Integration of surface and subsurface data for civil engineering (keynote)

Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-60750-617-1-37 37 Integration of surface and subsurface data for civil engineering Robert HACKa Engineering Geology, University of Twente, International Institute for GeoInformation Science and Earth Observation (ITC),Enschede, The Netherlands a Abstract. Digital geoinformation for the surface and subsurface is virtually never integrated in civil engineering projects. Reasons are that the surface information is gathered by different person’s and that the subsurface information is in different formats from the surface information. Likelihood and uncertainty of subsurface models is not quantifiable. To quantify the likelihood and correctness of the subsurface information the civil engineer would have to have full access to the original data, which is not available due to the none integration of the data. In the last 10 years considerable progress has been made in use of geographic information systems but the progress in the integration of data and the addressing of likelihood of subsurface data is limited. Keywords. Subsurface, surface, geotechnical, engineering, geology, digital data Introduction Many years geographic information systems are available for use in geotechnical engineering and engineering geology; at first two-dimensional and now also threedimensional systems. However still geoinformation systems to model the subsurface are used sparsely in civil engineering projects and then mainly in large projects only. On the other hand CAD/CAM systems are already for year’s standard practice in civil engineering. What are the reasons for this seemingly strange discrepancy? The author considers the lack of integrated systems for handling surface and subsurface data and the lack of a proper handling of uncertainties in the subsurface as major bottlenecks for a full integration of subsurface modeling in civil engineering. 1. Digital versus traditional handling of data for geotechnical engineering and engineering geology The first introduction of the use of digital data and the work with digital data to make geological and geotechnical models of the underground is quite some time ago. At the introduction the general feeling was that these tools would largely facilitate the work of an engineering geologist and improve the results of engineering geological and geotechnical modeling. However, digital interpretation and the use of digital modeling techniques did not yet make a breakthrough in engineering geology or geotechnical engineering. The use is fairly limited and, if used, often confined to only visualize the results of the modeling. The benefits of a good presentation and visualization of data Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. 38 R. Hack / Integration of Surface and Subsurface Data for Civil Engineering and underground models should not be underestimated, but is only one of the aspects which were expected to be beneficial at the introduction of digital data and computers. The reasons may be many, but a careful inspection of the way of working with geological and geotechnical data in engineering geology reveals a major flaw in the approach towards data in engineering geology. In the old times the traditional handmade geological model, interpretations and interpolations camouflaged this flaw. Before the digital times nobody in engineering geology had much interest in the accuracy of data interpolation and interpretation. Data was interpreted to the best knowledge of the engineering geologist or geotechnical engineer taking into consideration the geological environment and the available data (often a limited quantity). It was clear that the interpretation could not be quantified and nobody asked for accuracy. Strange enough, accuracy of field or laboratory measurements has always been regarded as important. Also nobody had much interest in the accuracy of geological maps. The consequence for the average site investigation in engineering geology was that the likelihood of the geological and geotechnical models of the underground to be correct was largely unknown. In the digital times data interpolation and interpretation are regarded as of major importance, and, consequently, the accuracy of geological maps and geological models become major topics. One way to solve the accuracy problem of the geological and geotechnical model is to increase the quantity of data to such a level that the no expertise of a geologist is required (just interpolation). The consequence is an increase in work and thus costs. The higher costs could be justified if it would lead to better results. This seems, however, not to be the case. Site investigations seem not to become a lot better if made with interpolation only, even not with large quantities of data. If no large quantities of data are used, but a geological interpretation, a disastrous and unexpected site effect is often observed. Everybody, including the clients, in general civil engineers, seems to find it necessary to ask questions about the accuracy of the model. It seems that because a computer is used the accuracy of the model has to be known. In most cases (according to the author: in virtual all cases) the answer can only be: "it looks good, but for the accuracy: no idea". This confirms then the existing ideas about geo-fantasy (and