INTRODUCTION

In the AH the traditional analysis, conservation, preservation, management, rehabilitation, exploitation and communication process is complex, driven from multidimensional data and approaches, fragmented, high-cost and still limited to major Monuments.

These activities are then based on a continuous collaboration between architects, historians, engineers, researchers, managers and specialists, who work together for solutions to a complex process that includes the entire lifecycle: knowledge, use, communication and management. This implies the need for a platform to promote a real collaborative work between all parties involved. Finally, the process of conservation and restoration requires an increasing degree of automation.

This situation leads to having only one database and one Information System for all of the different phases of the lifecycle and not for different and detached subsystems. Overall, the model deals with global knowledge about AH, which could be shared and made available at any time, in any place, to any user: researchers, professional operators, students, and city-users.

Unfortunately these requirements and this methodological model are today simply a dream. Currently we see a total lack of accessibility to the entire corpus of information that should be shared by the specialists and the breakdown of the process into discontinuous isolated parts.

The main reason of this deficit lies not only in the large amount of heterogeneous data (3D models, images, photos, drawings, written documents, etc.) required by the process, which prevents the immediate usability and an easy transfer of information, but also in the complexity and partiality of the systems developed to provide an answer to these problems.

A first key step to overcome these lacks and deficiencies is to recognize, as a central moment of the entire building lifecycle, the conservation and maintenance stages, whose design plans are substantially the active parts of the process, in which shape, appearance, functionality and efficiency of the building are determined, being therefore the most important features of interventions.

A further improvement towards a better AH process management is to exploit the BIM intrinsic capabilities well bounded by William J. Mitchell “Building Information Modeling (BIM) databases ... is opening up new ways to think about designing and producing buildings and - as we are beginning to see - new formal and functional possibilities.” (Mitchell, 2009).

The use of BIM software in the AH lifecycle field, as reported by recent researches (Apollonio, Gaiani & Sun, 2012; Dore & Murphy., 2014; Oreni et al., 2014; Barazzetti et al., 2015), has many advantages such as semantic object-oriented modeling which allows for the classification of heritage objects, automatic lists of objects and material and automated conservation documents. However BIM techniques present in the AH field some limitations which prevented an effective use until today. It is easy to notice that one of the current limitations of BIM in the AH field is the lack of parametric library objects within BIM software that could be used for historical buildings or heritage sites. In addition, BIM as a tool of new design generally are not capable of modeling non-ideal state such as deviation, damage and deterioration, which are of prime concern when documenting AH and, more in general, the integration of low-level captured geometry with 3D parametric BIM objects is so hard. Also the activation of direct manufacturing processes for all objects BIM in the AH field is practically impossible.

We think, however, that a more accurate analysis of failing factors is needed today in order to go further in the use of BIM in the AH and to embed the BIM in the AH lifecycle process. We propose an in-depth approach based on the following issues:

  • BIM methodology & AH
  • Problems related to the 3D capture techniques focusing on image-based modeling
  • Knowledge-based modeling in BIM platforms
  • Structuring acquired information for BIM processes.

Background

BIM methodology & AH

BIM enables accurate object-oriented parametric modeling, and inherently incorporates semantic data pertaining to structural, material and additional information (Eastman,Eastman, Teicholz & Sacks, 2011). Apart from collection of geometry, BIM allows temporal representation showing various construction phases (Fai,Graham, Duckworth, Wood & Attar, 2011). Associative modeling among internal documents improves the efficiency of as-built documentation by real time model adaption and automatic clash detection. BIM can enhance information exchange for improved maintenance among stakeholders in life cycle (Motawa & Almarshad, 2013). All these characteristics make BIM an ideal platform for documenting and sharing the information of AH, but the as-built BIM is still in infancy (Hichri,Stefani, De Luca & Véron, 2013).

