<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postponement of delay of interim payments is at the forefront of delay factors caused by the employer’s decision. Even the least one is to leave the job site caused by the contractor’s second part of the contract, the repeated unjustified stopping of the work at the site, without permission or notice from the client’s representatives. The developed model was applied to about 97 projects and used as a prediction model. The decision tree model shows higher accuracy in the prediction.</p>
</abstract>

Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postponement of delay of interim payments is at the forefront of delay factors caused by the employer’s decision. Even the least one is to leave the job site caused by the contractor’s second part of the contract, the repeated unjustified stopping of the work at the site, without permission or notice from the client’s representatives. The developed model was applied to about 97 projects and used as a prediction model. The decision tree model shows higher accuracy in the prediction.

Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms

Civil Engineering Department, Construction Project Management

Civil and Environmental Engineering

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

Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay...

Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms

Online veröffentlicht: 09. Dez. 2021

Seitenbereich: 673 - 680

DOI: https://doi.org/10.2478/cee-2021-0066

Schlüsselwörter
Delay, Construction, Decision tree, Bayesian, Portfolio

© 2021 Kadhim Raheim Erzaij et al., published by Sciendo

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

Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms

Online veröffentlicht: 09. Dez. 2021

Seitenbereich: 673 - 680

DOI: https://doi.org/10.2478/cee-2021-0066

SchlüsselwörterDelay, Construction, Decision tree, Bayesian, Portfolio

© 2021 Kadhim Raheim Erzaij et al., published by Sciendo

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

Schlüsselwörter
Delay, Construction, Decision tree, Bayesian, Portfolio