International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

BIM obstacles in industrial projects : a contractor perspective Ali, Mostafa; Mohamed, Yasser; Taghaddos, Hosein; Hermann, Rick Jun 30, 2015

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5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   BIM OBSTACLES IN INDUSTRIAL PROJECTS: A CONTRACTOR PERSPECTIVE Mostafa Ali1,3, Yasser Mohamed1, Hosein Taghaddos2 and Rick Hermann2 1 University of Alberta, Canada 2 PCL, Canada 3 MostafaAli@ualberta.ca Abstract: Using BIM technology is well established in construction projects, especially in industrial projects where a maze of pipes and modules have to be installed in congested work areas under tight time schedule . BIM offers potential benefits (e.g. visualization, collaboration, alignment …etc.) that can be key for complex project success; however, these benefits have not been fully implemented in industrial projects. The authors worked last two years closely with a construction company -which specialized in oil and gas projects- trying to discover ways to maximize utilization of knowledge embedded in BIM models. Though the partner company has used BIM for long time, we noticed many obstacles that hinder reaping the full benefits of BIM for construction planning and control. These obstacles are related to the ability to extend a model by adding new attributes and to link the model to data from external sources (e.g. cost or schedule control information systems). This paper discusses these obstacles, illustrates implemented short-term solutions to work around and mitigate these obstacles, and finally concludes by proposing using semantic web as a long-term solution to overcome these obstacles and clear the path for gaining all potential benefits during industrial projects construction. 1 INTRODUCTION Industrial projects are one of the first projects to use BIM technology; this is largely due to project complexity and size, as these two factors determine the degree of information technology usage in the project (Thomas, Macken, & Lee, 2001). However, the degree of usage varies based on company policy and employee expertise. (Jung & Joo, 2011) categorized BIM usage into passive and active utilization, passive usage encompass engineering analyses like safety and scheduling; while active usage works on extracting embedded knowledge in BIM. BIM builds a virtual model that can be investigated and tested before constructing the real project. It offers many benefits for construction projects like saving cost and time (Cooperative Research Center for Construction Innovation , 2007), (Azhar, 2011), and (Gilligan & Kunz, 2007). However, not all benefits are fully gained as some benefits are more popular than others; for example, clash detection and space utilization are more common than automating shop drawings for fabrication (Gilligan & Kunz, 2007). Although BIM has potential benefits, there are some unsolved issues. These issues can be grouped into contractual (e.g. model ownership) and technical level (e.g. interoperability) (Azhar, 2011).This paper represents a case study regarding BIM utilization in industrial projects. It tracks developing the model in engineering firms and shows the consequences of their decisions on the contractors. 026-1 The paper starts by a brief introduction about technologies used or proposed to enhance BIM utilization, then, it discusses current practice and issues found in the partner company’s current practice. Afterwards it presents short-term solutions that have been developed to mitigate these issues. Finally, we conclude by our vision for using semantic web technology as a long-term solution for these issues. 2 CURRENT PRACTICE AND ISSUES Industrial project model usually consists of multiple sub-models (e.g. structural, mechanical, electrical model, etc.), each model is designed separately and then all models are compiled by engineering firm into one model to review and detect any clashes. After that, the engineering firm issues the model for the contractor as one model. Figure 1 shows IDEF0 diagram for this process. Engineering firms that work with the partner company use two proprietary software applications (NavisWorks® and SmartPlant®). Based on their preferences, the engineering firm will determine which software they will use and the contractor has to comply with this choice; this adds overhead cost on the contractor including purchasing the two software and training its employees.  Figure 1: IDEF0 for issuing BIM model for the contractor.  During our work with the model, we observed the following issues: 1. BIM ownership (contractual level): The contractor receives only the compiled model for reviewing and visualization. However, the contractor cannot add to the model and, therefore, any operational attributes have to be saved in a separate database, 2. Lack of standards (contractual level): The same item might be labelled “I beam”, “I-Beam”, or “I Beam column” based on the engineering firm convention. In addition, some items do not have essential attributes (e.g. item type), 3. Model limitation (technical level): The contractor cannot calculate quantity take-off accurately as only boundary volume of the item is available, and 026-2 4. Interoperability (technical level): It is a tedious task to transfer data between different systems or software. This task requires using intermediate format and it is very prone to error and missing data. 3 SHORT-TERM SOLUTION In order to overcome the previously stated issues, a complete change in the process has to be done. However, this is not always feasible and it will take time to accomplish. Hence, we proposed some short-term solution to work around these issues temporarily while working on a longer-term solution. While developing this solution, we take into consideration two points: 1) We will try not to change current practice. In other words, changing the contractual relationship between contractor, owner, and engineering firm to facilitate more effective exchange of BIM details is outside the scope of this work; and 2) our solution should clarify limitations of the current practice to proof the need for long-term solution. This section will describe two of our quick solutions that have been implemented in the company. 3.1 Filter by discipline As we stated earlier, the partner company receives multiple BIM models from different engineering designers compiled as one model (Figure 2). With the lack of standards and conventions between the designers, the company found it hard to filter model items based on trade (e.g. extract all concrete piles, or all pipes). A coordinator has to visually inspect model items and add them to different display set based on their type. This is a daunting work that takes days to complete, and it becomes more problematic in case of fast tracking projects where new models come sometimes every week.  