The problem is first of all methodological. BIM processes are established for workflows as-planned instead of as-built (Eastman, Eastman, Teicholz & Sacks, 2011; Volk, Stengel & Schultmann, 2014). Although the developed data acquisition techniques have been linked to the as-built BIM workflow by commercial software, the gap between raw survey data and intelligent BIM objects is still to be bridged. The current limits of BIM in AH exist in the following aspects:

Semantic object Vs unsegmented mass

The semantic structure of BIM allows 3D models to be built, enriched and exchanged in object level (Eastman, Eastman, Teicholz & Sacks, 2011). The data acquisition techniques, however, generate huge amount of unsegmented data. Although various algorithms of object recognition have been addressed, little or none of them are applied to AEC industry especially to AH yet (Tang,Huber, Akinci, Lipman & Lytle, 2010; Hichri,Stefani, De Luca & Véron, 2013). It leads to a series of problems ranging from tedious and labor-intensive manual modeling to inaccurate documentation.

Standardization Vs Irregularity

BIM is a highly standard platform in the light of shape of component and the way they are organized. Therefore, it retains limited ability to represent the irregularity of AH caused by active factors (handcraft ornament and order variation from the same base) and passive factors (deviation, deformation, damage and weathering).

Parametric intelligence Vs Geometric accuracy

A typical BIM object has ambiguous geometry but explicit rules involving internal constraints and external adaption which represent the construction logic. As-built model of AH, however, usually contains millions of points, each represented by precise values without any relationships beyond geometry. The key point is how to transform the as-built model to parametric model without oversimplifying its geometry.

3D capture techniques focusing on image based modeling

A key step to establish an AH BIM is the shape and color data acquisition of the artifact. Basically, there are two approaches to the problem: active sensors (like terrestrial laser scanner (TLS) or structured light projectors) and image-based reconstruction.

Active optical sensors (Blais, 2004; Vosselman & Maas, 2010) provide directly 3D range data and can capture relatively accurate geometric details, although still costly, usually bulky, not easy to use, requiring stable platform and affected by surface properties. They can acquire millions of points, even on perfectly flat surfaces, often resulting in over-sampling, but it is likely that corners and edges are not well captured. These sensors have also limited flexibility, since a range sensor is intended for a specific range/volume and generally lack of good texture information. Different technologies are used to overcome this problem: Time of Flight (ToF) used for longer range with accuracy in the single point measurement ~6 to 10 mm; Phase-based used for shorter ranges (~1 to 50 m) with an accuracy in the single point measurement ~0.5 to 5 mm; triangulation-based used for short range (~0.1 to 1 m) and high accuracy (~0.05 to 2 mm).

The range-based modeling pipeline (Callieri,Dellepiane, Cignoni & Scopigno, 2011) is nowadays quite straightforward but there are some drawbacks: TLS are not part of the standard documentation procedure in archaeology and serve only a very special purpose (Barber & Mills, 2007). Besides that, technically trained personnel are needed to operate the device and data acquisition can be tiresome and time consuming. Scanning above or below these ranges should be avoided so as to prevent inaccurate data capture. Some laser scanning equipment can have problems with reflectance from certain materials, such as marble or gilded surfaces. But, most importantly, they are still expensive to be used widely and problems generally arise in case of huge data sets and complex objects. Lastly, because these range sensors have been developed from an industry-oriented perspective, only a few are really useful for 3D AH applications (Blais & Beraldin, 2006). They do not provide unlimited geometric accuracy and completeness over objects and landscapes of all sizes at a low cost. Laser scanners are not as versatile as cameras with regard to capturing data, as they require time to scan the object, whereas a camera can capture a scene almost instantaneously. Moreover, they require line of sight to the object being recorded, meaning that it cannot see through objects (including dense vegetation), and it cannot see around corners. Scanning systems have minimum and maximum ranges over that they operate.

