Temperamento

Traditionally, QSs and estimators take off manually from the Architect’s paper drawings or import 2D CAD drawings into an estimating software package and carry out onscreen QTO (Ajibade & Venkatesh, 2012; Drogemuller & Tucker, 2003). This process is described by Sabol (2008) and Ajibade & Venkatesh (2012) as a time consuming and a costly process, which is prone to human errors that often lead to inaccuracies in the estimates. Monteiro & Martins (2013) explain that issues can arise in this process because measurement is essentially open to interpretation. For example, a QS measuring from 2D drawings may not have the same vision as the designer or make assumptions that are not in line with the designer’s intent.

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Figure 3.6: 2D On-Screen Take Off (Lovegrove, 2010)

As demonstrated in Figure 3.6, the development of QTO software from 2D CAD has made it easier for QSs measuring quantities from 2D drawings by automating the computation of areas, lengths and volumes (Wijayakumar & Jayasena, 2013). Wijayakumar & Jayasena (2013) ascertain that automation has greatly improved QSs working efficiency but the manual process is still prevalent in 2D QTO. They explain that QSs still have to manually extract information from the drawing data files using digital pointers rather than scale rulers. This automates the QTO process and reduces mistakes, but it is still prone to human error and is time consuming (Sabol, 2008; Matipa et al. 2008; Wijayakumar & Jayasena, 2013).

BIM has the potential to help QS practices and construction companies estimate the cost of a project with more detail and accuracy, while reducing time and expenses (Eastman et al., 2011; Goucher & Thurairajah, 2012). Figure 3.7, illustrated by Eastman et al. (2011. p.279), presents the difference between the traditional estimating process and the 3D/BIM estimating process. The top half of the figure illustrates the 2D QTO process, where you can either manually take off the quantities from dimensions on the drawings or digitize quantities by using onscreen QTO applications. In the ‘BIM based estimating process’ (bottom half), Eastman et al. (2011) accentuate that there is a digital link between the quantities extracted from the 3D model and the assembly of the estimate. The quantities from the model can be automatically generated within the BIM authoring tool or can be imported into specialised BIM estimating software and manually or automatically extracted. This is discussed in greater detail in Section 3.4.2.

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Figure 3.7: Estimating Processes (Eastman et al., 2011, p.279).

The benefit of cost planning through BIM, as discussed by Sabol (2008), is that it quantifies exactly what is in the model, so there should be no variations between what has been measured and what is required. It is also much simpler and faster to quantify and cost a variation or an extension to the works as the quantities will automatically be updated when the model is changed (Eastman et al., 2011). Monteiro & Martins (2013) determine that QTO in BIM is carried out by routines that can perform calculations on the geometry of the model to extract measurement data. These are provided as an output through schedules within the software, which can also be exported to MS Excel (Fung et al., 2014).

One of the issues with extracting quantities within the BIM authoring tool is that it is primarily a design tool and is not robust enough for the QS carrying out cost estimating (Forgues et al., 2012; Olatunji et al., 2010; Sabol, 2008). A QS may not find the Architect’s BIM tool sufficient for cost plan modelling because architectural BIM schema are not compatible with the elemental or trade code structure required under QS classifications and Standard Method of Measurements (SMMs) (Lovegrove, 2014; Sabol, 2008; Sawhney, 2014). Figure 3.8 illustrates an example of the hierarchical structure of the Revit model schema (on the left). Quantities automatically extracted from that schema will output in the Revit structure

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(Category, Family & Type) and thus will need to be mapped to the applicable QS building elemental schema (on the right). This will be outlined in further detail in Section 3.4.3.