Figure 2: This BIM model lacks essential attributes to filter by trade. This model contains more than 700,000 model items. By investigating four models for four different projects that has been done in the last ten years, we found that although there is no explicit attribute that indicates the item trade, well-defined rules can be used to extract trade’s items. For example, in one of the projects we found that almost 30% of the pipes have an attribute called “Source file” that ends with “dgn”, different rules can be found for the rest of the pipes. Therefore, we decided to define a “Filter” structure that contains attributes for one rule. Figure 3 shows an example of a filter that captures the previously stated rule. It contains seven attributes as follows: 026-3 1. Criteria ID: to intersect multiple filters as shown later; 2. Condition ID: because filters are stored in SQL database, it was required as a part of the primary key; 3. Category: the trade name to apply the rule; 4. Category display name: this points to the tab name in Navisworks (Figure 3), although this attribute is not essential, we preferred to use it as it reduces the process time significantly; 5. Property display name: the property name as in Figure 3; 6. Condition: either equals, not equals, contains, not contains; and 7. Property value: the value we are looking for as in Figure 3.  Figure 3: An example of a rule. Table 1 shows the set of rules used to extract all items for four trades (namely; concrete, pipe, steel, and piles). It took three trials to get all items by refining the rules to capture these items. These rules are stored in SQL server database and a .NET plug-in reads the rules and applies them on the 3D model. As shown in Table 1, some trades require one rule while others require more than one. This clearly will depend on the project. However, we designed our tool to compile multiple rules using intersection and union to accommodate any complex filtering case. The general formula for intersection and union is:  Trade = (Filter 1 AND Filter 2 AND …..Filter N) OR (Filter N+1 AND Filter N+2 AND …Filter M) OR … For example: Concrete = (color is green and source file contains “dgn”) Or (Color is blue and Material is “C50”) If more than one filter have the same criteria ID then they will be combined using intersection (AND operator) to form a filter block, then filter blocks will be combined together using OR operator. For example, there are four rules for pipes in Table 1, the first two have a criteria Id of “2” while the other two rules have a criteria Id of “3”. This means the result of the first two filters will be intersected together to form the first block then the results of the other two filters will intersected together to form the second block then the two blocks will be merged using union operation as in Figure 4. Using the plug-in to apply these rules finds all items in the four trades, the plug-in will label each item with its trade so it can be retrieved later; in addition, a display set is created for each trade as in Figure 5. 3.2 Work areas With complex project that covers large area and consists of millions of components, it is always a good practice to partition it to small work areas. The partner company partitions their project according to two criteria: “Work packages” and “work areas”. Work package represents a set of items that have to be installed together and costs 500 to 1000 man-hours (i.e. one rotation), while work area represents a set of 026-4 items that reside inside an imaginary 3D box. These two breakdown criteria are independent of each other, so one pipe might span multiple work area (because of its length) while it belongs to one work package. Because of model ownership issues and software limitation, work area boundary boxes cannot be directly applied on the model. Therefore, we developed a plug-in using .NET technology that reads boundary boxes coordinates from database (Figure 6) and create a display set that contains all items inside any boundary box. This enables the user to isolate one or more work areas easily as in Figure 7. Moreover, these display sets can be accessed by people on site using handheld devices to check a virtual prototype of their required task. Table 1: Set of rules required to capture four trades in the project. Criteria Id Condition Id Category Category display name Property display name Condition Value 1 1 Concrete PDS Material Contains Concrete 2 1 Pipe Item Material Equals Colour 66 2 2 Pipe Item Source File Contains Dgn 3 1 Pipe Item Material Equals Colour 115 3 2 Pipe Item Source File Contains Dgn 4 1 Steel PDS Material Contains Steel 4 2 Steel Item Material Equals Colour 166 5 1 Pile PDS Material Contains Steel 5 2 Pile PDS Material Equals Colour 161 6 1 Tray PDS Ol_type Contains Tray   Figure 4: Applying filters to find all pipes in the model. First each rule is applied individually, then they are combined using AND operator to form Filer blocks that grouped using OR operator to find items by trade.  026-5  Figure 5: A display set will be created for each trade and each item will be labeled.  Figure 6: Work Area boundary box is defined in a separate database.  Figure 7: The plug-in will create display set for each work area, so it can be filtered easily. 4 LONG-TERM SOLUTION 4.1 Overview The aforementioned solution overcomes the obstacles but it shows many limitations that can be overcome only by changing the current process. These limitations can be stated as following: • Manual inspection of the model to find filtering rules; 026-6 • Rules can filter steel items, but it cannot identify their classes; • Need for customized configuration for each project (impossible to automate); and • Any non-graphical data (e.g. work area boundary box and scaffold log) have to be stored in a separate database, which requires management and sometimes causes discrepancies.  A more effective solution will require a complete change in the process to streamline the flow of data from engineering firms to construction and allow linking multiple data sources flawlessly. The first step, in our opinion, is using layered system approach. Layered system separates data layer from the application, which will eliminate interoperability issue and any software limitation while providing one source for all kind of data. The structure of the data layer should be chosen carefully as it has a direct impact on all aspects of the system. Here, we suggest using semantic web technology; many scholars (e.g. Jung and Joo 2011) have mentioned its potentials in BIM future to facilitate data exchange and information integration. Our long-term solution (Figure 8) consists of two main steps: creating the data layer and using it to automate some tasks (e.g. schedule generation). The first step will require developing ontology for the data layer, which stores all attributes related to BIM model items, and another component will use shape recognition techniques to recognize unlabelled items and 3D legacy CAD models. After defining the data layer, many applications can be developed to automate different task. We selected three applications (partial models, schedule generation, and scaffold discovery) that we thought would be most useful for a contractor. The following two sections give a brief introduction to shape recognition and semantic web technology as they apply to the proposed long-term solution. 