Image-based methods (Remondino & El‐Hakim, 2006), circumvent these drawbacks, allowing surveys at different levels and in all possible combinations of object complexities, with high quality outputs, easy usage and manipulation of the final products, few time restrictions, good flexibility and low cost (Bryan,Blake & Bedford, 2013). 3D modeling from images provides sparse or dense point clouds, according to the employed measurement methodology (manual or automated), project requirements and aims. For simple structures (e.g. buildings) interactive approaches are satisfactory, but for complex and detailed surfaces need automated measurement approaches. Recent developments in automated and dense image matching (Furukawa & Ponce, 2010; Hirschmuller, 2008; Remondino,El-Hakim, Gruen & Zhang, 2008; Hiep,Keriven, Labatut & Pons, 2009), allows getting dense and well-calibrated point clouds semi-automatically from images.

According to (Remondino, 2011), the choice of the 3D data capture approach depends on the required accuracy, object dimensions, location constraints, the instrument’s portability and usability, surface characteristics, the working team experience, the project budget and the final goal of the survey.

To author an accurate and realistic 3D model the way previously mentioned, single capturing techniques are not able to give satisfactory results in all situations (i.e. high geometric accuracy, portability, automation, photo-realism, low costs, flexibility, efficiency). Image and range data could be combined to fully exploit the intrinsic potentialities of each approach (De Luca,Véron & Florenzano, 2006; Guarnieri,Remondino & Vettore, 2006; Stamos et al., 2008; Gašparovic & Malarić, 2012). However this is a complex solution not fitting the conservation needs but only the render of a very accurate representation of the current state of the building. Using a BIM 3D model as final output, the most part of the data will be lost during the geometric conversion, and the result rarely is satisfactory.

Similarly to the mature and long-established 3D capture pipeline (Bernardini & Rushmeier, 2002), automated photogrammetric techniques, emerged in the last years from the collaboration between Computer Vision and Photogrammetric communities, are able to output shape and color, but at considerably lower costs, using a nearly standardized workflow: a) images acquisition, b) feature detection, c) feature matching, d) sparse 3D reconstruction, e) dense 3D reconstruction, f) coordinate transformation, g) mesh generation (Snavely,Seitz & Szeliski, 2008; Frahm et al., 2010; Agarwal et al., 2011).

Main drawback in the image-based methods is in that images contain all the useful information to derive 3D geometry and texture at low cost, but require a mathematical formulation (perspective or projective geometry) to transform 2D image observation into 3D information. Furthermore, the recovering of a complete, detailed, accurate and realistic 3D textured model from images is still a difficult task, in particular for large and complex sites and if uncalibrated or widely separated images are used.

A second problem is that these approaches were developed for consumer cameras and cannot provide resolution as high as and above 10 Mp, which modern cameras can readily provide. This limited resolution, coupled with Bayer pattern sensor demosaicking from a single matrix of red, green, and blue pixels, can severely limit the color accuracy. Performances sometimes are unclear and often low reliability and repeatability (Remondino,Del Pizzo, Kersten & Troisi, 2012). Moreover, a deep and metric evaluation of the different (hidden) steps is still missing.

Other unsolved issues, as always in the photogrammetric pipeline, involve:

  • Efficiency of photogrammetric processing algorithms that can drop out for limited image quality, or certain surface materials to be acquired, resulting in noisy point clouds or difficulties in feature extraction;
  • Known distance or Ground Control Points (GCP), required in order to derive metric 3D results;
  • Variations that arise from the use of various cameras by different working groups, which can further affect many photo-consistency-based reconstruction algorithms (Zhao,Zhou & Wu, 2012);
  • Color capture, management and rendering (i.e., OpenGL graphics).

The use of 3D acquired data inside a BIM workflow introduces further problems, mainly linked to the lack of point clouds proper editing commands, making BIM production a highly manual, time-consuming process. Filling the gap between unstructured acquired data and semantic objects in BIM is a challenging task. Automatic methods for structural and semantic analysis of point clouds are essential. Although robust methods have been proposed, the application in AEC industry is still to be developed (Tang,Huber, Akinci, Lipman & Lytle, 2010; Hichri,Stefani, De Luca & Véron, 2013).

