A Big Data Analytics Approach for Construction Firms Failure Prediction Models

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

Research output: Contribution to journalArticle

2 Citations (Scopus)
33 Downloads (Pure)

Abstract

Using 693 000 datacells from 33 000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine, multiple discriminant analysis (MDA), and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN's number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art big data analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross validation was used for selection of the model with best parameter values, which were used to develop a new ANN model that outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost, but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise.

Original languageEnglish
Article number8438924
Pages (from-to)689 - 698
Number of pages10
JournalIEEE Transactions on Engineering Management
Volume66
Issue number4
Early online date17 Aug 2018
DOIs
Publication statusPublished - Nov 2019

Keywords

  • Big Data
  • Data models
  • Computational modeling
  • Biological system modeling
  • Analytical models
  • Tuning
  • Computers
  • support vector machines
  • Artificial neural networks
  • construction industry
  • machine learning
  • big data applications
  • predictive models

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Strategy and Management

Fingerprint Dive into the research topics of 'A Big Data Analytics Approach for Construction Firms Failure Prediction Models'. Together they form a unique fingerprint.

  • Cite this

    Alaka, H., Oyedele, L. O., Owolabi, H. O., Akinade, O. O., Bilal, M., & Ajayi, S. O. (2019). A Big Data Analytics Approach for Construction Firms Failure Prediction Models. IEEE Transactions on Engineering Management, 66(4), 689 - 698. [8438924]. https://doi.org/10.1109/TEM.2018.2856376