geologists in general). Hence, digital modeling in engineering geology leads then to more work, more costs, or, we show that geological models cannot be justified mathematically, and have to admit that the model largely depends on expertise only. Is 3D modeling totally useless in engineering geology? It is the opinion of the author that 3D modeling techniques can only find a place in engineering geology if they are considerably better than the traditional hand interpretations. Then it would result in better site investigations and would lead to cost reduction for the total project. As shown above the weakest point in engineering geology and geotechnical engineering is the geological model. The geological model is made based on the expert opinion of mostly one single geologist (or engineering geologist or geotechnical engineer). There is no mathematical justification for the model and the accuracy of the model is unknown. An improvement would be if it would be possible to use during the making of the geological model the expertise of more experts; i.e. if more than one geologist could be involved in making a model. Obviously for the average site investigation this would be far too expensive. An alternative may be the use of expert systems and knowledge bases. The knowledge base may include tools that facilitate the interpretation, but especially, should include geological standard models that in a particular geological environment can be fitted to a given set of data. If such a database could be developed by a large team of experts the database would get the status of a Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. R. Hack / Integration of Surface and Subsurface Data for Civil Engineering 39 reference standard. Apart from reducing the influence of a single geologist, it would also (at least partly) rebut the criticism of non-geologists on accuracy of the model (the geo-fantasy) because the geological model can be referenced to the standard model in the standard database. 2. Two- versus three-dimensional modeling In many projects a full tree-dimensional system is used for the engineering part of the project, for example, Bentley [1] and Autocad [2], while for the surface or subsurface geology and geotechnical details a two-dimensional or so-called 2.5-dimensional geographic information system is used. A Two-Dimensional Geographic Information System (2D-GIS) uses a two-dimensional database. Therefore, it cannot be used for the construction of an underground model, which by definition is three-dimensional. In some systems it is possible to model one of the properties as third (mostly the depth or “z”) coordinate. This sort of systems is often called two and a half dimensional system (2.5D). A 2.5D-GIS also has a two-dimensional database, with the exception that it allows pseudo-3D viewing of surfaces. The space between the surfaces, however, is not described in the database. Hence, this space is by definition considered homogeneous, an assumption seldom true. The use of the z-coordinate as property limits also the use of the GIS to relative simple geological situations as it is not possible to have the same boundary (e.g. multiple z-coordinate values) at the same x, y coordinate. 3D-GIS use a fully three-dimensional database, in which every point in space is described. Therefore, the use of 3D-GIS has an essential added value over 2- or 2.5D-GIS. 3. Accuracy and uncertainty Accuracy of geological and geotechnical data can thus not be quantified but worse also uncertainty of models cannot be quantified. This implies that to be able to judge the likelihood that a model is “good” all data has to be available that is used to make the model. Hence this not only means the data as measured from boreholes, penetration tests or samples has to be available but also all data that has been used to make the model. 4. Example of a full three-dimensional subsurface model An Intelligent Decision Support System (IDSS) for soft soil shield tunneling is presented as example [3, 4]. The IDSS development involves a integration of 3D modeling, visualization, and artificial intelligence technology for decision-making in tunneling projects. Modeling and geotechnical characterization of soil volumes is critical for the choice of the type of Tunnel Boring Machine (TBM) and to forecast the TBM performance. The modeling of the subsurface was done interactively based on available geological knowledge of the geological environment and boreholes and Cone Penetration Test (CPT). The area is located in the Netherlands near Rotterdam. The subsurface consists of lenses and interbedding layers of sand, clay and peat in a deltaic Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. 