Journal Article

Study on Foundation Pit Construction Cost Prediction Based on the Stacked Denoising Autoencoder

Wu, Han, Liu, Denghui, Wang, Junwu, Liu, Lanjun

2020

Overview

To accurately predict the construction costs of foundation pit projects, a model based on the stacked denoising autoencoder (SDAE) is constructed in this work. The influencing factors of foundation pit project construction costs are identified from the four attributes of construction cost management, namely, engineering, the environment, the market, and management. Combined with Chinese national standards and the practice of foundation pit project management, a method of the quantization of the influencing factors is presented. 60 deep foundation pit projects in China are selected to obtain 13 main characteristic factors affecting these project construction cost by using the rough set. Then, considering the advantages of the SDAE in dealing with complex nonlinear problems, a prediction model of foundation pit project construction costs is created. Finally, this paper employs these 60 projects for a case analysis. The case study demonstrates that, compared with the actual construction costs, the calculation error of the proposed method is less than 3%, and the average error is only 1.54%. In addition, three error analysis tools commonly used in machine learning (the determination coefficient, root mean square error, and mean absolute error) emphasize that the calculation accuracy of the proposed method is notably higher than those of other methods (Chinese national code, the multivariate return method, the BP algorithm, the BP model optimized by the genetic algorithm, the support vector machine, and the RBF model). The relevant research results of this paper provide a useful reference for the prediction of the construction costs of foundation pit projects.

• Publisher: Hindawi Publishing Corporation,Hindawi,John Wiley & Sons, Inc
• Subject: Accuracy / Case studies / Construction contracts / Construction costs / Deep foundations / Engineering / Error analysis / Foundation construction / Genetic algorithms / Machine learning / Mathematical analysis / Mathematical problems / Neural networks / Noise reduction / Prediction models / Project management / Questionnaires / Regression analysis / Research methodology / Support vector machines