New Strategies for Intelligent Computing in Improving the Accuracy of Engineering Costs

Abstract

Accurate construction cost calculation is crucial for assessing project viability and selecting design programs. This paper enhances calculation accuracy by first employing the Boruta algorithm to identify vital cost-influencing factors, which serve as the basis for an improved construction cost model. We introduce an enhanced Artificial Neural Network (ANN) model that integrates the AdaBoost algorithm and cost-sensitive methods to refine construction cost estimations. The efficacy of this model is demonstrated through its overall engineering cost error rate of 3.92%, with specific errors in single-side cost, labor, materials, and machinery usage at 3.51%, 7.09%, 3.36%, and 7.93%, respectively. These results meet established accuracy standards, showcasing the model’s potential to significantly improve construction cost management and control.

Keywords

• boruta algorithm
• ann
• adaboost
• cost-sensitive method
• construction cost
• 03b70