Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models
This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 an...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2673-3951/6/2/35 |
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| author | Md. Mahfuzul Islam Shamim Abu Bakar bin Abdul Hamid Tadiwa Elisha Nyamasvisva Najmus Saqib Bin Rafi |
| author_facet | Md. Mahfuzul Islam Shamim Abu Bakar bin Abdul Hamid Tadiwa Elisha Nyamasvisva Najmus Saqib Bin Rafi |
| author_sort | Md. Mahfuzul Islam Shamim |
| collection | DOAJ |
| description | This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the studies—, enhance predictive accuracy and adaptability to complex, dynamic project environments. Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. ML models, including ANNs and deep learning models, represent approximately 70% of the reviewed studies, demonstrating a clear trend toward the adoption of advanced AI techniques. On average, deep learning models perform with 85–90% accuracy in cost estimation, making them highly effective for handling complex, nonlinear relationships and large datasets. Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. Hybrid models combine the strengths of different algorithms, achieving 80–90% accuracy on average, and are particularly effective in complex, multi-faceted projects. Overall, deep learning and hybrid models offer the highest accuracy in cost estimation, while machine learning and regression models still provide reliable results for specific applications. |
| format | Article |
| id | doaj-art-e0cc88de5d3e40baa80349e0cffa93b4 |
| institution | Kabale University |
| issn | 2673-3951 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Modelling |
| spelling | doaj-art-e0cc88de5d3e40baa80349e0cffa93b42025-08-20T03:29:43ZengMDPI AGModelling2673-39512025-04-01623510.3390/modelling6020035Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid ModelsMd. Mahfuzul Islam Shamim0Abu Bakar bin Abdul Hamid1Tadiwa Elisha Nyamasvisva2Najmus Saqib Bin Rafi3Faculty of Business, Information and Human Sciences (FBIHS), Infrastructure University Kuala Lumpur (IUKL), Kajang 43000, Selangor, MalaysiaFaculty of Business, Information and Human Sciences (FBIHS), Infrastructure University Kuala Lumpur (IUKL), Kajang 43000, Selangor, MalaysiaCenter for Postgraduate Studies, Infrastructure University Kuala Lumpur (IUKL), Kajang 43000, Selangor, MalaysiaPublic Administration, Jahangirnagar University, Dhaka 1342, BangladeshThis systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the studies—, enhance predictive accuracy and adaptability to complex, dynamic project environments. Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. ML models, including ANNs and deep learning models, represent approximately 70% of the reviewed studies, demonstrating a clear trend toward the adoption of advanced AI techniques. On average, deep learning models perform with 85–90% accuracy in cost estimation, making them highly effective for handling complex, nonlinear relationships and large datasets. Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. Hybrid models combine the strengths of different algorithms, achieving 80–90% accuracy on average, and are particularly effective in complex, multi-faceted projects. Overall, deep learning and hybrid models offer the highest accuracy in cost estimation, while machine learning and regression models still provide reliable results for specific applications.https://www.mdpi.com/2673-3951/6/2/35cost prediction modelsartificial intelligence (AI)machine learning (ML)project managementproject efficiency |
| spellingShingle | Md. Mahfuzul Islam Shamim Abu Bakar bin Abdul Hamid Tadiwa Elisha Nyamasvisva Najmus Saqib Bin Rafi Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models Modelling cost prediction models artificial intelligence (AI) machine learning (ML) project management project efficiency |
| title | Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models |
| title_full | Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models |
| title_fullStr | Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models |
| title_full_unstemmed | Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models |
| title_short | Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models |
| title_sort | advancement of artificial intelligence in cost estimation for project management success a systematic review of machine learning deep learning regression and hybrid models |
| topic | cost prediction models artificial intelligence (AI) machine learning (ML) project management project efficiency |
| url | https://www.mdpi.com/2673-3951/6/2/35 |
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