4.2 Shape recognition Since the mid-1970s, Computer Aided Design (CAD) starts to replace traditional paper drawing as it provides more quality, facilitates editing, increases productivity. However, CAD systems lacks object oriented concept, which limits the ability to share data between different systems (Anumba, et al., 2008). However, the advent of BIM quickly supersedes CAD systems as BIM seams to address CAD limitations. Yet, there are huge legacy projects, which use CAD models. On the other hand, the proliferation of 3D objects required new methods to search and query these objects (3D objects retrieval) as traditional text search is insufficient (Funkhouser, et al., 2003). There are many researches regarding 3D object retrievals (see (Cardone, et al., 2003), (Iyer, et al., 2005) and (Tangelder & Veltkamp, 2008)) that have been used to many application like cost estimation in engineering mechanics by comparing the current model with previously done models (Cardone, et al., 2003). The basic idea behind 3D objects retrieval is finding the shape signature (also called descriptor and shape representation in some references) and compare it the previously stored signatures database. The similarity between two 3D objects is measured by the distance between the two signatures (zero means the two objects are identical). 4.3 Semantic web technology During the project life cycle, different parties (e.g. architectures, engineers, contractors…etc.) generate a sheer volume of documents and drawings (Rujirayanyong & Shi, 2006). These documents used to be stored in binders; each binder –which represents a project or a cycle - was stored in lockers. Accessing data from these binders requires a huge amount of time and effort to the extent that companies have to hire people just to archive and organize these binders. With the advent of PCs, binders have been replaced by folders that contains electronic version of all documents and drawings (scanned or CAD drawings). This means faster information retrieval in a cheaper way. However, as governed by old expertise, PCs have been used to store files without paying 026-7 much attention to describe their contents. That means user will have multiple files that should be explored manually to find the required information. (Berners-Lee, et al., 2001) suggest extending the structure of World Wide Web to a computer-readable format that called semantic web. Semantic web will use schema (called ontology) to define the data stored in machines. This will enable machines to interpret the data instead of just storing them. Using this powerful structure, machines can link multiple data sources to generate information automatically. Semantic web utilizes two profound technologies: eXtensible Markup Language (XML) and the Resources Description Framework (RDF) (Berners-Lee, et al., 2001). XML is very similar to HTML language (that is used for current World Wide Web); however, it gives the user the ability to define his own tags and structured them in any sophisticated way to capture complex data model that cannot be contained in traditional database. Though its flexibility, it is hard for anyone to build on or extract data from existing XML file other than the creator due to its arbitrary structure. On the other hand, RDF specification has been developed by World Wide Web Consortium (W3C); it uses triples (subject, predicate, and object) to express data. RDF provides a standard way to exchange data (Segaran, et al., 2009). Incorporating these two technologies will offer data model flexibility and yet a standard way to represent data; however, it requires another component that can identify similar concepts from different data sources. For example, how the machine can know that the “mouse” in animal context is similar to “mouse” in mammal context while it is different from “mouse” in hardware context or from the programming language “mouse”, here where ontology comes to play as it uses Uniform Resource Identifier (URI) for different concepts. Therefore, ontolgies are an essential tool for merging data from different sources (Deshpande & Kumbhar, 2011), and semantic web success depends on ontology proliferation (Maedche & Staab, 2001). In construction domain, semantic web applications can be grouped into files’ management and knowledge extraction. As an example of files’ management, (Jiayi & Anumba, 2008) utilized semantic web technology to discover relations between construction project files. In addition, (Wang, et al., 2011) developed ontology to manage construction files contents. Regarding knowledge extraction, researchers developed ontologies to extract information from 3D models: For estimation purposes, (Lee, et al., 2014) developed an ontology to evaluate multiple work items for tiles. Another ontology is developed by (Zhang & Issa, 2013) to extract partial models from IFC format. However, most of these applications have been verified by simplified case studies rather than real ones. 4.4 Long-term solution summary The following list summarizes the main points of the proposed solution and states success criteria for each point: 1. Create ontology to enable error-free exchange of data that should be able to merge data from different sources (e.g. combine two 3D parametric models). Success criteria: Using Quality value (Fröbel, et al., 2011).  2. Recognize 3D shapes and convert it to the proposed ontology. There are many techniques to calculate shape signature to recognize 3D items; these techniques vary on their efficiencies and computational power based on the type of 3D object (e.g. 3D solid or 3D surface) and the degree of intricate details. We propose using shape distribution technique (Osada, et al. 2002) because of its robustness and insensitivity to small details (i.e. It will not be largely affected if the item missing some parts). This step will be applied on legacy CAD projects and unidentified items in BIM models. Success criteria: a ratio of correctly recognized objects over total number of objects. 3. Generate partial 3D models based on the proposed ontology, After constructing and populating our ontology , it can be queried using the semantic query language (SPARQL). It will be used to filter 026-8 the model items by trade, work package, work area …etc. to generate partial models. These models will be very helpful for tradesmen who need a 3D model for only their trades within a specific work area. Success criteria: Expert opinion. 4. Automate entities mapping to a schedule by mapping every work package (“work package is a detailed plan that contains 500: 1000 hours to be executed during one rotation” (Ryan 2009)) to an activity and create relations based on spatial attributes and traditional sequences between trades. Success criteria: Expert opinion. 5. Scaffold discovery based on reported progress, the system will discover required scaffold ahead of time so the user can remove this constraint before actually starting the required task. The system should estimate erection man-hours based on historical data. This will be an example of automation in execution phase. Success criteria: Expert opinion and scaffold estimation models.  