Research on Construction Efficiency Based on Improved PCA-BP Neural Network Model

This paper proposes a construction efficiency analysis method based on an improved PCA-BP neural network model to address the challenges of construction efficiency in offshore photovoltaic, which is influenced by multiple factors and difficult to cope with by traditional management methods. This method integrates the dimensionality reduction advantages of PCA with the nonlinear fitting ability of BP neural networks, comprehensively identifying and classifying controllable and uncontrollable factors in project progress. The study collected extensive construction data and used PCA preprocessing to reduce data dimensionality and improve the effectiveness of model training. An improved BP neural network model was then used for deep learning analysis to assess and predict construction efficiency. The model can monitor critical path activities in real time, and adjust the allocation of personnel and resources in advance based on historical and real-time weather, site data, logistics and transportation to optimise the construction plan. This study further improved the efficiency and flexibility of schedule execution by comprehensively considering factors such as human resources and technical capabilities. Empirical research has shown that this method significantly improves the accuracy of construction efficiency prediction, provides dynamic adjustment strategies for project managers, and increases the success rate and overall efficiency of project execution.