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The purpose of this paper is to characterize the relationships between air pollutant emissions from heavy duty diesel equipment and highway construction project scope, schedule, and budget. Objectives included estimating total project emissions; developing a daily emissions profile; and developing new emissions estimating metrics based on project scope, schedule, and budget.

The research approach involved collecting real-world data related to project scope, schedule, and budget from two highway case study projects. The data were used to establish an emissions inventory estimating methodology to calculate total emissions for each case study. The total emissions were normalized based on project size, duration, and cost in order to develop new emissions estimating metrics.

The results proved that it is possible to characterize total equipment emissions based on project size, duration, and cost. The new emissions estimating metrics were quantitatively similar for both case studies.

The results were based on two case study projects. Additional data from more projects is needed to provide more highly refined numerical results.

This approach enables project planners and managers to assess the environmental impacts of highway projects along with the financial and time impacts.

Construction equipment is a major contributor to the nation’s air pollution problem. Before pollutant emissions can be managed they must first be measured.

The new emissions estimating metrics are a novel approach to comparing environmental impacts of two or more projects, as well as estimating total emissions for future highway construction projects.

This paper aims to focus on developing a baseline model for time overrun.

Information on 321 completed construction projects used to assess the predictive performance of two statistical techniques, namely, multiple regression and the Bayesian approach.

The eventual results from the Bayesian Markov chain Monte Carlo model were observed to improve the predictive ability of the model compared with multiple linear regression. Besides the unique nuances peculiar with projects executed, the scope factors initial duration, gross floor area and number of storeys have been observed to be stable predictors of time overrun.

This current model contributes to improving the reliability of predicting time overruns.

The building industry, through its structure and its mandate, faces endemic information problems; expert systems are expected to impact positively. Expert systems are suited to situations of uncertainty; knowledge and reasoning are separated, allowing easier updating. Knowledge acquisition from human experts is difficult and problems of information reliability arise, suggesting the scope for cooperation between knowledge engineers and documentalists familiar with the domain. In building, prevailing conditions seem to indicate the appropriateness of expert systems, particularly during the design phase; however, written documentation and general research results are rarely consulted. This highlights the need for an information ‘refining’ stage between production and use. It is easier to set up expert systems for specialised sub‐domains; however, on‐going research is attempting to develop a comprehensive approach to project‐specific information that would be operational from initial design through to completed construction. Criteria for a comprehensive design information system can be listed.

A major concern for construction professionals at the rural road agency in Ghana is the problem of fixing contract duration for bridge construction projects in rural areas. The purpose of the study was to develop a tool for construction professionals to forecast duration for bridge projects.

In all, 100 questionnaires were distributed to professionals at the Department of Feeder Roads to ascertain their views on the work items in a bill of quantities (BOQ) that impact significantly on the duration of bridge construction projects. Historical data for 30 completed bridge projects were also collected from the same Department. The data collected were executed work items in BOQ and actual durations used in completing the works. The qualitative data were analysed using the relative importance index and the quantitative data, processed and analysed using both the stepwise regression method and artificial neural network (ANN) technique.

The identified predictors, namely, in-situ concrete, weight of prefabricated steel components, gravel sub-base and haulage of aggregates, used as independent variables resulted in the development of a regression model with a mean absolute percentage error (MAPE) of 25 per cent and an ANN model with a feed forward back propagation algorithm with an MAPE of 26 per cent at the validation stage. The study has shown that both regression and ANN models are appropriate for predicting the duration of a new bridge construction project.

The predictors used in the developed models are limited to work items in BOQs only of completed bridge construction projects as well as the small sample size.

The study has developed a working tool for practitioners at the agency to forecast contract duration for bridge projects prior to its commencement.

The study has quantified the relationship between the work items in BOQs and the duration of bridge construction projects using the stepwise regression method and the ANN techniques.