BIM-based multi-source detection system and detection method of operating bridge

Abstract

The invention discloses a BIM-based multi-source detection system and detection method of an operating bridge. On the basis of the BIM technology, the unmanned aerial vehicle and the three-dimensionallaser detection technology are integrated into the bridge disease detection, so that the bridge inspection efficiency and precision are improved greatly. An image processing method based on a sub-pixel difference is proposed, so that a defect of poor precision of the existing hardware equipment is overcome to a certain extent. Therefore, the bridge operation and maintenance inspection efficiencyare improved substantially; the informationization and intelligence degrees of the operation and maintenance decision-making system of the bridge are increased; and thus operation and maintenance decision-making of the bridge becomes scientific.

Description

technical field [0001] The invention relates to the technical field of bridge detection and safety evaluation, in particular to a BIM-based multi-source detection system and detection method for operating bridges. Background technique [0002] At present, the demand for operation and maintenance technology of bridge infrastructure is gradually increasing, while the operation and maintenance work still uses the mode of manual inspection, empirical decision-making and treatment, which has low detection efficiency, slow speed, and is easily affected by human factors to cause disease detection deviations. Document data is difficult to form a chain for analysis, maintenance data is scattered, and diseases are difficult to accurately visualize and present problems. It cannot be well adapted to the current process of information engineering, so that the maintenance decision-making of bridges in the later period lacks the most basic accurate and comprehensive maintenance data. [0...

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Problems solved by technology

[0002] At present, the demand for operation and maintenance technology of bridge infrastructure is gradually increasing, while the operation and maintenance work still uses the mode of manual inspection, empirical decision-making and treatment, which has low detection efficiency, slow speed, and is easily affected by human factors to cause disease detection deviations. Document data is difficult to form a chain for analysis, maintenance data is scattered, and diseases are difficult to accurately visualize and present problems, etc.

It cannot be well adapted to the current process of information engineering, so that the maintenance decision-making of bridges in the later stage lacks the most basic accurate and comprehensive maintenance data

[0003] At present, UAV aerial survey technology is gradually being applied to the disease detection process of transportation infrastructure. It can quickly complete the extraction of existing surface information of objects based on manual remote control. However, when it is applied to bridge disease detection, it is often only The disease information on the bridge surface can be obtained, and most of them are concentrated on the upper part and both sides of the bridge. There is a lack of information on the sheltered part of the lower part of the bridge and the corresponding mechanical data inside the bridge, so it is impossible to independently and comprehensively complete the rapid detection of bridge diseases

In addition, the image accuracy of UAV low-altitude aerial survey is restricted by the current hardware facilities. Often, one image pixel spans the entire width of small cracks, and it is difficult to obtain accurate measurement data of cracks.

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