Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

Abstract

Purpose

Knowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.

Design/methodology/approach

The study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.

Findings

The developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.

Originality/value

The developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

Citation

Adinyira, E., Adjei, E.A.-G., Agyekum, K. and Fugar, F.D.K. (2021), "Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)", Engineering, Construction and Architectural Management, Vol. 28 No. 5, pp. 1491-1514. https://doi.org/10.1108/ECAM-08-2020-0618