Machine Learning Model for Predicting Construction Project Success in Tanzania

Astin Ntulo (), Elizabeth Mkoba, Dina Machuve and Sanket Mohan Pandhare
Additional contact information
Astin Ntulo: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Elizabeth Mkoba: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Dina Machuve: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Sanket Mohan Pandhare: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)

A chapter in Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, 2024, pp 105-112 from Springer

Abstract: Abstract The construction projects in Tanzania mainly use the post-implementation evaluation method to determine the project completion success. This traditional method led to the discovery of errors and mistakes in project execution only after the project was completed. These errors and mistakes can be avoided only if the construction project implementation were to be monitored in real time by using machine learning models. The purpose of this paper is to present a machine learning model for predicting construction project success in Tanzania using a random forest ensemble learning algorithm. Research methodology employed quantitative and qualitative methods. Data were collected from 26 regions of Tanzania’s mainland. A total of 100 respondents of which are contractors and consultants for construction projects were involved in data collection process through questionnaires and expert interviews to identify factors influencing construction project success. Generative Adversarial Networks (GANs) were used to expand the dataset to 1082. The model was developed, trained, and tested. The model had an accuracy of 97.5% and was deployed in a web-based application. The contribution of this study is to provide a tool that can be used by practitioners and policy makers to predict construction project success in Tanzania.

Keywords: Construction project; Project success; Machine learning; Generative adversarial network (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-56576-2_10

Ordering information: This item can be ordered from
http://www.springer.com/9783031565762

DOI: 10.1007/978-3-031-56576-2_10

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().