Using Machine Learning CART Decision Trees for Predicting the Causes of Delays in Projects from the Construction Industry

Authors

DOI:

https://doi.org/10.34190/ecmlg.20.1.3161

Keywords:

Project Management, project delay management, Decision Trees (DT), Machine Learning (ML)

Abstract

Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company’s financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with “The firm is not allowed to apply for an extension of contract period”, with two possible predictors, firstly, as the main factor it is found “Mistakes, inconsistencies, and ambiguities in specification and drawing”, while other aspect highlights “Poor site supervision and management by the contractor”. The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.

Author Biographies

Joao Alexandre Lobo Marques, University of Saint Joseph

PhD in Engineering. Vice-Rector for Research and Innovation and Associate Professor with Aggregation at the University of Saint Joseph, Macau SAR, China. Specialist in Project Management and Artificial Intelligence.

Chun Fung Ka, University of Saint Joseph

PhD in Business Administration. Head of Engineer and Project Management in several construction projects in Macau, Hong Kong and the Greater Bay Area, China.

Bruno Riccelli, Federal University of Ceara

Adjunct Professor at the Federal University of Ceará (UFC), Brazil. He received a B.Sc. degree in computer engineering, an M.Sc., and a Ph.D. in teleinformatics engineering from the same institution. His fields of interest include artificial intelligence, computer vision, CAD systems, biomedical systems, and neuromarketing.

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Published

2024-12-02