Normatividad

10.1 Chapter introduction

Initial consideration of the research problem suggested the possibility of applying Artificial Intelligence (Al) techniques to Procession. In the end, there was insufficient time to fully develop these ideas for the initial and operational prototypes of Procession. However, considerable effort had already been put into progressing this approach for the VTK prototype v1.0 (see Appendix 16).

One of the main reasons for not implementing the Al options was the difficulty in evaluating them. The author’s prototyping methodology was described in an earlier chapter (see Figure 3.5). The timescale of this research only allowed for two prototyping cycles. In order to evaluate a software tool that ‘learns’ from repeated use, each prototyping loop would have required multiple sub-loops. This in term would have added complexity to the evaluation methodology.

This chapter briefly describes the initial work carried out in connection with applying Al techniques to the Procession tool, leaving open the

possibility of further investigation. In addition, several other directions for future development are proposed.

10.2 Artificial Intelligence (Al)

It is suggested that later research versions of Procession might use a Deliberative Al approach, such as Case-Based Reasoning. It is proposed that heuristics, developed from experience on previous projects, would be stored in a ‘legacy archive’ and used to calculate the relevance of

performance deviations from the project baseline. In Procession’s VTK prototype v1.0 (see Section 6.3) an early attempt was made to implement this approach, with the Significance of Deviance Algorithm (see Appendix

16). This algorithm calculates the significance of an individual project task to the current total value for a specific deviation parameter in a specific project. The mathematical approach utilised is based on statistical methodologies, such as ANalysis Of VAriance (ANOVA). In Procession v1.0, this value was termed ‘the significance of deviance', which is a current snapshot of

significance. By using the current value to increase or decrease an on going record of previous significance (the legacy archive), Procession v1.0 ‘learnt’ from its runtime experiences.

Appendix 16 provides a more in depth explanation of the significance calculation. A basic introduction to Al is provided in Appendix 15. This concludes that techniques such as Case-Based Reasoning may be the best choice for an application such as Procession. The author has published further details on Procession vl.O elsewhere (North 2000c).

10.3 Implementing ‘quality’ as a deviation parameter

In Chapter 5, a 3D framework for measuring construction planning performance was proposed. One of the deviation performance parameters shown in Figure 5.3 is ‘quality’ or ‘conformance’. However, it was stated that visualising quality was beyond the scope of this research. In the longer term, quality is a matter of great concern to construction clients and its future inclusion in Procession seems desirable. Project planning tools (such as Microsoft Project) do not currently provide any method for measuring quality. It is possible to imagine a quality measurement technique that would

10.4 Adding ‘time’ as a dimension

Procession only deals with time in the context of slippage from the planned programme (see Figure 5.3) i.e. duration variance or work variance. Each Procession 3D data surface is a single snapshot at time ‘T . The ability to view time-based changes in the data surface might prove extremely

informative for the user. As a step towards this, Procession v1.2 features the button ‘Toggle Animate' (see Appendix 14). This simulates the animation of the data surface, representing changes in Deviation Parameter levels over time. Future work might consider how to replace this simulated animation with a real chronological sequence.

10.5 Automating morphological analysis

In Chapter 5, Morphological Frameworks were introduced and one possible decomposition of construction performance into dimensions was proposed (see Figure 5.2). Of course, any such deconstruction of a system is largely subjective. As such, researchers are responsible for justifying their chosen breakdown with existing literature and their own investigations. In the specific case of construction performance (as presented in this thesis),

further research might consider the applicability of alternative frameworks and their suitability for mapping to visual structures.

Taking this one stage further, could include the automating of the morphological analysis process. It is possible to envisage a software system capable of analysing a given system or problem and proposing an

appropriate set of dimensions. There might be two, three or as many dimensions as specified by the user. Input to such a system might take the form of an information modelling language document, or schema. The

with the dimensions labelled. A secondary software layer might allow the automatic generation of Information Visualisation graphics from the framework.

It is possible to imagine a scenario where a user presented with a data set and its information model, could rapidly generate an insightful Information Visualisation of this data. Initial reading suggests that little or no work has been carried out in this area. In the first edition of the peer-reviewed journal Information Visualization, editor-in-chief Chen (2002) hints at the importance of automatic generation when he writes:

“the question is whether such geometry is intrinsically derivable from the data or one has to impose it on top of the data. Information Visualization traditionally focuses on finding meaningful and intuitive ways to present non-spatial and non-numerical information to people.” 10.6 Chapter summary and contextualisation

This chapter proposed a future direction for the work initiated by this research. Before the development of Procession’s Initial Prototype (v1.1), the previous version (vl.O) made use of slightly different concepts and

technologies (see Section 6.3). Procession v1.0 experimented with the use of AI techniques. Heuristics were developed from experience on previous projects, stored in a ‘legacy archive’ and used to calculate the relevance of performance deviations from the project baseline.

Other possibilities for the future include; implementing ‘quality’ as a deviation parameter, adding ‘time’ as a dimension and automating

morphological analysis. It is suggested that later versions of Procession might utilize and expand the ideas discussed in this chapter.