Based on this research, the principles and theories supporting the concept of measuring a physical and social data set describing an asset and its user community have been described. Further described are the different objective and subjective data types that can be collected and brought together to be analysed in a way that key performance criteria can be evaluated
and actionable strategies developed to effect improvements. As has been discovered there are no tools or literature detailing the integration of these principles and so it is proposed that a set of working assumptions will be made on the basis of a conceptual Test Bench. This Test Bench will indicate how the data sets and processes can be integrated to form a double loop integrated feedback process to analyse and identify actions informed through feedback to improve the operation of the asset. Figure 4.5 shows in conceptual form how these elements come together based on the feedback loops (and double loops). The physical information is managed through the black pathways, with data loops being generated from The briefing process (As Requested) The delivered asset (As Built) The operational asset (As Operated)
These data sets are gathered from the brief, the design and construction BIM delivery process and the data collected from the sensors and other devices. This is held in a database to enable query activities between the two data sets. This data can be used for longitudinal analysis to see how the design develops and cross sectional to perform analysis and comparisons with other external data sets such as the social data. This outer loop can be considered a single loop with the feedback data only able to be considered in terms of the actual sensor measured values, the as built expected values and the as required brief.
Figure 4.5 ‐ Test Bench Context Model
The social data is gathered through the green loop, this is subjective, qualitative data gathered about the perceptions of the asset's users. For this data to be of use it must be pre‐processed to sectors with relative values. The SOFI and TASIT tools both have some capability in this area, enabling calculated values to be used to visualise the perceptions of the users with respect to themselves, their co‐community members and the space they generally inhabit, this taken in isolation can be considered a single loop. The social data collected is presented in an analytic form but it can only be interpreted with respect to its control which, in this case, would be set by the research focus question or in practice the brief and regulation. It is only when these two loops are brought into sequence that we can create a double loop just as Argyris (2002) envisaged. The proposed model depicts this by the red and blue lines. The red is a reference set of building physics meta values designed to ensure that physical data of the correct state is presented for analysis. However, this is all derived from the data collected from the black processes. The blue loop then traces the
interface with SOFI and the analytics of the two data sets, physical and social linked through space and community to provide a composite analytic with physical loop outputs providing reference and reasons for social performance and vice versa, thus providing a double loop learning process. SOFI makes use of an internal function known as worlds which enables double loop testing of the convergence with and across data sets. This is then passed back to the briefing process as an "action" and thus around the loop again. The application of the double loop principle is discussed in detail as part of the application process in Chapter seven.
4.6
Summary
To fully understand the operational outcomes of an asset in service is a complex and time consuming activity. There are many dimensions to consider and the analysis is dependent upon perceptive and time critical transactions. However, taken in sections an approach can be suggested that has certain levels of scientific evidence to support the integration of these sections into a synthesised model to provide social and physical information to an analysis regime that can provide proactive feedback to identify "actions" to improve the performance of assets over time.
These principles have been found to provide useful insights in their own right but through the application of a feedback framework and methodology for integrating the processes and information a far richer insight will be possible from the point of view of the building or service user. This framework or Test Bench will be the focus of further research defined in the next Chapter to establish the validity of the approach.
Chapter 5
Research Methodology
5.1
Introduction
At the end of Chapter four the concepts of managing social and physical data in a structured methodology and the incorporation of a feedback loop had led to the development of a Test Bench. This Test Bench is the theoretical position where an approach is required to test its validity. The purpose of this Chapter is to introduce the relevant research methodologies which may be suitable for such a test and to select an appropriate approach.
5.2
Background to Research Methods
The selection of a research methodology requires the understanding of what research is and how the various methods of research can be applied to a research subject to the best effect. Leedy & Ormrod (2001) said "research is at times mistaken for gathering information, documenting facts and rummaging for information. Research is the process of collecting, analysing and interpreting data in order to understand a phenomenon." They went further to say "The research process is systematic in that defining the objective, managing the data and communicating the findings occur within established frameworks and in accordance with existing guidelines. The frameworks and guidelines provide researchers with an indication of what to include in the research, how to perform the research and what types of inferences are probable based on the data collected".
Research starts with a research question according to Williams (2007), to help the researcher focus on the phenomenon of interest. It is from this point that the researcher can chose an appropriate approach or perspective from which to make sense of each phenomenon of interest. This perspective is further developed by the review of the literature as presented in Chapters two, three and four.
The research philosophy of this work is informed and structured by the research process "onion" shown in Figure 5.1 presented by Saunders et al (2008). There are a number of approaches that can be taken to research but the lack of ambiguity of the onion approach allows a clear focus on the key issues of interest whilst ensuring a clear framework within which to work. This is supported by Guba & Lincoln (1994) who suggest that the question of research methods is of secondary importance to questions of which paradigm is applicable.
The philosophical domains that this research covers are diverse. This indicates an approach to the research as a whole that excludes an extreme view, the subjectivity of the human measurement element, including potential emotional or unpredictable responses combined with the more prescriptive engineering inputs presents a need to be clear but flexible. The following sections describe the research process which has been considered.
The literature outlines two distinctive research approaches; deductive and inductive. A deductive approach is suggested to be applicable to scientific research, where the researcher develops a hypothesis which is tested and examined to establish a theory (Hussey & Hussey 1997). The inductive approach follows research data to construct theory. Therefore, it can be suggested that both of these approaches are relevant to the scope. Saunders et al indicates that this combined approach is perfectly possible and may indeed be advantageous. The deductive elements will lend themselves well to the BIM and engineering elements, but as we move from data to theory in the world of social measurement a more inductive approach may be better suited. There are six layers to the research onion as presented by Saunders et al and they are considered below in the context of this research.