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Towards Automated Compliance Checking in the Construction Industry

  • Thomas H. Beach
  • Tala Kasim
  • Haijiang Li
  • Nicholas Nisbet
  • Yacine Rezgui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8055)

Abstract

The Construction industry has a complex structure of regulatory compliance, consisting of statutory requirements and performance based regulations. The increasing importance of sustainability has further intensified this, with a new building’s compliance against sustainability assessment methodologies now often an important contractual requirement.

Automatic compliance checking against these requirements has been long sought after within this industry and several approaches have attempted to achieve this goal. The key improvement that can be made to many existing approaches is enabling the development and maintenance of the regulations by those who are most qualified to do this the domain experts. This is illustrated by the fact that in many cases regulatory compliance systems are closed and when modifications are needed they must be made by software’s developers. This process is simply not viable in this industries rapidly changing environment.

In this paper we describe our framework for compliance checking, showing the potential for utilising an integrated process to enable domain experts to create and maintain their own regulations that can then be executed by an open source rule engine. We will describe our process, the methodology and software developed to support it. We will present our initial results in the form of two case studies illustrating progress towards automation of commonly used regulations. Finally, we will also discuss how our approach could be generalised to other related sectors to enable the adoption of a similar approach towards automatic regulatory compliance.

Keywords

Data Item Construction Industry Regulation Document Block Level Compliance Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Thomas H. Beach
    • 1
  • Tala Kasim
    • 1
  • Haijiang Li
    • 1
  • Nicholas Nisbet
    • 2
  • Yacine Rezgui
    • 1
  1. 1.School of EngineeringCardiff UniversityCardiffUK
  2. 2.AEC3 UK LtdUK

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