Figure 8: Long-term solution framework: shape recognition algorithms will be used to classify items. These data will be stored on ontology data format that can be queried using SPARQL to generate partial models. It also can facilitate automating schedule generation and scaffold discovery. 5 CONCLUSION This paper discussed a case study related to the use of BIM by a partner company on industrial projects. We identified a number of obstacles regarding implementing BIM technology. In order to overcome these obstacles, a solution that relies on multiple filtering rules is proposed as a short-term solution that can mitigate these obstacles, but has limitations and cannot be generalized. Therefore, we propose a different approach that relies on separation of the data layer from the model and the use semantic web technology to facilitate sharing data between different users and extraction/integration from different information sources. This approach can automate different tasks like generating partial models for trades, schedule generation, and scaffold discovery. 026-9 References Anumba, C. J., J. Pan, R.R.A. Issa, and I. Mutis. "Collaborative project information management in a semantic web environment." Engineering, Construction and Architectural Management 15, no. 1 (2008): 78 - 94. Azhar, Salman. "Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry." Leadership and Management in Engineering, 2011: 241-252. Berners-Lee, Tim, James Hendler, and Ora Lassila. "The Semantic Web." Scientific American, May 2001: 29-37. Cardone, Antonio, Satyandra K. Gupta, and Mukul Karnik. "A survey of shape similarity assessment algorithms for product design and manufacturing applications." Journal of Computing and Information Science in Engineering 3, no. 2 (2003): 109-118. Cooperative Research Center for Construction Innovation . "Adopting BIM for Facilities Adopting BIM for Facilities Management : Solutions for Managing the Sydney Opera House." Brisbane, Qld, Australia, 2007. Deshpande, Neela J., and Rajendra Kumbhar. "Construction and Applications of Ontology: Recent Trends." DESIDOC Journal of Library & Information Technology 31, no. 2 (2011): 84-89. Fröbel, Toni, Berthold Firmenich, and Christian Koch. "Quality assessment of coupled civil engineering applications." Advanced Engineering Informatics 25, no. 4 (2011): 625-639. Funkhouser, Thomas, et al. "A search engine for 3D models." ACM Transactions on Graphics (TOG) 22, no. 1 (2003): 83-105. Gilligan, Brian, and John Kunz. VDC Use in 2007: Significant Value, Dramatic Growth, and Apparent Business Opportunity. CIFE Stanford University , 2007. Iyer, Natraj, Subramaniam Jayanti, Kuiyang Lou, Yagnanarayanan Kalyanaraman, and Karthik Ramani. "Three-dimensional shape searching: state-of-the-art review and future trends." Computer-Aided Design 37, no. 5 (2005): 509–530. Jiayi, PAN, and Chimay J. Anumba. "Semantic-Discovery of Construction Project Files." Tsinghua Science & Technology 13, no. S1 (2008): 305-310. Jung, Youngsoo, and Mihee Joo. "Building information modelling (BIM) framework for practical implementation." Automation in Construction, 2011: 126-133. Lee, Seul-Ki, Ka-Ram Kim, and Jung-Ho Yu. "BIM and ontology-based approach for building cost estimation." Automation in Construction, 2014: 96–105. Maedche, Alexander, and Steffen Staab. "Learning Ontologies for the Semantic Web." IEEE Intelligent Systems 16, no. 2 (2001): 72-79. Osada, Robert, Thomas Funkhouser, Bernard Chazelle, and David Dobkin. "Shape Distributions." ACM Transactions on Graphics, 2002: 807-832. Porter, M.,. "The Economic Performance of Regions." Regional Studies 37, no. 6-7 (2003): 545–546. Rujirayanyong, Thammasak, and Jonathan J. Shi. "A project-oriented data warehouse for construction." Automation in Construction, 2006: 800–807. Ryan, Geoff. Schedule for Sale: Workface Planning for Construction Projects. Authorhouse, 2009. Segaran, Toby, Colin Evans, and Jamie Taylor. Programming the semantic web. 1st. O'REILLY, 2009. Tangelder, Johan W. H., and Remco C. Veltkamp. "A survey of content based 3D shape retrieval methods." Multimedia Tools and Applications 39, no. 3 (2008): 441-471. Thomas, Stephen R., Candace L. Macken, and Sang-Hoon Lee. Impacts of design/information technology on building and industrial projects. Austin, TX: NIST, Construction Industry Institute, University of Texas, 2001. Wang, Han-Hsiang, Frank Boukamp, and Tarek Elghamrawy. "Ontology-Based Approach to Context Representation and Reasoning for Managing Context-Sensitive Construction Information." Journal of computing in civil engineering 25, no. 5 (2011): 331-346. Zhang, Le, and Raja R. A. Issa. "Ontology-Based Partial Building Information Model Extraction." Journal of Computing in Civil Engineering, 2013: 576–584.     026-10  5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   BIM OBSTACLES IN INDUSTRIAL PROJECTS: A CONTRACTOR PERSPECTIVE Mostafa Ali1,3, Yasser Mohamed1, Hosein Taghaddos2 and Rick Hermann2 1 University of Alberta, Canada 2 PCL, Canada 3 MostafaAli@ualberta.ca Abstract: Using BIM technology is well established in construction projects, especially in industrial projects where a maze of pipes and modules have to be installed in congested work areas under tight time schedule . BIM offers potential benefits (e.g. visualization, collaboration, alignment …etc.) that can be key for complex project success; however, these benefits have not been fully implemented in industrial projects. The authors worked last two years closely with a construction company -which specialized in oil and gas projects- trying to discover ways to maximize utilization of knowledge embedded in BIM models. Though the partner company has used BIM for long time, we noticed many obstacles that hinder reaping the full benefits of BIM for construction planning and control. These obstacles are related to the ability to extend a model by adding new attributes and to link the model to data from external sources (e.g. cost or schedule control information systems). This paper discusses these obstacles, illustrates implemented short-term solutions to work around and mitigate these obstacles, and finally concludes by proposing using semantic web as a long-term solution to overcome these obstacles and clear the path for gaining all potential benefits during industrial projects construction. 1 INTRODUCTION Industrial projects are one of the first projects to use BIM technology; this is largely due to project complexity and size, as these two factors determine the degree of information technology usage in the project (Thomas, Macken, & Lee, 2001). However, the degree of usage varies based on company policy and employee expertise. (Jung & Joo, 2011) categorized BIM usage into passive and active utilization, passive usage encompass engineering analyses like safety and scheduling; while active usage works on extracting embedded knowledge in BIM. BIM builds a virtual model that can be investigated and tested before constructing the real project. It offers many benefits for construction projects like saving cost and time (Cooperative Research Center for Construction Innovation , 2007), (Azhar, 2011), and (Gilligan & Kunz, 2007). However, not all benefits are fully gained as some benefits are more popular than others; for example, clash detection and space utilization are more common than automating shop drawings for fabrication (Gilligan & Kunz, 2007). Although BIM has potential benefits, there are some unsolved issues. These issues can be grouped into contractual (e.g. model ownership) and technical level (e.g. interoperability) (Azhar, 2011).This paper represents a case study regarding BIM utilization in industrial projects. It tracks developing the model in engineering firms and shows the consequences of their decisions on the contractors. 