Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

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.

Do spectral gamma ray data really reflect humid-arid palaeoclimates? A test from the Palaeogene Interbasaltic weathered horizons at the Giant’s Causeway, N.Ireland

Since 1999, the rapid, inexpensive and non-destructive use of Th/K and Th/U ratios from spectral gamma ray measurements have been used as a proxy for changes in palaeo-hinterland weathering. This model is tested here by analysis of in situ palaeoweathering horizons where clay mineral contents are well-known. A residual palaeoweathered horizon of Palaeogene laterite (developed on basalt) has been logged at 14 locations across N. Ireland using spectral gamma ray detectors. The results are compared to published elemental and mineralogical data. While the model of K and U loss during the early stages of weathering to smectite and kaolinite is supported, the formation (during progressively more advanced weathering) of gibbsite and iron oxides has reversed the predicted pattern and caused U and Th retention in the weathering profile. The severity (duration, humidity) of weathering and palaeoweathering may be estimated using Th/K ratios as a proxy. The use of Th/U ratios is more problematic should detrital gibbsite (or similar clays) or iron oxides be detected. Mineralogical analysis is needed in order to evaluate the hosts for K, U and Th: nonetheless, the spectral gamma ray machine offers a real-time, inexpensive and effective tool for the preliminary or conjunctive assessment of degrees of weathering or palaeoweathering.