Monitoring Algorithm of Stress Point of Concrete Penstock in Large Construction Engineering

With the rapid development of the national construction industry, cracks and other problems often appear in the concrete structure during the initial and subsequent construction. When these problems develop further, the structural safety of the entire building may be compromised. Therefore, it is necessary to analyze the causes of cracks and other problems in concrete buildings, and be able to monitor and analyze these problems in time, and then propose reasonable solutions. This is already a problem that the entire construction technicians urgently need to solve. This paper studies the algorithm for monitoring stress points of concrete penstocks in large construction projects. Firstly, it uses literature research to explain the form of stress nodes in large-scale construction projects and the deficiencies in the research on the stress nodes of concrete penstocks in large-scale construction projects. In the experiment, the existing 3 algorithms are used to detect the force points, and compare their detection degree and false alarm rate. The experimental results show that the detection effect of the KNN algorithm is obviously inferior to the other two algorithms with the same neighbor parameters. Its detection rate is only 91%, and the false alarm rate reaches 30%. The other two algorithms are equivalent. The detection effect of the KNN algorithm is obviously inferior to the other two algorithms, the detection rate is poor, the outlier force points that are obviously deviating from the whole around the dense force points are not recognized, and the data of many normal force points located at the edge of the sparse area Instead, it was recognized as abnormal. Among the three algorithms, the detection rate of the NLOF algorithm is better, reaching 99%, which is significantly higher than the other two algorithms.