40 R. Hack / Integration of Surface and Subsurface Data for Civil Engineering environment. The 2nd Heinenoord Tunnel was built between 1996 and 1999. It was the first tunnel in the Netherlands to be made by a TBM. The location of the tunnel site is shown in figure 1. The data flow in the project is shown in figure 2. Figure 1. Map showing the location of the Heinenoord Tunnel along the Oude Maas river in the southwestern part of the Netherlands [4]. Various types of data sources were available: geological maps from the Dutch Geological Survey, borehole data, cone penetration tests (CPT), interpreted geotechnical profiles based on CPTs, and seismic data. To be able to make a correct three-dimensional model the various data can all be visualized simultaneously on screen and at its correct position in three-dimensions (figure 3). The vertical scale in seismic data depends on the velocity of the various layers through which the seismic ray pass, hence the interpretation of seismic data depends on the lithology. Regrettable most 3D-GIS systems for use in mining or geotechnical work are not suited for a varying vertical scale. From the interpreted data sources, three volumetric (litho-) stratigraphic models were built (small, medium, and large scale). The various scales were chosen to identify the influence of the amount of data available for the model (small scale: large amount of data; large scale: small amount of data). Figure 4 shows lithostratigraphic models at various levels of detail. Property distributions were made by statistical distributions based on CPT-logs (e.g. cone resistance, sleeve friction, friction ratio, water pressure) and sample values. Figure 5 shows horizontal (XY-plane) and vertical (Z-direction) variograms of the cone resistance which was recorded by CPTs. The variogram indicates the amount of correlation that exists between pairs of measured cone resistances in space. The correlation (vertical axis) diminishes with distance (horizontal axis); 0 means perfect correlation (Figure 5). 5. Finite element (FE) modeling The volumetric model was transferred into a finite element modeling package. Typical soil strength values were assigned to the various layers. Stresses on the tunnel, Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. R. Hack / Integration of Surface and Subsurface Data for Civil Engineering Figure 2. Flow chart showing research methodology [4]. 41 Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. 42 R. Hack / Integration of Surface and Subsurface Data for Civil Engineering Figure 3. Interactive visualization and modeling process [4]. induced by the (fictitious) construction of a building on the surface above the tunnel, were then calculated. The vertical displacement caused by the loading is shown in Figure 6. The model results were then exported back into the 3D-GIS. These kinds of calculations help to identify potential areas of high risk due to surface loading. Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. R. Hack / Integration of Surface and Subsurface Data for Civil Engineering 43 Figure 4. 3D models of litho-stratigraphy at various levels of detail. The incision in the middle of model is the Oude Maas river (after [4]). 6. Results of the example study The project was done already some 10 years ago and therefore not every conclusion from the project holds for the geotechnical modeling today. However some conclusions from that time still apply today: 1) Format for the data sources varies widely. Conversions programs may be available to convert one format into another. Problem with these conversion programs is that it is often very difficult to check whether the conversion is correct and complete. In the experience of the author often some of the original information is not converted and this may go unnoted without a careful check. 2) Uncertainty or likelihood of the correctness of the model cannot be established. 3) Transfer from data from the GIS to for example three—dimensional finite element programs is very complicated and cumbersome, and sometimes simply impossible or only possible with major simplifications. For the return; e.g. transfer of the results to the GIS program, applies the same. Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. 44 R. Hack / Integration of Surface and Subsurface Data for Civil Engineering Figure 5. Top: 3D grid model with simulated cone resistances. High cone resistances are indicated in orange, red and white (gravel and sand), low ones in grey and blue (peat; clay and silt). Five CPT con resistance logs are shown in black. The cell dimensions are 25×25×1 meter and the model dimensions are 255×1250×55 meter.; bottom: horizontal and vertical variograms of cone resistance (after [4]). Figure 6. Vertical displacement caused by applying a fictitious load on the surface above the tunnel (after [4]). Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. R. Hack / Integration of Surface and Subsurface Data for Civil Engineering 45 4) Varying scales depending on the interpretation such as velocity scales in seimics or resistivity scales in resistivity surveys cannot be handled in GIS systems and can only be interpreted in specialized geophysical programs. This would be no problem if the geophysical programs would allow for a proper integration of the boreholes and other geological and geotechnical data. Regrettable this is mostly not the case. 7. Present day situation 7.1. Visualization Visualization of subsurface models has improved considerable. Even to the extent that the subsurface model can be projected onto the real world while walking or moving through an area. A pair of goggles allows for the projection of subsurface details onto the visual real world on surface (figure 7). Three-dimensional visualization in the office is still a bit cumbersome, but possible even with little costs. The gaming industry and adventure movies (for example, the movie “Avatar”) filmed and to be viewed in threedimensions have caused an enormous boost in the development of cheap threedimensional visualization tools for home or office use. Figure 7. User with a tracked handheld client (right) sees the real world augmented with a semantic 3D model of underground infrastructure (left) (after [5]) 7.2. Various formats Formats of digital data are still varying however some major steps have been made to improve this. Various projects in the world are on steam to set formats for digital data handling such as the Joint Technical Committee 2 (JCT2) of the Federation of Geotechnical Societies [6, 7], and many other groups in the world [8, 9, 10, 11]. Another important development is the use of various internet standards such as XML for the definition of the GeoScience Markup Language (GeoSciML) [12], and the Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. 