Project quality visual supervision system and method applied to municipal construction

CN120410338AActive Publication Date: 2025-08-01QINGDAO WEST COAST URBAN CONSTR GRP CO LTD
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Patent Information

Application Number
CN202510906167.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-02
Publication Date
2025-08-01
Estimated Expiration
2045-07-02

AI Technical Summary

Technical Problem

The existing municipal bridge engineering quality supervision technology has failed to effectively deal with the differences in geographical characteristics, resulting in improper configuration of construction parameters and neglecting environmental factors such as high altitude, resulting in excessively rapid intensity decay in the early stage of service of bridges, and lack of dynamic analysis of parameter decay during service cycle.

Method used

Build a multi-output regression model, combine geographical features to form spatial feature vectors, optimize construction parameters by improving genetic algorithms, combine BIM technology and LSTM model for real-time monitoring and early warning, and dynamically adjust construction parameters.

Benefits of technology

Automatic adjustment of construction parameters in different geographical areas is achieved, dynamically reflecting the degradation process of bridge performance, and improving the accuracy and prediction capabilities of project quality supervision.

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Abstract

The invention discloses a project quality visual supervision system and method applied to municipal construction, and relates to the technical field of intelligent construction, and the method comprises the following steps: constructing a construction parameter set and an effect parameter set; acquiring historical bridge construction sample data, and processing geographic features to form spatial feature vectors; parameter coding and normalization are completed; calculating an effect parameter attenuation rate, building a multi-output regression model, splicing construction parameters and spatial features as inputs, and training the model by using a WMSE loss function; constructing an optimization function by taking the attenuation rate as a target, and solving an optimal parameter combination by using an improved genetic algorithm in combination with constraint conditions; a visual platform is constructed, digital twin bodies are constructed, effect parameters are predicted based on a sensor and an LSTM model, different model prediction values are compared, and early warning is performed when a threshold value is exceeded. According to the invention, the conditions of lack of dynamic adaptation to the environment and insufficient utilization of the parameter attenuation process in the prior art can be effectively improved.
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Citation Information

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