026-1 The paper starts by a brief introduction about technologies used or proposed to enhance BIM utilization, then, it discusses current practice and issues found in the partner company’s current practice. Afterwards it presents short-term solutions that have been developed to mitigate these issues. Finally, we conclude by our vision for using semantic web technology as a long-term solution for these issues. 2 CURRENT PRACTICE AND ISSUES Industrial project model usually consists of multiple sub-models (e.g. structural, mechanical, electrical model, etc.), each model is designed separately and then all models are compiled by engineering firm into one model to review and detect any clashes. After that, the engineering firm issues the model for the contractor as one model. Figure 1 shows IDEF0 diagram for this process. Engineering firms that work with the partner company use two proprietary software applications (NavisWorks® and SmartPlant®). Based on their preferences, the engineering firm will determine which software they will use and the contractor has to comply with this choice; this adds overhead cost on the contractor including purchasing the two software and training its employees.  Figure 1: IDEF0 for issuing BIM model for the contractor.  During our work with the model, we observed the following issues: 1. BIM ownership (contractual level): The contractor receives only the compiled model for reviewing and visualization. However, the contractor cannot add to the model and, therefore, any operational attributes have to be saved in a separate database, 2. Lack of standards (contractual level): The same item might be labelled “I beam”, “I-Beam”, or “I Beam column” based on the engineering firm convention. In addition, some items do not have essential attributes (e.g. item type), 3. Model limitation (technical level): The contractor cannot calculate quantity take-off accurately as only boundary volume of the item is available, and 026-2 4. Interoperability (technical level): It is a tedious task to transfer data between different systems or software. This task requires using intermediate format and it is very prone to error and missing data. 3 SHORT-TERM SOLUTION In order to overcome the previously stated issues, a complete change in the process has to be done. However, this is not always feasible and it will take time to accomplish. Hence, we proposed some short-term solution to work around these issues temporarily while working on a longer-term solution. While developing this solution, we take into consideration two points: 1) We will try not to change current practice. In other words, changing the contractual relationship between contractor, owner, and engineering firm to facilitate more effective exchange of BIM details is outside the scope of this work; and 2) our solution should clarify limitations of the current practice to proof the need for long-term solution. This section will describe two of our quick solutions that have been implemented in the company. 3.1 Filter by discipline As we stated earlier, the partner company receives multiple BIM models from different engineering designers compiled as one model (Figure 2). With the lack of standards and conventions between the designers, the company found it hard to filter model items based on trade (e.g. extract all concrete piles, or all pipes). A coordinator has to visually inspect model items and add them to different display set based on their type. This is a daunting work that takes days to complete, and it becomes more problematic in case of fast tracking projects where new models come sometimes every week.  Figure 2: This BIM model lacks essential attributes to filter by trade. This model contains more than 700,000 model items. By investigating four models for four different projects that has been done in the last ten years, we found that although there is no explicit attribute that indicates the item trade, well-defined rules can be used to extract trade’s items. For example, in one of the projects we found that almost 30% of the pipes have an attribute called “Source file” that ends with “dgn”, different rules can be found for the rest of the pipes. Therefore, we decided to define a “Filter” structure that contains attributes for one rule. Figure 3 shows an example of a filter that captures the previously stated rule. It contains seven attributes as follows: 026-3 1. Criteria ID: to intersect multiple filters as shown later; 2. Condition ID: because filters are stored in SQL database, it was required as a part of the primary key; 3. Category: the trade name to apply the rule; 4. Category display name: this points to the tab name in Navisworks (Figure 3), although this attribute is not essential, we preferred to use it as it reduces the process time significantly; 5. Property display name: the property name as in Figure 3; 6. Condition: either equals, not equals, contains, not contains; and 7. Property value: the value we are looking for as in Figure 3.  Figure 3: An example of a rule. Table 1 shows the set of rules used to extract all items for four trades (namely; concrete, pipe, steel, and piles). It took three trials to get all items by refining the rules to capture these items. These rules are stored in SQL server database and a .NET plug-in reads the rules and applies them on the 3D model. As shown in Table 1, some trades require one rule while others require more than one. This clearly will depend on the project. However, we designed our tool to compile multiple rules using intersection and union to accommodate any complex filtering case. The general formula for intersection and union is:  Trade = (Filter 1 AND Filter 2 AND …..Filter N) OR (Filter N+1 AND Filter N+2 AND …Filter M) OR … For example: Concrete = (color is green and source file contains “dgn”) Or (Color is blue and Material is “C50”) If more than one filter have the same criteria ID then they will be combined using intersection (AND operator) to form a filter block, then filter blocks will be combined together using OR operator. For example, there are four rules for pipes in Table 1, the first two have a criteria Id of “2” while the other two rules have a criteria Id of “3”. This means the result of the first two filters will be intersected together to form the first block then the results of the other two filters will intersected together to form the second block then the two blocks will be merged using union operation as in Figure 4. Using the plug-in to apply these rules finds all items in the four trades, the plug-in will label each item with its trade so it can be retrieved later; in addition, a display set is created for each trade as in Figure 5. 