46 R. Hack / Integration of Surface and Subsurface Data for Civil Engineering meta-standard for geotechnical data, the Geotechnical Markup Language (GeotechXML) [13] with a subset for slopes (SlopeSML) [14]. 8. Integration of surface and subsurface data Integration of surface and subsurface data is in the first steps of being integrated. The Geo Building Information Modelling (GeoBIM) is a subset of CityGML [15], which among others integrates surface and subsurface infrastructure data (figure 8) [16]. Figure 8. Building pit excavation in an area previously covered with buildings. Small infrastructure, geology and boreholes are indicated (after: [16]) However, GeoBim is not a complete set of integrated formats to handle surface and subsurface data. The requirement of integration of subsurface and surface data for use in civil engineering is already formulated in the 90’s of the last century [17, 18]. More recent articles [19, 20] have emphasised the point, but progress has been limited. Integration of surface and subsurface data not only is easy when planning or designing surface or subsurface structures, but is necessary to make risk assessment more transparent. Risk assessment, and thus assessment of the likelihood of the correctness of the subsurface model, for civil engineering structures becomes more and more important and required, accelerated by a series of disasters with underground excavations [21, 22, 23]. Another project working on the integration of surface and subsurface data is by the Delft and Twente Universities in The Netherlands [24, 25, 26]. A complete model for (semantic) model for data handling has been made and various tools to convert data from various formats (Figure 9). Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. R. Hack / Integration of Surface and Subsurface Data for Civil Engineering 47 Figure 9. UML diagram indicating the required thematic semantic information for the description of subsurface geological and geotechnical objects, to be collected by means of site investigation, field measurements and laboratory tests of geological objects (after [27]). Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. 48 R. Hack / Integration of Surface and Subsurface Data for Civil Engineering Recently uncertainty quantification has partially been solved by Clarke [28] with the “generic confidence evaluation scheme”. For each data point, a confidence value (in the range from 1 to 10) is assigned. The boundary that is defined with this data can then be attributed a confidence value based on the interpolation of the confidence values of the data points. Figure 10 gives an example. Figure 10. a) Grid displaying confidence data in the Thames Gateway region between Woolwich and Gravesend for the boundary of the Chalk Group. Color variation from blue to red indicates a change from low to high confidence in figure b. The red rectangle in a) gives the approximate location (free after [29]). 9. Conclusions In recent years large progress has been made on three-dimensional Geographic information programs. The handling and operation of the programs is far friendlier and the visualization is many times better and cheaper than 10 years ago. However, the comparison between a project done 10 years ago and the practice today shows that relatively little progress has been made in integration of surface and subsurface data. Exchange of data may still lead to unwanted effects and data may still be lost or not transferred completely. The problem of quantifying of uncertainty of subsurface models is even less solved. First attempts to qualify or quantify data uncertainty are on its way but are not yet applicable in every geological environment. References [1] Bentley. http://www.bentley.com/nl-NL/ . [2] AutoCad/Autodesk, 2010. http://usa.autodesk.com/ . [3] S. Ozmutlu, H.R.G.K. Hack, C. de Rooij, R. van der. Putten, 3D Modelling aspects of soft ground for tunnelling with TBM. Proc. 3D Modelling of Natural Objects: A Challenge for the 2000’s. 4-5 June. Nancy, France (1998) 10. [4] J.G. Veldkamp, H.R.G.K. Hack, S. Ozmutlu, M.A.N. Hendriks & R. Kronieger & Van Deen J.K., Combination of 3D-GIS and FEM modelling of the 2nd Heinenoord Tunnel, the Netherlands Proc. Int. Symp. Engineering geological problems of urban areas, EngGeolCity-2001, (2001), Ekaterinburg, Russia (article) [5] G. Schall, & D. Schmalstieg, Interactive Urban Models generated from Context-Preserving Transcoding of Real-Wold Data. Proc. 5th Int. Conf. on GIScience (GISCIENCE 2008), abstracts volume, Park City, Utah, USA (2008), 23-26. [6] D.G. Toll, Geo-Engineering Data: Representation and Standardisation, Electronic Journal of Geotechnical Engineering, (2007), http://www.ejge.com/2007/Ppr0699/Ppr0699.htm. [7] JTC2 Joint Technical Committee number 2 of the Federation of Geotechnical Societies. http://www.dur.ac.uk/geo-engineering/jtc2, (2010). [8] AGS AGS data format. Association of Geotechnical and Geoenvironmental Specialists (AGS), UK, (2009), http://www.ags.org.uk/site/datatransfer/intro.cfm . Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. R. Hack / Integration of Surface and Subsurface Data for Civil Engineering 49 [9] Y.S. Chang, & H.D. Park, Development of a web-based Geographic Information System for the management of borehole and geological data. Computers & Geosciences 30 (8)(2004), 887-897. [10] Y.Choi, S.Y. Yoon, & H.D. Park, Tunneling Analyst: A 3D GIS extension for rock mass classification and fault zone analysis in tunnelling. Computers & Geosciences. 35 (6) (2009)ˈ 1322-1333 [11] GEF Geotechnical Exchange Format. http://www.geffiles.nl/, (2007) [12] GeoSciML,(https://www.seegrid.csiro.au/twiki/bin/viewfile/CGIModel/Geologic Feature?rev=2;filename=GeoSciML_mapped_feature_10_2007.gif ) 2010 [13] Geotech-XML, 2010 [14] SlopeSML, SlopeSML (Slope Stability Mark-up Language) http://www.ins.itu.edu.tr/bulent/slopesml/2010. [15] CityGML,: CityGML – Exchange and Storage of Virtual 3D City Models, http://www.citygml.org/1522/, (accessed February, 5th 2010). [16] F. Zobl, & R. Marschallinger, Subsurface GeoBuilding Information Modelling GeoBIM, GEOinformatics, 11(8), December 2008, 40-43. [17] P.G. Fookes, Geology for Engineers: the Geological Model, Prediction and Performance. The First Glossop Lecture. Quarterly Journal of Engineering Geology and Hydrogeology. 30 (4)( 1997), 293-424 [18] H. R. G. K. Hack, Digital data for engineering geology: disaster or benefit? European Science Foundation Conference: Virtual environments for the Geosciences: Space–time modelling of bounded natural domains. Rolduc, The Netherlands. World Wide Web Address: http://www.xs4all.nl/~hack/WORKHack/esf/esf_1997/abstracts [19] R. Hack, B. Orlic, S. Ozmutlu, S. Zhu, & N. Rengers, Three and more dimensional modelling in geoengineering. Bulletin of Engineering Geology and the Environment 65(2)( 2006), 143-153. [20] W. Yanbing, W. Lixin, S. Wenzhong, & L. Xiaojuan, 3D Integral Modeling for City Surface & Subsurface. In: Innovations in 3D Geo Information Systems. (2006), 95-105. [21] W. S. Atkins, The risk to third parties from bored tunnelling in soft ground. Research report 453. Health and Safety Executive., Sudbury, UK.(2006),78. [22] G. A. Fenton, & D. V. Griffiths, Risk Assessment in Geotechnical Engineering. Publ. Wiley. ISBN-13: 978-0470178201 480 (2008). [23] M. V. Staveren, Uncertainty and ground conditions : a risk management approach. ISBN: 978-0-75066958-0, Elsevier, Boston, MA. (2006), 332. [24] W. Tegtmeier, Harmonization of geo-information related to the lifecycle of civil engineering objects – including uncertainty and quality of surveyed data and derived real world representations (in preparation). (2010) [25] W. Tegtmeier, R. Hack, S. Zlatanova, and Van Oosterom, P.J.M., The problem of uncertainty integration and geo-information harmonization. In: Coors, V., Rumor, M., Fendel, E. & Zlatanova, S. (eds.), Urban and regional data management (2008),171-184. Taylor&Francis. [26] W. Tegtmeier, Van Oosterom, P.J.M., S. Zlatanova, and H.R.G.K. Hack, Information management in civil engineering infrastructural development : with focus on geological and geotechnical information. In: Proceedings of the ISPRS workshop Vol. XXXVIII-3-4/C3 Comm. III/4, IV/8 and IV/5 : academic track of GeoWeb 2009 conference : Cityscapes, Vancouver Canada, (2009), 27-31. [27] W. Tegtmeier, H.R.G.K. Hack, S. Zlatanova, and Van Oosterom P.J.M. An integrated 3D model including (sub-) surface real world and design information – supporting information management in infrastructural development. (in press). [28] S.M. Clarke, Confidence in geological interpretation. A methodology for evaluating uncertainty in common two and three-dimensional representations of subsurface geology. British Geological Survey Internal Report, IR/04/164. (2004), 29. [29] K.R. Royse, H.K. Rutter, and D.C. Entwisle, Property attribution of 3D geological models in the Thames Gateway, London: new ways of visualising geoscientific information. Bull. of Engineering Geology and the Environment. Publ. Springer Berlin / Heidelberg. ISSN: 1435-9529. Vol 68, (1) / February, (2009), 1-16. Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. Information Technology in Geo-Engineering Proceedings of the 1st International Conference (ICITG) Shanghai Edited by David G. Toll Durham University, UK Hehua Zhu Tongji University, China and Xiaojun Li Tongji University, China Amsterdam • Berlin • Tokyo • Washington, DC Hack, R., 2010. Integration of surface and subsurface data for civil engineering. In: Toll, D.G., Zhu, H., Li, X. (Eds), Information Technology in Geo-Engineering. IOS Press, Amsterdam, The Netherlands. ISBN: 978-1-60750-616-4. DOI: 10.3233/978-1-60750-617-1-37. pp. 37-49. © 2010 The authors and IOS Press. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without prior written permission from the publisher. ISBN 978-1-60750-616-4 (print) ISBN 978-1-60750-617-1 (online) Library of Congress Control Number: 2010933984 Published by IOS Press under the imprint Millpress. 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