Intelligent Detection and Automatic Repair of Cement Pole Defects Based on Deep Reinforcement Learning

Cement poles are an important part of building construction, and their quality problems directly affect the success of the entire project. In order to solve this problem, this paper adopts deep reinforcement learning technology and uses network analysis technology to build a model for cement pole detection and repair. The model includes front-end acquisition terminal equipment, as well as functional modules such as sensor arrays and embedded microprocessors, which perform operations such as data processing, positioning calculation and artificial neural network fusion in real time. Through this system, the location of steel bar defects during construction is identified, surface stress and strain are measured, and the mechanism of crack formation is studied. Through deep learning methods, cement poles are tested and optimized to improve their quality and durability. Test results show that the deep reinforcement learning algorithm requires more interactive samples in real environments to achieve higher accuracy, with an accuracy rate of over 94%; the scalability can reach up to 0.85. This model can not only effectively improve the quality control capabilities of cement poles, but also has high practicability and operability, and can provide strong technical support and guidance for the building construction industry.