Building Engineering Cost Prediction Model Based on TSNE and Improved Grey Correlation Algorithm

Abstract

With the development of digitalization in the construction industry, the complexity of construction projects is increasing. In response to the widely valued cost and quality management of construction projects, improved algorithms based on machine vision learning can effectively cope with the increasingly complex cost prediction of construction projects. In the entire model design, how to complete the selection and processing of key indicators for construction projects is currently a key issue that needs to be urgently solved. This article studied the use of TSNE (T-Distributed Stochastic Neighbor Embedding) and improved grey correlation algorithm to design a cost prediction model, aiming to improve the prediction accuracy of construction project costs and reduce system prediction time. This article analyzed the cost index system and simulation experiments of construction projects, applied machine learning based TSNE and improved grey correlation algorithm, combined index dimensionality reduction processing and construction project unit test set samples, and discussed the following conclusions based on the data results: In the context of intelligent large-scale infrastructure, the construction project cost prediction model using TSNE and improved grey correlation algorithm had higher prediction accuracy than traditional prediction methods in all construction project samples, with an average improvement of 5.1%. At the same time, in terms of model prediction efficiency, the improved algorithm was better than traditional prediction methods, with an average improvement of 12.75%. This indicated that the construction project cost prediction model based on TSNE and improved grey correlation algorithm has a relatively good prediction effect overall.