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<p>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.</p>
</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.

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

Department of Electromechanical Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Accurate construction cost calculation is crucial for assessing project viability and selecting design programs. This paper enhances calculation accuracy by...

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

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 06 kwi 2024

Przyjęty: 20 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1042

Słowa kluczowe
Boruta algorithm, ANN, AdaBoost, Cost-sensitive method, Construction cost

© 2024 Yunfei Song, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 06 kwi 2024

Przyjęty: 20 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1042

Słowa kluczoweBoruta algorithm, ANN, AdaBoost, Cost-sensitive method, Construction cost

© 2024 Yunfei Song, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Boruta algorithm, ANN, AdaBoost, Cost-sensitive method, Construction cost