Application of machine learning algorithm in structural safety assessment of building engineering

The structural safety of construction engineering is an important topic in the construction industry, but the traditional structural safety assessment method has limitations. In this study, machine learning is used as a powerful data analysis tool, and support vector machine (SVM) algorithm is used to evaluate the structural safety of building engineering. The algorithm has the advantages of high efficiency, strong generalization ability, suitable for small sample learning and dealing with nonlinear problems. In order to reduce the complexity of the model, the feature selection method based on mutual information is studied to optimize the SVM algorithm, and the optimal parameter combination is determined by grid search and cross validation. Data sources include open data sets and field surveys, covering key information such as building structure design, material properties and use environment. After data preprocessing such as data cleaning, feature extraction and normalization, SVM model shows high prediction accuracy in identifying buildings with different damage degrees. Compared with logistic regression and random forest algorithm, the optimized SVM model shows superiority in accuracy, recall and F1 value. This model can help engineers to evaluate the structural safety of construction projects more quickly and accurately, and provide timely safety warning and risk assessment. This study not only provides an efficient and accurate method for structural safety assessment of construction projects, but also lays a foundation for the wider application of machine learning in the construction industry.