Search results

Construction economics scholars have emphasised the importance of construction output forecasting and have called for increased investment in infrastructure projects due to the positive relationship between construction output and economic growth. However, construction output tends to fluctuate over time. Excessive changes in the volume of construction output have a negative impact upon the construction sector, such as liquidation of construction companies and job losses. Information gleaned from extant literature suggests that fluctuation in construction output is a global problem. Evidence indicates that modelling of construction output provides information for understanding the factors responsible for these changes.

An interpretivist epistemological lens is adopted to conduct a systematic review of published studies on modelling of construction output. A thematic analysis is then presented, and the trends and gaps in current knowledge are highlighted.

It is observed that interest rate is the most common determinant of construction output. Also revealed is that very little is known about the underlying factors stimulating growth in the volume of investment in maintenance construction works. Further work is required to investigate the efficacy of using non-linear techniques for construction output modelling.

This study provides a contemporary mapping of existing knowledge relating to construction output and provides insights into gaps in current understanding that can be explored by future researchers.

Communication plays an important role in the quality of the relationship, trust and collaboration among construction project teams. Literature suggests that effective communication is vital for achieving improved project performance. The purpose of this paper is to identify and assess the barriers to effective communication in the Nigerian construction industry, using attribution theory paradigm.

Questionnaires detailing 15 barriers to effective communication were administered to consultants and contractors engaged in the Nigerian construction sector. In total, 100 valid responses were analysed using mean score, factor analysis and factor score.

Unclear project objectives, ineffective reporting systems and poor leadership were ranked as the most significant barriers to effective communication. Factor analysis uncovered that the principal reasons responsible for ineffective communication include: managerial and technical barriers and credibility and background barriers.

The results provide information on barriers which needs to be addressed in order to improve communication in construction projects in Nigeria. There is a need to conduct post-completion reviews of construction projects so that project team members can learn and share knowledge on past experiences. Although the present study was conducted in the Nigerian context, it is envisaged that the research outcome will be widely applicable to other developing countries.

The study provides insights on the perceived barriers to effective communication in Nigerian construction projects. The paper presents the direction for further research aimed at improving communication in construction projects in Nigeria.

Unresolved construction dispute can be detrimental to project success. A systematic method to evaluate the seriousness of construction dispute of a construction project will assist management to take appropriate corrective actions. Evaluation of construction disputes requires an analysis on both the likelihood of occurrence and its impact, which are normally expressed by practitioners in linguistic terms. The application of classical discrete analysis will not be able to accommodate the fuzzy nature of this information. To determine how fuzzy sets theory can be applied to construction disputes evaluation (CDE), a fuzzy CDE model has been developed based on the knowledge extracted from practitioners in Hong Kong. The fuzzy CDE system consists of four components: dispute identification, dispute analysis; dispute evaluation; and dispute control. This paper describes the framework and operation of the fuzzy CDE system developed. The results indicate that CDE can be modelled by the fuzzy sets theory.

The fragmented and highly competitive nature of the construction industry (CI) limits efficiencies and inhibits communication. However, through the effective use of information technology (IT), these communication barriers between clients, consultants, suppliers, subcontractors and contractors can be broken down. Public sector clients in Australia have proposed an IT implementation strategy with the aim of improving communication and increasing productivity in the CI. However, IT is relatively new to the industry and so are the problems associated with it especially for the small subcontractors. The aims of this paper are to examine the current usage of IT by Australian subcontractors, and to identify the potential problems for subcontractors in IT implementation. The results indicate that the uptake of email and the internet by subcontractors is at a low level and the awareness of IT training and education is very low. The indications are that Australian subcontractors are not yet ready for the implementation of even rudimentary IT technology.

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR).

Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models.

The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy.

The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability.

The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy.

This is the first study to apply the NNAR model to construction output forecasting research.

The studies on contractor prequalification focus more on the review of models and algorithms rather than review of the criteria for contractor prequalification. However, the basis of every prequalification model primarily relates to the measurement and judgement of prospective contractors based on a set of decision criteria. This paper aims to address the gap by reviewing academic papers on contractor prequalification criteria.

A desktop search was conducted under the “T/A/K (title/abstract/keyword)” field of the Scopus search engine. A total of 49 papers were initially identified; however, only peer reviewed journals were selected for the study; therefore, a sample of 36 was subsequently used. Further filtering was done in which 26 papers were found valid for further analysis as it was realized that, not all the identified papers presented empirical arguments about the issue of contractor pre-qualification criteria. The selected 26 papers were subjected to content analysis to identify the key contractor pre-qualification criteria.

A total of 41 criteria were identified which were subsequently classified into six main categories, namely, technical considerations, management considerations, financial considerations, reputation considerations, general experience considerations and health, safety and environmental considerations. There was an indication that, the involvement of health, safety and environmental considerations in contractor prequalification proceedings is limited.

The major limitation of this research was the limited number of papers selected for further analysis based on the Scopus search engine. The identified criteria serve as a basis for further empirical studies on contractor prequalification criteria.

The outcome of this study broadens the understanding of practitioners and researchers on the various criteria for contractor prequalification.

By critically reviewing available literature on contractor prequalification, the study sets the tone for further empirical studies on contractor prequalification.