Knowledge-based modeling in BIM platform

In the light of information organization, BIM is an ideal platform for representation and management of AH. Inherently BIM is semantically structured as a system of information. The information comes from prior knowledge and reality-based data acquisition. The prior knowledge could be extracted from Architectural Treatises that are served as a knowledge system. Before the advent of digital media, Treatises are one of the most important approaches for architectural knowledge broadcasting. The organization of BIM is highly analogous to the nature of architectural Treatises in terms of semantic structure and parametric relationships.

Classical architecture has always depended on precedents and therefore on Treatises. Vitruvius himself indebted to ancient authors, and the later architects and theorists such as Alberti, Palladio and Claude Perrault to a great extent depended on Vitruvius. Instead of merely imitating the contents of Architecture Treatises using digital 3D model, recent research tends to focus on extracting the shape grammar of classical architecture developed by (Stiny & Mitchell, 1978; Mitchell, 1990). Parametric modeling and knowledge extraction from architectural Treatises have been proposed in (De Luca,Véron & Florenzano, 2007; Murphy,McGovern & Pavia, 2011). They aims at constructing parametric library of architectural component and integrate it with hybrid data acquisition techniques.

BIM is an ideal platform for knowledge-based modeling not only for its semantic structure, but also for its potential to integrate as-built information. Among the many features of BIM software, object-based modeling is a huge gain when used for as-built BIM in terms of semantic organization and data segmentation. Instead of the traditional CAD pipeline, in which operators begin to work from scratch or they use drawing templates, BIM users define at first a family or a class (Eastman,Eastman, Teicholz & Sacks, 2011). The family or class is essentially the category for architectural component (window, column, beam, etc.), which retains geometry, relationships and structural information corresponding to its semantic logic. E.g. Autodesk Revit 2015 represents architectural objects with three types of family varying in potentials of as-built modeling:

  • system families, hard-coded into the software parametric engine, and slightly editable by users;
  • loadable families, authored starting from templates and can be used in several different projects;
  • in-place families, allowing user to customize but used primarily in single projects.

System family retains the most intelligence but limited possibility to integrate with captured data. System families consist of components such as wall, floor, and ceiling whose distinctions are generally quantitative (e.g. thickness, length, width), and therefore should be defined numerically rather than geometrically. Loadable families are the most commonly used family in Revit. They are created from templates where semantic behaviors have been pre-defined. Loadable families provide a more flexible modeling environment than system family, so the distinctive components such as Order (column), Entablature (beam), window and door could be made in loadable families with parametric intelligence for potential adaption and variation generation. Extracted features from captured data could be integrated to loadable families by nesting a cross section on which a 3D shape is built by extrusion (Entablature), revolve (Order) or sweep (window and door). In-place family does not retain any pre-defined constraints, for it is created as instance drawing like in traditional CAD. The drawing is not compulsory to obey the construction logic of any category. Hence it is in in-place family that irregular objects could be made (e.g. an inclined wall) with the loss of parametric intelligence and semantic information. A totally different approach in Revit is the "mass modeling" family authoring, in which many editing commands are available.

Summarizing it’s possible to state that today BIM solutions are rich in potential but many problems emerge for an effective use.

Structuring acquired information for BIM

Structuring acquired information is a critical step towards as-built BIM. The unstructured point clouds from laser scanning or photogrammetry cannot be directly handled or recognized by current BIM software.

Categorizing the acquired data into semantic objects and extracting the topological data on object level is mandatory. The need for semantically rich 3D model has long been existed in the field of architectural heritage (De Luca,Véron & Florenzano, 2007; Apollonio,Gaiani & Corsi, 2010) for improved information organization and representation. BIM inherently structures the 3D model semantically, hence it is on object level that acquired information should be extracted, enriched and converted to as-built BIM.