Modeling duration of building renovation projects

• Published in: Journal of financial management of property and construction Vol. 28; no. 3; pp. 423 - 438
• Main Authors: Lawal, Haruna Sa'idu, Ahmadu, Hassan Adaviriku, Abdullahi, Muhammad, Yamusa, Muhammad Aliyu, Abdulrazaq, Mustapha
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 13.11.2023; Emerald Group Publishing Limited
• Subjects: Artificial neural networks; Building construction; Construction companies; Construction costs; Construction industry; Design factors; Error analysis; Financial management; Highway construction; Managers; Mathematical models; Model accuracy; Monte Carlo simulation; Multilayer perceptrons; Prediction models; Questionnaires; Radial basis function; Renovation & restoration; Urban planning; South Korea; Nigeria; Australia; Ireland; Project; Construction; Renovation; Prediction; Duration; Artificial neural network; Modeling
• ISSN: 1366-4387; 1366-4387; 1759-8443
• DOI: 10.1108/JFMPC-06-2022-0030

Purpose This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors. Design/methodology/approach The study used a questionnaire to obtain basic information relating to identified project scope factors as well as information relating to the impact of the non-scope factors on the duration of building renovation projects. The study retrieved 121 completed questionnaires from construction firms on tertiary education trust fund (TETFund) building renovation projects. Artificial neural network was then used to develop the model using 90% of the data, while mean absolute percentage error was used to validate the model using the remaining 10% of the data. Findings Two artificial neural network models were developed – a multilayer perceptron (MLP) and a radial basis function (RBF) model. The accuracy of the models was 86% and 80%, respectively. The developed models’ predictions were not statistically different from those of actual duration estimates with less than 20% error margin. Also, the study found that MLP models are more accurate than RBF models. Research limitations/implications The developed models are only applicable to projects that suit the characteristics and nature of the data used to develop the models. Hence, models can only predict the duration of building renovation projects. Practical implications The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it. Social implications The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it; it also helps clients to effectively benchmark projects duration and contractors to accurately estimate duration at tendering stage. Originality/value The study presents models that combine both scope and non-scope factors in predicting the duration of building renovation projects so as to ensure more realistic predictions.