A framework for big data analytics approach to failure prediction of construction firms

Hafiz Alaka, Lukumon O. Oyedele, Hakeem A Owolabi, Muhammad Bilal, Saheed O. Ajayi, Olugbenga O. Akinade

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
114 Downloads (Pure)


This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.
Original languageEnglish
Pages (from-to)207-222
Number of pages16
JournalApplied Computing and Informatics
Issue number1/2
Early online date12 Mar 2018
Publication statusPublished - 2020

Bibliographical note

Published Under a Creative Commons Attribution (CC BY) license
Please note the publisher of this journal changed during the period the article was being published. The information reflected here is that recorded by Emerald, the publisher who took over the publication of Applied Computing and Informatics


  • Big data analytics
  • failure prediction models
  • construction businesses
  • machine learning
  • MapReduce/Spark
  • Construction businesses
  • Machine learning
  • Failure prediction models

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications


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