Asset Details
Do you wish to reserve the book?
DSCostPred: a double-stacking model for construction cost prediction
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
DSCostPred: a double-stacking model for construction cost prediction
Do you wish to request the book?
DSCostPred: a double-stacking model for construction cost prediction
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
DSCostPred: a double-stacking model for construction cost prediction
2025
Overview
The prediction of construction project cost plays a core role in engineering construction projects. However, the current prediction involves a multi-dimensional and dynamically variable system, and each major category can be further subdivided into many specific factors. Meanwhile, variables’ relationships present a complex network of nonlinearity and interaction, which seriously affected the prediction accuracy. To solve this problem, we proposed a dual-stacking construction cost prediction method based on variable stacking and model stacking (DSCostPred). This method emphasizes that classifying variables and applying different algorithms respectively can avoid the impact of variables’ functional differences. First, the variables are pre-classified to avoid mutual interference among them. Then, to learn the attribute and function positioning, as well as the complex interaction among them, different types of models are utilized to learn the variables. In algorithm design, to achieve the organic combination of multiple attributes and multiple models, a variable stacking is introduced into stacking ensemble learning to form collaborative predictions with model stacking. This method was compared with the classical method on real data, and the results show the superior performance. In addition, the ablation experiments and SHAP analysis also demonstrated the feasibility of the double-stacking idea we proposed.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
