Building Information Modelling (BIM) is one of the most visible aspects of a deep and fundamental change that is rapidly transforming the global construction industry. It is the platform that brings about collaboration between project stakeholders and improvement of project outcomes. The growing worldwide adoption and implementation of BIM for its powerful data-based modeling, visualization, analysis and simulation capabilities represents a paradigm shift to an integrated digital information infrastructure that will ultimately revolutionize almost all aspects of the construction industry. Many developed economies of the world have recorded impressive outcomes by implementing BIM in their construction practices, and this necessitates the need for an investigation into the level of its awareness and factors affecting its adoption in the Nigerian Construction Industry. The study was undertaken through a survey of Nigerian Building construction firms. The central issues addressed were the awareness of the respondents on BIM, and their perception on the drivers and barriers to its adoption in the Nigerian construction industry. Structured questionnaires and semi-formal interviews were used for data collection. The study highlighted areas requiring attention by researchers, government and other stakeholders towards a country wide implementation of BIM technologies and has set a scene for developing a framework for BIM adoption in the Nigerian construction industry. This will make the industry equipped to operate in line with the global best practices and deliver projects successfully and more efficiently and also be a good market for foreign firms to benefit. BACKGROUND

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.

Purpose The revolution brought about by the internet and the World Wide Web has led to the development of numerous e-Tendering systems for public sector tendering that have automated various aspects of the manual tendering processes that are known to experience numerous problems. However, one key area that has not been fully addressed is the automation of the evaluation of public tenders based on group decision-making. This paper presents part of the development of a Web-based e-tendering system called Nigerian Public Sector eTender (NPS-eTender) that automate the evaluation of public sector tenders based on group decision-making. Design/methodology/approach The system was developed using object-oriented methodologies. Specifically, Ripple and unified process methodologies were adopted. Findings The results of the system validation showed that NPS-eTender has an average rating of 74% with respect to correct and accurate modelling of the existing tendering domain and an average rating of 67.6% with respect to its potential to enhance the proficiency of public sector tendering in Nigeria. Based on the results of the validation, it can be concluded that the automation of the tender evaluation process can lead to a more proficient tendering process. Originality/value This research has contributed to the development of an e-Tendering system for the public sector that supports the whole tendering lifecycle including the automation of evaluation of public tenders based on group decision-making.

The construction industry makes a significant contribution to the growth and development of every economy, by providing infrastructure for other productive ventures, shelter to the citizens and generating employment to people of different levels of knowledge and skills. In Nigeria, the construction industry contributes an average of over 3% to the annual gross domestic product and an average of about one-third of the total fixed capital investment. Despite the huge potentials of the Nigerian construction industry, little attention is given to its significance in driving the Nigerian economy to greater heights. This study explored the empirical evidence of causal relationship between the growth and development of the Nigerian economy and that of the construction sector. Econometric techniques such as unit root test, Granger causality test and Johansen's co-integration test were conducted to establish the actual relationship between the output of the construction sector (CS) and the gross domestic product (GDP) of the country. Twenty six years' time series data for the CS and the GDP between 1990 to 2015 was obtained from the statistics database of the Central Bank of Nigeria (CBN) and used for the analyses. The research revealed that despite the harsh economic realities facing the country in recent times, there exist a bi-directional linkage between the CS and the GDP of Nigeria. Each of them precedes the other by one year. The study recommends that any effort to diversify the Nigerian economy should consider revamping the construction industry for improved productivity in order to benefit from its significant positive effects on the economy.