Marinelli, M D L F N L S (2015) Non-parametric bill-of-quantities estimation of concrete road bridge superstructure: An artificial neural networks approach. In: Raiden, A and Aboagye-Nimo, E (eds.) Proceedings of 31st Annual ARCOM Conference, 7-9 September 2015, Lincoln, UK.
Abstract
Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.
| Item Type: | Conference Paper (Paper) |
|---|---|
| Uncontrolled Keywords: | artificial neural networks; bill of quantities; concrete bridge; cost |
| Index terms: | construction industry, Greece, budget overrun, bills of quantities, artificial neural network, transportation infrastructure, urban area, accuracy, reinforcing steel, efficiency, environmentally friendly, planning process, concrete bridge, estimate, estimation, bridge construction, cost estimating, dataset, design stage, implementation, construction project, regression model, planner |
| Subjects: | financial and cost management, modelling and simulation, sustainable design, infrastructure engineering, contractual arrangements, profession, building materials, performance management, professional development, infrastructure and transport systems, project controls, professional practice, data management, industry analysis, Geography, statistical analysis, urban form and morphology, production management |
| Topics: | Construction Materials, Research Practice, Design Practice, Project Management, Quality Management, Urban Studies, Sustainability, Procurement, Engineering Principles, Information Management, Geographical Context, Cost Management, Time Control, Roles and Professions, Digital Applications |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here