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Purpose – The purpose of this study was develop a computer-based cost prediction model for institutional building projects in Nigeria through the use of artificial neural network (ANN) technique. The back-propagation network learns by example and provides good prediction to novel cases. Design/methodology/approach – The input variables were derived from related works with modification and advices from professionals through a field survey. Two hundred and sixty completed project data were used for training and development of the ANN model. Back-propagation algorithm using the gradient descent delta learning rule with a learning coefficient of 0.4 was used. The input layer of the model comprised of nine variables; building height, compactness of building, construction duration, external wall area, gross floor area, number of floors, proportion of opening on external walls, location index and time index. Findings – Several multi-layer perceptron networks were developed with varying architecture from which the network 9-7-5-1 was selected. The performance of the model over the validation sample revealed that the model has a mean absolute per cent error of 5.4 per cent and average error of prediction of −2.5 per cent over the sample. The ANN model was considered to be effective for construction cost prediction. Research limitations/implications – The model may not be suitable for other building types because of the uniqueness of such facility even though significant difference is not anticipated for buildings such as commercial and residential. The models were evaluated based on the prediction errors; other means of evaluation were not used. Originality/value – The study thus provides a simple, yet effective means of predicting construction costs of institutional building projects in Nigeria using an ANN model.

Artificial Neural Networks has gained considerable application in construction engineering and management in recent time. Over 100 resources published in refereed journals and conference proceedings were screened and reviewed with the view to exploring the trend and new directions of the applications of different ANN algorithms. The study revealed successful applications of ANNs in cost prediction, optimization and scheduling, risk assessment, claims and dispute resolution outcomes and decision making. It was observed that ANN have been applied to problems that are difficult to solve with traditional mathematical and statistical methods. The integration of ANN with other soft computing methods like Genetic Algorithm, Fuzzy Logic, Ant Colony Optimization, Artificial Bee Colony and Particle Swarm Optimization were also explored which generally indicated better results when compared with conventional ANNs. The study provides comprehensive repute of ANN in construction engineering and management for application in different areas for improved accuracy and reliable predictions.

The need to meet the massive infrastructural gaps has led to the adoption of alternative procurement methods. Build Operate and Transfer (BOT) is one of the new ways used for procuring infrastructure. In developing countries, BOT projects are characterised by high-risk profile discouraging private investment. Therefore, it is imperative to identify the critical risk factors inherent in such arrangements with the view to attracting the desired level of private investment. This study employed Pareto Analysis to identify vital risk factors of BOT projects in Nigeria. Structured questionnaires were used to establish critical risk factors based on the perception of key stakeholders (government, concessionaire, lenders, and developers) in Abuja. Kaduna, Port Harcourt and Lagos. Descriptive statistics were used to obtain Standard Deviation of the risk factors indicating their impacts and severity. Based on the results, Pareto Analysis was carried out to separate the /'vital few' from the 'trivial many'. The results indicated nine risk factors as the vital few responsible for 80% contribution. The risk factors include; changes in government policies, hostile general business environment, project company default, time performance risk, cost performance risk, excessive development cost, instability in government, failure to raise finance for the project and lack of experience in handling the project. Therefore, for effective implementation of BOT projects, it is necessary for stakeholders to focus on the /'vital few' risk factors responsible for 80% of the risk impacts. The results of the study may not be generalised for use by clients and contractors operating in environments with different political and economic climate with Nigeria as the impact and likelihood of occurrence of risks may vary. Keywords: Build-operate-transfer, Nigeria, Pareto analysis, Risk factors.

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