verified the feasibility and effectiveness of the proposed method through the analysis of the sample highway sections
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