A pipeline health status assessment method based on random forest model

Abstract

Provided is a pipeline health state assessment method based on a random forest model, belonging to the city water supply pipe network technical field. The method comprises: respectively extracting the pipeline basic information and historic damage case from the foundation database and the damaged database of a city water supply network; performing data pre-processing on obtained pipeline information; utilizing a random forest model to establish the relation between an independent variable and a dependent variable, and evaluating model classification effects; utilizing the random forest model passing classification effect evaluation to predict the damage probability of the water supply pipe network; grading prediction results, representing health levels by different colors, and drafting a health state thematic map; and evaluating pipeline damage influence factor importance, and analyzing influence rules. The pipeline health state assessment method can assess pipe network health states, obtain prediction results fundamentally according with real conditions, effectively evaluate pipeline states, and provide certain theoretical support for water supply enterprises to make a plane of determining pipeline maintenance and reconstruction priorities, and optimizing maintenance.

Description

technical field [0001] The invention relates to a method for routinely evaluating the health status of pipelines, which belongs to the field of urban water supply pipeline networks. Background technique [0002] As an important part of urban infrastructure, the safe and efficient operation of urban water supply network is an important guarantee for people's normal life and development of production. At present, my country's urban water supply network has problems such as serious pipeline aging, difficult maintenance, backward management level, and poor maintenance and management, which inevitably lead to frequent damage accidents and affect the service level of the water supply system. On the one hand, this wastes a lot of high-quality water resources and increases the cost of water supply; on the other hand, it causes damage to underground public facilities, even hinders traffic, and disrupts citizens' life and production order. Therefore, it is imperative to carry out a p...

AI Technical Summary

Problems solved by technology

The direct detection method can obtain the operation status of the pipeline more accurately, but it often requires a large amount of capital investment, and the actual monitoring will be limited by the site and other conditions; the modeling analysis method saves manpower and material resources, and is a research hotspot of experts and scholars at home and abroad

[0004] There are many factors affecting the health of pipelines, and there are complex nonlinear relationships, and it is difficult to quantitatively evaluate the degree of influence; the construction level of my country's pipeline network database is lagging behind, the records of historical data are incomplete and inaccurate, and there is a lack of uniform standards, resulting in large differences

Existing pipeline evaluation methods mostly use Logistic generalized linear regression (CN102222169), genetic algorithm (CN102072409), analytic hierarchy process (CN103578045), neural network (CN103258243) and other methods to establish models, and these methods have subjectivity to varying degrees Strong, high data quality requirements, suitable for specific pipe networks, large amount of calculation, etc.

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