3.2 Work areas With complex project that covers large area and consists of millions of components, it is always a good practice to partition it to small work areas. The partner company partitions their project according to two criteria: “Work packages” and “work areas”. Work package represents a set of items that have to be installed together and costs 500 to 1000 man-hours (i.e. one rotation), while work area represents a set of 026-4 items that reside inside an imaginary 3D box. These two breakdown criteria are independent of each other, so one pipe might span multiple work area (because of its length) while it belongs to one work package. Because of model ownership issues and software limitation, work area boundary boxes cannot be directly applied on the model. Therefore, we developed a plug-in using .NET technology that reads boundary boxes coordinates from database (Figure 6) and create a display set that contains all items inside any boundary box. This enables the user to isolate one or more work areas easily as in Figure 7. Moreover, these display sets can be accessed by people on site using handheld devices to check a virtual prototype of their required task. Table 1: Set of rules required to capture four trades in the project. Criteria Id Condition Id Category Category display name Property display name Condition Value 1 1 Concrete PDS Material Contains Concrete 2 1 Pipe Item Material Equals Colour 66 2 2 Pipe Item Source File Contains Dgn 3 1 Pipe Item Material Equals Colour 115 3 2 Pipe Item Source File Contains Dgn 4 1 Steel PDS Material Contains Steel 4 2 Steel Item Material Equals Colour 166 5 1 Pile PDS Material Contains Steel 5 2 Pile PDS Material Equals Colour 161 6 1 Tray PDS Ol_type Contains Tray   Figure 4: Applying filters to find all pipes in the model. First each rule is applied individually, then they are combined using AND operator to form Filer blocks that grouped using OR operator to find items by trade.  026-5  Figure 5: A display set will be created for each trade and each item will be labeled.  Figure 6: Work Area boundary box is defined in a separate database.  Figure 7: The plug-in will create display set for each work area, so it can be filtered easily. 4 LONG-TERM SOLUTION 4.1 Overview The aforementioned solution overcomes the obstacles but it shows many limitations that can be overcome only by changing the current process. These limitations can be stated as following: • Manual inspection of the model to find filtering rules; 026-6 • Rules can filter steel items, but it cannot identify their classes; • Need for customized configuration for each project (impossible to automate); and • Any non-graphical data (e.g. work area boundary box and scaffold log) have to be stored in a separate database, which requires management and sometimes causes discrepancies.  A more effective solution will require a complete change in the process to streamline the flow of data from engineering firms to construction and allow linking multiple data sources flawlessly. The first step, in our opinion, is using layered system approach. Layered system separates data layer from the application, which will eliminate interoperability issue and any software limitation while providing one source for all kind of data. The structure of the data layer should be chosen carefully as it has a direct impact on all aspects of the system. Here, we suggest using semantic web technology; many scholars (e.g. Jung and Joo 2011) have mentioned its potentials in BIM future to facilitate data exchange and information integration. Our long-term solution (Figure 8) consists of two main steps: creating the data layer and using it to automate some tasks (e.g. schedule generation). The first step will require developing ontology for the data layer, which stores all attributes related to BIM model items, and another component will use shape recognition techniques to recognize unlabelled items and 3D legacy CAD models. After defining the data layer, many applications can be developed to automate different task. We selected three applications (partial models, schedule generation, and scaffold discovery) that we thought would be most useful for a contractor. The following two sections give a brief introduction to shape recognition and semantic web technology as they apply to the proposed long-term solution. 4.2 Shape recognition Since the mid-1970s, Computer Aided Design (CAD) starts to replace traditional paper drawing as it provides more quality, facilitates editing, increases productivity. However, CAD systems lacks object oriented concept, which limits the ability to share data between different systems (Anumba, et al., 2008). However, the advent of BIM quickly supersedes CAD systems as BIM seams to address CAD limitations. Yet, there are huge legacy projects, which use CAD models. On the other hand, the proliferation of 3D objects required new methods to search and query these objects (3D objects retrieval) as traditional text search is insufficient (Funkhouser, et al., 2003). There are many researches regarding 3D object retrievals (see (Cardone, et al., 2003), (Iyer, et al., 2005) and (Tangelder & Veltkamp, 2008)) that have been used to many application like cost estimation in engineering mechanics by comparing the current model with previously done models (Cardone, et al., 2003). The basic idea behind 3D objects retrieval is finding the shape signature (also called descriptor and shape representation in some references) and compare it the previously stored signatures database. The similarity between two 3D objects is measured by the distance between the two signatures (zero means the two objects are identical). 4.3 Semantic web technology During the project life cycle, different parties (e.g. architectures, engineers, contractors…etc.) generate a sheer volume of documents and drawings (Rujirayanyong & Shi, 2006). These documents used to be stored in binders; each binder –which represents a project or a cycle - was stored in lockers. Accessing data from these binders requires a huge amount of time and effort to the extent that companies have to hire people just to archive and organize these binders. With the advent of PCs, binders have been replaced by folders that contains electronic version of all documents and drawings (scanned or CAD drawings). This means faster information retrieval in a cheaper way. However, as governed by old expertise, PCs have been used to store files without paying 026-7 much attention to describe their contents. That means user will have multiple files that should be explored manually to find the required information. (Berners-Lee, et al., 2001) suggest extending the structure of World Wide Web to a computer-readable format that called semantic web. Semantic web will use schema (called ontology) to define the data stored in machines. This will enable machines to interpret the data instead of just storing them. Using this powerful structure, machines can link multiple data sources to generate information automatically. Semantic web utilizes two profound technologies: eXtensible Markup Language (XML) and the Resources Description Framework (RDF) (Berners-Lee, et