Building Information Modelling in UK Construction Projects: A State of the Art Review

This paper aims to present a state-of-the-art review of the scope and practical implications of the Building Information Modelling (BIM) platform in the UK construction practice. Theoretical developments suggest that BIM is an integration of both product and process innovation, not just a disparate set of software tools. BIM provides effective collaboration, visual representation and data management, which enable the smooth flow of information throughout the project’s lifecycle. The most frequently reported benefits are related to Capital Cost (capex) and Operational costs (opex) and time savings. Key challenges, however, focus on the interoperability of software, capital installation costs, in-house experience, client preference and cultural issues within design teams and within the organisation. The paper concludes with a critical commentary on the changing roles and a process required to implement BIM in UK construction projects, and suggests areas for further research.

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.