Haruna Sa'idu Lawal, Hassan Adaviriku Ahmadu, Muhammad Abdullahi, Muhammad Aliyu Yamusa and Mustapha Abdulrazaq
This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors.
Abstract
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.
Details
Keywords
Hassan Adaviriku Ahmadu, Ahmed Doko Ibrahim, Yahaya Makarfi Ibrahim and Kulomri Jipato Adogbo
This study aims to develop a model which incorporates the impact of both aleatory and epistemic uncertainties into construction duration predictions, in a manner that is…
Abstract
Purpose
This study aims to develop a model which incorporates the impact of both aleatory and epistemic uncertainties into construction duration predictions, in a manner that is consistent with the nature/quality of information available about various factors which bring about uncertainties.
Design/methodology/approach
Data relating to 178 completed Tertiary Education Trust Fund (TETfund) building construction projects were obtained from construction firms via questionnaire survey. Using 90% of the data, the model was developed in the form of a hybrid-based algorithm implemented through a suitable user-friendly graphical user interface (GUI) using MATLAB programming language. Bayesian model averaging, Monte Carlo simulation and fuzzy logic were the statistical methods used for the algorithm development, prior to its GUI implementation in MATLAB. Using the remaining 10% data, the model's predictive accuracy was assessed via the independent samples t-test and the mean absolute percentage error (MAPE).
Findings
The developed model's predictions were found not statistically different from those of actual duration estimates in the 10% test data, with a MAPE of just 2%. This suggests that the model's ability to incorporate both aleatory and epistemic uncertainties improves accuracy of duration predictions made using it.
Research limitations/implications
The model was developed using a particular type of building projects (TETfund building construction projects), and so its use is limited to projects with characteristics similar to those used for its development.
Practical implications
The developed model's predictions are expected to serve as a useful basis for consultancy firms and contractor organisations to make more realistic schedules and benchmark measures of construction period, thereby facilitating effective planning and successful execution of construction projects.
Originality/value
The study presented a model which permits combined manipulation of aleatory and epistemic uncertainties, hence ensuring a more realistic incorporation of uncertainty into construction duration predictions.
Details
Keywords
Hassan Adaviriku Ahmadu, Yahaya Makarfi Ibrahim, Ahmed Doko Ibrahim and Muhammed Abdullahi
– This paper aims to develop a multivariate model that will be applicable to the Nigeria construction industry.
Abstract
Purpose
This paper aims to develop a multivariate model that will be applicable to the Nigeria construction industry.
Design/methodology/approach
A self-administered questionnaire survey was used to source information on project scope factors and qualitative factors considered in the study. Principal component regression was used for data analysis and model development, using SPSS 16.0 for windows, while T-test was used for model testing and validation.
Findings
The study found that delay in progress payment by owner, lateness in revising and approving design document by owner, delay in delivering the site to the contractor by the owner, change order by owner during construction, complexity of project design, poor site management and supervision by contractors, and rain effect on construction activities are qualitative/non-project scope factors with good predictive abilities.
Research limitations/implications
Cost, gross floor area and number of floors were the only quantitative/project scope factors considered in the study. The developed models therefore do not account for any variation in duration which may arise from other project scope factors, such as location, procurement route and type of contract.
Originality/value
The qualitative factors which emerged as predictors in the derived models increased the accuracy of the models. The models developed therefore serve as useful construction time prediction tools for both consultancy firms and contractor organizations in the Nigerian construction industry.