al., 2001). XML is very similar to HTML language (that is used for current World Wide Web); however, it gives the user the ability to define his own tags and structured them in any sophisticated way to capture complex data model that cannot be contained in traditional database. Though its flexibility, it is hard for anyone to build on or extract data from existing XML file other than the creator due to its arbitrary structure. On the other hand, RDF specification has been developed by World Wide Web Consortium (W3C); it uses triples (subject, predicate, and object) to express data. RDF provides a standard way to exchange data (Segaran, et al., 2009). Incorporating these two technologies will offer data model flexibility and yet a standard way to represent data; however, it requires another component that can identify similar concepts from different data sources. For example, how the machine can know that the “mouse” in animal context is similar to “mouse” in mammal context while it is different from “mouse” in hardware context or from the programming language “mouse”, here where ontology comes to play as it uses Uniform Resource Identifier (URI) for different concepts. Therefore, ontolgies are an essential tool for merging data from different sources (Deshpande & Kumbhar, 2011), and semantic web success depends on ontology proliferation (Maedche & Staab, 2001). In construction domain, semantic web applications can be grouped into files’ management and knowledge extraction. As an example of files’ management, (Jiayi & Anumba, 2008) utilized semantic web technology to discover relations between construction project files. In addition, (Wang, et al., 2011) developed ontology to manage construction files contents. Regarding knowledge extraction, researchers developed ontologies to extract information from 3D models: For estimation purposes, (Lee, et al., 2014) developed an ontology to evaluate multiple work items for tiles. Another ontology is developed by (Zhang & Issa, 2013) to extract partial models from IFC format. However, most of these applications have been verified by simplified case studies rather than real ones. 4.4 Long-term solution summary The following list summarizes the main points of the proposed solution and states success criteria for each point: 1. Create ontology to enable error-free exchange of data that should be able to merge data from different sources (e.g. combine two 3D parametric models). Success criteria: Using Quality value (Fröbel, et al., 2011).  2. Recognize 3D shapes and convert it to the proposed ontology. There are many techniques to calculate shape signature to recognize 3D items; these techniques vary on their efficiencies and computational power based on the type of 3D object (e.g. 3D solid or 3D surface) and the degree of intricate details. We propose using shape distribution technique (Osada, et al. 2002) because of its robustness and insensitivity to small details (i.e. It will not be largely affected if the item missing some parts). This step will be applied on legacy CAD projects and unidentified items in BIM models. Success criteria: a ratio of correctly recognized objects over total number of objects. 3. Generate partial 3D models based on the proposed ontology, After constructing and populating our ontology , it can be queried using the semantic query language (SPARQL). It will be used to filter 026-8 the model items by trade, work package, work area …etc. to generate partial models. These models will be very helpful for tradesmen who need a 3D model for only their trades within a specific work area. Success criteria: Expert opinion. 4. Automate entities mapping to a schedule by mapping every work package (“work package is a detailed plan that contains 500: 1000 hours to be executed during one rotation” (Ryan 2009)) to an activity and create relations based on spatial attributes and traditional sequences between trades. Success criteria: Expert opinion. 5. Scaffold discovery based on reported progress, the system will discover required scaffold ahead of time so the user can remove this constraint before actually starting the required task. The system should estimate erection man-hours based on historical data. This will be an example of automation in execution phase. Success criteria: Expert opinion and scaffold estimation models.  Figure 8: Long-term solution framework: shape recognition algorithms will be used to classify items. These data will be stored on ontology data format that can be queried using SPARQL to generate partial models. It also can facilitate automating schedule generation and scaffold discovery. 5 CONCLUSION This paper discussed a case study related to the use of BIM by a partner company on industrial projects. We identified a number of obstacles regarding implementing BIM technology. In order to overcome these obstacles, a solution that relies on multiple filtering rules is proposed as a short-term solution that can mitigate these obstacles, but has limitations and cannot be generalized. Therefore, we propose a different approach that relies on separation of the data layer from the model and the use semantic web technology to facilitate sharing data between different users and extraction/integration from different information sources. This approach can automate different tasks like generating partial models for trades, schedule generation, and scaffold discovery. 026-9 References Anumba, C. J., J. Pan, R.R.A. Issa, and I. Mutis. "Collaborative project information management in a semantic web environment." Engineering, Construction and Architectural Management 15, no. 1 (2008): 78 - 94. Azhar, Salman. "Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry." Leadership and Management in Engineering, 2011: 241-252. Berners-Lee, Tim, James Hendler, and Ora Lassila. "The Semantic Web." Scientific American, May 2001: 29-37. Cardone, Antonio, Satyandra K. Gupta, and Mukul Karnik. "A survey of shape similarity assessment algorithms for product design and manufacturing applications." Journal of Computing and Information Science in Engineering 3, no. 2 (2003): 109-118. Cooperative Research Center for Construction Innovation . "Adopting BIM for Facilities Adopting BIM for Facilities Management : Solutions for Managing the Sydney Opera House." Brisbane, Qld, Australia, 2007. Deshpande, Neela J., and Rajendra Kumbhar. "Construction and Applications of Ontology: Recent Trends." DESIDOC Journal of Library & Information Technology 31, no. 2 (2011): 84-89. Fröbel, Toni, Berthold Firmenich, and Christian Koch. "Quality assessment of coupled civil engineering applications." Advanced Engineering Informatics 25, no. 4 (2011): 625-639. Funkhouser, Thomas, et al. "A search engine for 3D models." ACM Transactions on Graphics (TOG) 22, no. 1 (2003): 83-105. Gilligan, Brian, and John Kunz. VDC Use in 2007: Significant Value, Dramatic Growth, and Apparent Business Opportunity. CIFE Stanford University , 2007. Iyer, Natraj, Subramaniam Jayanti, Kuiyang Lou, Yagnanarayanan Kalyanaraman, and Karthik Ramani. "Three-dimensional shape searching: state-of-the-art review and future trends." Computer-Aided Design 37, no. 5 (2005): 509–530. Jiayi, PAN, and