Bart Rogiers
Katholieke Universiteit Leuven
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Publication
Featured researches published by Bart Rogiers.
Mathematical Geosciences | 2012
Bart Rogiers; Dirk Mallants; Okke Batelaan; Matej Gedeon; Marijke Huysmans; Alain Dassargues
Various approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods—multiple linear regression and artificial neural networks—that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling.Artificial neural networks (ANNs) are combined with a generalised likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from the literature demonstrates the importance of site-specific calibration. The data set used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size Ks-pairs. Finally, an application with the optimised models is presented for a borehole lacking Ks data.
Applied Radiation and Isotopes | 2013
Sven Boden; Bart Rogiers; Diederik Jacques
Decommissioning of nuclear building structures usually leads to large amounts of low level radioactive waste. Using a reliable method to determine the contamination depth is indispensable prior to the start of decontamination works and also for minimizing the radioactive waste volume and the total workload. The method described in this paper is based on geostatistical modeling of in situ gamma-ray spectroscopy measurements using the multiple photo peak method. The method has been tested on the floor of the waste gas surge tank room within the BR3 (Belgian Reactor 3) decommissioning project and has delivered adequate results.
PLOS ONE | 2017
Bart Rogiers; Dirk Mallants; Okke Batelaan; Matej Gedeon; Marijke Huysmans; Alain Dassargues
Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.
Volume 2: Facility Decontamination and Decommissioning; Environmental Remediation; Environmental Management/Public Involvement/Crosscutting Issues/Global Partnering | 2013
Bart Rogiers; Sven Boden; Diederik Jacques
Reliable methods to determine the contamination depth in nuclear building structures are very much needed for minimizing the radioactive waste volume and the decontamination workload. This paper investigates the geostatistical integration of in situ gamma-ray spectroscopy measurements of different spatial supports. A case study is presented from the BR3 decommissioning project, yielding an estimated reduction of waste volume of ∼35%, and recommendations are made for future application of the proposed methodology.Copyright
Water Resources Research | 2013
Eric Laloy; Bart Rogiers; Jasper A. Vrugt; Dirk Mallants; Diederik Jacques
Applied Clay Science | 2013
Li Yu; Bart Rogiers; Matej Gedeon; Jan Marivoet; Mieke De Craen; Dirk Mallants
Hydrology and Earth System Sciences | 2013
Bart Rogiers; Koen Beerten; Tom Smeekens; Dirk Mallants; Matej Gedeon; Marijke Huysmans; Okke Batelaan; Alain Dassargues
Geologica Belgica | 2013
Bart Rogiers; Koen Beerten; Tuur Smeekens; Dirk Mallants; Matej Gedeon; Marijke Huysmans; Okke Batelaan; Alain Dassargues
Environmental Earth Sciences | 2014
Bart Rogiers; Thomas Vienken; Matej Gedeon; Okke Batelaan; Dirk Mallants; Marijke Huysmans; Alain Dassargues
Water Resources Research | 2013
Eric Laloy; Bart Rogiers; Jasper A. Vrugt; Dirk Mallants; Diederik Jacques
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