Duration estimation model for bridge construction projects in Ghana

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications The study has developed a working tool for practitioners at the agency to forecast contract duration for bridge projects prior to its commencement. Originality value 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.

Conceptual estimation of construction duration and cost of public highway projects

“…Several studies have attempted to model the relationship between highway projects features and their durations in early stages (Nevett et al, 2021, Titirla and Aretoulis 2019, Son et al, 2019, Okere, 2019, Nani et al, 2017, Pesko et al, 2017. Pesko et al, (2017) concluded that integrating the estimation of duration and cost can compromise the significance of input data, leading to reduction of estimating accuracy compared to that based on separate estimation.…”

“…However, no recent study is comprehensive and combines project characteristics (Sharma et al, 2021, Meharie and Shaik, 2020, Son et al, 2019, project scope (Qiao et al, 2019, Okere, 2019, Juszczyk, 2020, cost and schedule information (Sharma et al, 2021, Titirla andAretoulis, 2019), level of technical complexity (Karaca et al, 2020, Gardner et al, 2017 and expected risk profile for different project types in one robust general model with acceptable accuracy. Some studies even use item quantities for duration and/or cost prediction in the conceptual phase when they may not be precisely determined (Nevett et al 2021;Pesko et al 2017;Nani et al 2017). Besides, some studies rely solely on datasets that comprise factors whose probability and/or impact are determined subjectively by experts in questionnaires (Le et al, 2021).…”

“…For example, they have been used for the conceptual estimation of highway projects cost especially when limited information is available (Meharie and Shaik, 2020, Tijanic et al, 2019, Karaca et al, 2020, Aretoulis, 2019, Cao et al, 2018, Gardner et al, 2017, Chou et al, 2015. Additionally, they have been applied to estimate the duration of highway projects in their early stages, though not as widely as cost (Titirla and Aretoulis, 2019, Nani et al, 2017, Pesko et al, 2017, Mensah et al, 2016. ANN has gained popularity in several disciplines owing to its superiority in comparison with other AI techniques for several reasons (Sharma et al, 2021).…”

Predicting initial duration of project using linear and nonlinear regression models

“…The Google scholar search engine was used to check the universal database about the issues. [23] used multi factors that could be adopted to forecast road duration at the early stage of the project. This study adopted the available factors after preparing the bill of quantity by the estimator and pricing the whole road activities.…”

Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model

“…Moreover, the accuracy of the decision-making relies heavily on the outcomes of a reliable bridge condition forecasting model ( [2,15]; Lu and Zheng 2017). Many recent researches focus on improving model forecasting accuracy and shed some light on improving forecasting accuracy [2,4,[12][13][14][15]. However, many of the researches ignore the forecasting reliability and provide incomplete picture regarding forecasting accuracy.…”