Chimay J. Anumba. "Semantic-Discovery of Construction Project Files." Tsinghua Science & Technology 13, no. S1 (2008): 305-310. Jung, Youngsoo, and Mihee Joo. "Building information modelling (BIM) framework for practical implementation." Automation in Construction, 2011: 126-133. Lee, Seul-Ki, Ka-Ram Kim, and Jung-Ho Yu. "BIM and ontology-based approach for building cost estimation." Automation in Construction, 2014: 96–105. Maedche, Alexander, and Steffen Staab. "Learning Ontologies for the Semantic Web." IEEE Intelligent Systems 16, no. 2 (2001): 72-79. Osada, Robert, Thomas Funkhouser, Bernard Chazelle, and David Dobkin. "Shape Distributions." ACM Transactions on Graphics, 2002: 807-832. Porter, M.,. "The Economic Performance of Regions." Regional Studies 37, no. 6-7 (2003): 545–546. Rujirayanyong, Thammasak, and Jonathan J. Shi. "A project-oriented data warehouse for construction." Automation in Construction, 2006: 800–807. Ryan, Geoff. Schedule for Sale: Workface Planning for Construction Projects. Authorhouse, 2009. Segaran, Toby, Colin Evans, and Jamie Taylor. Programming the semantic web. 1st. O'REILLY, 2009. Tangelder, Johan W. H., and Remco C. Veltkamp. "A survey of content based 3D shape retrieval methods." Multimedia Tools and Applications 39, no. 3 (2008): 441-471. Thomas, Stephen R., Candace L. Macken, and Sang-Hoon Lee. Impacts of design/information technology on building and industrial projects. Austin, TX: NIST, Construction Industry Institute, University of Texas, 2001. Wang, Han-Hsiang, Frank Boukamp, and Tarek Elghamrawy. "Ontology-Based Approach to Context Representation and Reasoning for Managing Context-Sensitive Construction Information." Journal of computing in civil engineering 25, no. 5 (2011): 331-346. Zhang, Le, and Raja R. A. Issa. "Ontology-Based Partial Building Information Model Extraction." Journal of Computing in Civil Engineering, 2013: 576–584.     026-10  BIM Obstacles in Industrial Projects: A Contractor PerspectiveMostafa AliUniversity of Alberta1Outline Introduction Current Practice Issues Solution Short-term solution Long-term solution Conclusion23Introduction Industrial projects are complex projects where a maze of pipes and modules have to be installed in congested work areas under tight time schedule. BIM offers potential benefits (e.g. visualization, collaboration, alignment …etc.) that can be key for complex project success.4Current Practice5BIM Utilization BIM usage can be categorized into passive and active utilization*. Passive usage encompass engineering analyses like safety and scheduling. While active usage works on extracting embedded knowledge in BIM. These benefits have not been fully implemented in industrial projects.* Jung, Youngsoo, and Mihee Joo. "Building information modelling (BIM) framework for practical implementation." Automation in Construction, 2011: 126-133.6Active Usage: Scaffolding It was required to estimate scaffold man-hour for each component separately (e.g. modules, vessels, etc.). After many trials, we were unable to build a reliable model due to  poor database structure.  trying to match data from different sources that are not fully consistent.7Issues BIM ownership (contractual level): The contractor receives only the compiled model for reviewing and visualization, Lack of standards (contractual level): labels based on the engineering firm convention, Software limitation (technical level): Quantity take-off accuracy, and Interoperability (technical level): transfer data between different systems or software.8• Enhance industry’s current practice.• Build on existing tools.• Clarify limitations.SolutionData transfer problemIndustryAcademicShort-term solution• Research objectives.• Bounded only by imagination.• A better solution for industry.Long-term solution9Short-term solution10Planning: grouping Model might contain 1 million items without enough attributes “dump model”. It is required to filter model items based on trade (e.g. extract all concrete piles, or all pipes). It takes significant time for a coordinator to visually inspect model items.11Planning: grouping12Planning: work areas Group items according to their location. Work area can be intersected with discipline (e.g. concrete items in work area 2).13Just short-term solutions The user has to visually inspect the model to find filtering rules. Rules can filter steel items, but it cannot tell their classes. Customized configuration for each project (very hard to automate). Work areas & scaffold logs are saved in a separate database. Problems in quantity take-off (only boundary volume is available).ShaperecognitionData warehouseSoftware limitation(decoupling)14Long-term solution15Long-term SolutionOntology based data format3D CAD modelsShaperecognitionAttributesBIM modelsPartial modelsSPARQLAutomationPlanning phaseScheduleExecution phaseScaffold16Shape recognition 3D shape recognition (object retrieval) to search or query 3D objects. Each 3D object has a shape signature (graph or vector), that can be compared to previously stored signatures. There are many techniques to calculate shape signature: Spatial function, Shape histogram and section images (prismatic sections) are applicable in our case.• Feature • Spatial function• Shape histogram • Section images• Topological graphs • Shape statistics17Shape histogramW610x92 W 410x85 C section HSS section1- Generate distribution for the unlabeled shape2- Match and pick from standard sections’ distribution18Shape histogram8%10%7%9%10%1,000,000 distances500,000 distances50,000 distances1,000 distances100 distancesWeight Difference %55%53% 54%30%16%159.810.05 0.0402468101214161,000,000distances500,000distances50,000distances1,000distances100distances0%10%20%30%40%50%60%Average Time (seconds)Success RateSuccess rate Avg. Time (seconds)91%84% 82%49%24%0%10%20%30%40%50%60%70%80%90%100%1,000,000 distances 500,000 distances 50,000 distances 1,000 distances 100 distances19What is semantic web?Mouse Mammalis Mammal AnimalisMouse AnimalisIBM MouseManufacturesIBM AnimalManufacturesOntology20Why semantic web?“What is the point of having countless books and libraries whose titles the owners can scarcely read through in a whole lifetime?” (“Time Magazine, Sep 8, 2014,”)The roman philosopher Seneca, c. 4 BC – AD 65 American consumed about 3.6 zettabytes of information in 2008*. Semantic web technology enables machines to derive knowledge from existing information.* Weinberger, D., 2014. Too Big to Know: Rethinking Knowledge Now That the Facts Aren’t the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room. Basic Books.21Application #1: Scaffold (revisited)22Application #2: Schedule generation Each work package will be mapped to an activity. Work package is a detailed plan that contains 500 : 1000 hours to be executed during one rotation*. Relationships between activities: Spatial based on work areas. Traditional sequences between trades.* Ryan, G., 2009. Schedule for Sale: Workface Planning for Construction Projects. Authorhouse.23Conclusion Challenges for utilizing BIM. Short-term solution can mitigate the problem. Long-term solution to enhance the process.2425

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