ABSTRACT
Accurate estimation of construction time has been identified as a major problem to the construction industry at large, several efforts have being made to develop models to predict duration of construction projects, researchers argued that development of models to predict duration of renovation projects is in its preliminary stage and went on to develop a model to predict duration of renovation projects. Existing models are scope based model and researchers have questioned the accuracy of scope based models as they do not include other non-scope factors which affect duration of projects. This study is aimed at developing a model which incorporate both scope and non-scope factors in predicting duration of building renovation projects. To achieve this aim, non-scope factors as well as project scope factors with good predictive abilities were firstly, identified from literature. A questionnaire survey was then used to obtain from construction firms; basic data/information relating to the identified project scope factors as well as data/information relating to impact of the non-scope factors on duration of building renovation projects. Out of a computed sample size of 357 TETfund building renovation projects, data relating to 121 completed TETfund building renovation projects were provided by the construction firms. Descriptive statistics was used to gain an overview of data obtained from the questionnaire survey, the study revealed that the most significant factors that affect duration of building renovation projects are variations, incomplete scope of work, material delivery, scarcity of materials, labour productivity, and owner financial capacity are the top five factors that influence duration of renovation projects. inferential statistics was used to determine the variance in duration of different categories of renovation projects and the ANOVA shows that statistical significant difference exist in duration of different types of renovation projects. 90% of the data was used to develop an artificial neutral network models on SPSS25. MAPE and T-Test were used to validate the models. 8 Artificia Nuera Network models were developed one multilayer perceptron (MLP) and radial basis function (RBF) model for each category. The accuracy of the models are 86%, 80%, 90%, 87%, 91%, 87%, 98%, 96% in this other; MLP(General), RBF(General), MLP(Block of offices and classroom), RBF(Block of offices and classrooms), MLP(Library), RBF(Library), MLP(Theatre), RBF(Theatre). The developed models will help project managers to accurately predict the duration of building renovation projects.