Application and improvement of variational autoencoder (VAE) in construction engineering material cost optimization based on big data

Abstract

In construction engineering, material procurement costs constitute a significant portion of total project expenditures. Conventional cost optimization approaches often fail to effectively respond to abnormal events such as sudden market price fluctuations and supply chain disruptions, leading to challenges in maintaining controlled cost management. To address this issue, this study introduces a deep learning-based framework utilizing a variational autoencoder (VAE) to enhance cost prediction accuracy and improve optimization strategies under dynamic conditions. The proposed approach begins with the collection and preprocessing of construction material cost data, including the handling of missing values, outliers, and duplicate records. Principal component analysis (PCA) is then applied to extract key features and reduce data dimensionality. A VAE model is subsequently constructed, in which both encoder and decoder networks map high-dimensional data into a compact latent space. Model parameters are optimized through reparameterization techniques using the Adam optimizer to minimize reconstruction error and Kullback–Leibler (KL) divergence.