Machine learning - based framework for construction delay mitigation

The construction industry, for many decades, has been underperforming in terms of the success of project delivery. Construction delays have become typical of many construction projects leading to lawsuits, project termination, and ultimately dissatisfied stakeholders. Experts have highlighted the lack of adoption of modern technologies as a cause of underproductivity. Nevertheless, the construction industry has an opportunity to tackle many of its woes through Construction 4.0, driven by enabling digital technologies such as machine learning. Consequently, this paper describes a framework based on the application of machine learning for delay mitigation in construction projects. The key areas identified for machine learning application include "cost estimation", "duration estimation", and "delay risk assessment". The developed framework is based on the CRISP-DM graphical framework. Relevant data were obtained to implement the framework in the three key areas identified, and satisfactory results were obtained. The machine learning methods considered include Multi Linear Regression Analysis, K-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, and Ensemble methods. Finally, interviews with professional experts were carried out to validate the developed framework in terms of its applicability, appropriateness, practicality, and reliability. The main contribution of this research is in its conceptualization and validation of a framework as a problem-solving strategy to mitigate construction delays. The study emphasized the cross-disciplinary campaign of the modern construction industry and the potential of machine learning in solving construction problems

Machine learning model for delay risk assessment in tall building projects

Risky projects such as tall buildings have suffered an alarming rate of increase in delays and total abandonment. Though numerous delay studies predominate, what is lacking is constructive research to develop tools and techniques to wrestle the inherent problem. Consequently, this paper presents the development of a machine learning model for delay risk assessment in tall building projects. Initially, 36 delay risk factors were identified from previous literature, and subsequently developed into surveys to determine the likelihood and consequence of the risk factors. Forty-eight useable responses obtained from subject matter experts were used to develop a dataset suitable for machine learning application. K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble methods were considered. Feature subset selection revealed that the most relevant independent variables include “slowness in decision making”; “delay in sub-contractors work”; “architects’/structural engineers’ late issuance of instruction”; and “waiting for approval of shop drawings and material samples”. The final results showed that the best model for predicting the risk of delay was based on ANN with a classification accuracy of 93.75%. Ultimately, the model developed in this study could support construction professionals in project risk management of tall buildings

Causes of delay in the global construction industry: a meta analytical review

The construction industry continues to suffer from efficiency and productivity losses such as delays. The effects of delays may include time and cost overruns, litigation and project abandonment. Thus, the global research domain is saturated with studies investigating the causes of construction delay. However, despite the rising trend of construction globalization, a systematic review of what has been achieved so far in construction delay research is lacking. Such a study will synthesize extant data from previous studies to illustrate the global picture and will be of potential benefit to concerned stakeholders. Consequently, this study presents an overall review of studies on the causes of construction delays and executes a meta-data analysis utilizing Relative Importance Index (RII) values from some influential studies in the last 15 years. 36 common delay causes investigated globally were identified, and the effect summaries derived from the meta-analysis showed that the top five causes include: “contractor’s financial difficulties”, “delay in approval of completed work”, “slow delivery of materials”, “poor site organization and coordination between various parties”, and “poor planning of resources and duration estimation/scheduling”