Figure 3.8: Classification of Building Elements (Wu et al. 2014)

This means that QTO outputs from the model are limited to the classification structure of the model, which is further compounded as these output schedules cannot be manipulated within the application (Monteiro & Martins, 2013). Ultimately, there is no magic button in the BIM authoring tool that will generate a BOQ in Ireland’s Agreed Rules of Measurement 4 (ARM 4) or UK’s NRM 2. Different jurisdictions have different SMMs. Discussed in Section 2.3, In UK, the SMM 7 has been replaced by the NRM 2 and in Ireland the current standard is the ARM 4. Australia, the Middle East and Canada also have different SMMs. Olatunji et al. (2010) state that multiple SMMs mean that the financial returns to software developers to develop country specific BIM/SMM filters are limited. Olatunji et al. (2010) note that there is a major issue in developing country specific estimates if the support of such filters is modest and the investment required to develop robust filters is considerable. The development of such filters has so far been restricted to research projects. Arguably there is a case for re-engineering measurement procedures so that they are comparable with BIM schema (Monteiro & Martins, 2013). There are precedents for more extreme approaches, which disregard SMMs altogether in favour of BIM-related cost code structures (Goucher & Thurairajah, 2012; Monteiro

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& Martins, 2013; Olatunji et al., 2010). These issues will be discussed further in Section 3.4.3.

Despite agreement among a number of authors that the advantages of BIM revolve around a single model, Howell & Batcheler (2005), Taylor & Bailey (2011) and Smith (2014) dispute this in practice. Sabol (2008, p. 11) maintains that “a monolithic, do everything system” is yet to be developed. In practice these authors

have found that design team members rely on purpose built models including separate models for 3D design, structural steel fabrication, energy analysis and the 4th and 5th dimensions of sequencing and construction cost planning (Howell & Batcheler, 2005; Sabol, 2008; Smith, 2014). This view poses a weakness to the idealistic BIM process outlined above, as it appears to go against the fundamental principle of using a single model for the purpose of consistency, collaboration, and reliability. The primary driver of these discipline specific purpose-built models, is that individuals who have greater expertise in their own fields prefer to build their own model in the way that suits them with discipline dedicated technology (Mitchell, 2012).

Mitchel (2012) states that from a QS perspective, practitioners are currently utilising their own traditional software to extract BIM quantities into familiar programmes. This has been facilitated by existing QS and estimating software adding BIM capability (Wu et al., 2014). Goucher & Thurairajah (2012) state that this process has seen the emergence of 5D BIM specialists whom can utilise 5D BIM technology, rather than rely on the BIM authoring tool to carry out QS activities. Sylvester & Dietrich (2010) and Crowley (2013) agree that with the 5D process, practitioners can move from spending time on generating quantity and cost information, to validating the quantities and costs contained within their estimates. However, this does not mean that the BIM authoring tool is not sufficient as a QTO application. It can produce a useful schedule of quantities and also through the use of open BIM (giving users the ability to programme/add additional functionality) provide users with the ability to add QS functionality. Further developments in this field will bring greater integration between disciplines possibly eliminating the need for a plethora of discipline specific software, but currently, this is not yet common practice (Mitchel, 2012; Eastman et al, 2011; Smith 2014; Howell & Batchelor).

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3.4.2 5D BIM Estimating

QSs have traditionally relied on the functionality and computation capabilities of the spreadsheet. Most QSs produce their estimates and cost reports in spreadsheet applications such as MS Excel and Open Office, while many others use estimating applications that contain workbooks similar to standard spreadsheet applications (Matipa et al., 2008). The spreadsheet provides an automated tool that can carry out the number crunching, while providing an adaptable format to present cost information (OGC, 2007). Variations of the spreadsheet are embedded in many estimating applications where QTO, cost databases and the spreadsheet workbook exist in the same product and are harnessed and linked to help produce formatted cost plans.

According to Eastman et al. (2011, p. 220), “No BIM tool (BIM design tool) provides the full capabilities of a spreadsheet or estimating package, so estimators must identify a method of BIM QTO that works best for their specific estimating processes”. Sylvester & Dietrich (2010) recommend that the BIM 5D process

requires third party software to conduct specialised activities related to detailed cost estimating. Eastman et al. (2011, p. 220) present three primary options for QSs working with BIM, based on Figure 3.7 presented previously in Section 3.4.1:

1. Export BIM object quantities to estimating software 2. Use a BIM quantity take-off tool

3. Link the BIM tool directly to the estimating software

Option 1 entails QSs exporting quantities from the BIM authoring tool, such as Revit Architecture, to the QSs estimating tool. Eastman et al. (2011) explain that even though there are over one hundred commercial estimating packages, MS Excel is the most commonly used. They maintain the reason for this is spreadsheet programmes provide the computational and formatting capabilities necessary for BOQ production at a price that is considerably less than bespoke estimating software. BIM quantities can be exported to MS Excel, where the QS can validate and utilise this data in their cost reports (Eastman et al., 2011; Wu et al., 2014). However, Hardin (2009) contends that this process is ineffective because there is no automatic link between the model and the quantities exported to MS Excel. Option 1 may also require QSs to buy both the design tool as well as the estimating software; firstly to generate and

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export the quantities from the design tool and then to use the quantities to produce an estimate in the estimating tool. It would also require QSs to have knowledge of both systems (Monteiro & Martins, 2013).

The second alternative proposed by Eastman et al. (2011) is to use an estimating/QS tool that is capable of linking directly to a BIM tool, via a plug-in, such as VICO, Innovaya and Tocoman iLink. Matipa et al. (2008) describe this system as an integrated environment, where two software vendors have solved their interoperability problems through collaboration in an integrated system. This is a move to a more holistic BIM process where the design and cost management functions are housed in the same application. This process enables users to link objects in the BIM to assemblies and items in the QS’s estimating database (Aouad et al., 2005; Eastman et al., 2011; Monteiro & Martins, 2013). This method is not possible if the Architect’s authoring tool is not compatible with the QS’s plug-in, or if the QS is using software that does not have this capability. Another practical issue, is that in this option (similar to option 1) the QS may be required to buy BIM design software that they are not familiar with and which will not give them the capabilities to carry out 2D onscreen measurement, which at the moment is still the most prevalent method in producing quantities (Forgues et al., 2012).

Traditional estimating applications have generally not been designed specifically for BIM. However, many of the leading software estimating vendors have added a BIM interface to their existing application as an updated extra (Eastman et al., 2011; Lovegrove, 2014). Outlined by Eastman et al. (2011) as their third option for QSs working with BIM, it integrates the outputs of the model with the cost database and workbook functions of the existing software (Matipa et al., 2008; Sabol, 2008). This is different than option 2, in that the 5D process happens in the QS’s software rather than the BIM design tool. For this process to be facilitated, information needs to be exchanged between the authoring tool and the 5D BIM tool (Monteiro & Martins, 2013). As discussed in Section 3.3.8, this is carried out with the use of interoperable data, exported from the BIM application and imported subsequently into the 5D application (Monteiro & Martins, 2013; Wijayakumar & Jayasena, 2013). Once the model is in the 5D tool, Sattineni & Bradford, (2011) state that the 5D application has the potential to leverage the model by culling QTO and through a database management interface, price the components necessary for cost estimating. The

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primary advantage of option 3 is that there is no need to purchase or get trained in new BIM software (Eastman et al., 2011) other than a possible upgrade to the existing estimating application. Another advantage is that the product still has the functionality for 2D take off, which is still prevalent in QS practice. Monteiro & Martins (2013) state that most 5D applications fall into this category such as Exactal’s ‘CostX’, Nomitech’s ‘CostOS’, Buildsoft’s ‘Cubit’, CATO and Autodesk’s ‘QTO’. Matipa et al. (2008) propose that the skill in this process is the ability to navigate the model to extract the relevant quantities, and to do this, the QS must be familiar with the structure of the model. Forgues et al. (2012) maintain that a choice has to be made between adopting an application compatible with the company legacy and utilising BIM software that can integrate with their existing cost database. It is for this reason that many companies choose option 3, as they can continue with their legacy programmes. These three options indicate that this choice is absolute. As discussed in the previous section currently QSs are utilising their own discipline specific software to extract quantum from the 3D authored model (Smith, 2014; Matipa et al., 2010; Wu et al., 2014). However, if the quantities were already somewhat mapped to the QSs schema prior to the QS importing it into their software, it would help the QS post-processing the quantities for use in their estimates. This is discussed in further detail in the following section.