Architectural research and its enemies

Like the Research Assessment Exercise (RAE) that preceded it, the UK government's proposed Research Excellence Framework (REF) is a means of allocating funding in higher education to support research. As with any method for the competitive allocation of funds it creates winners and losers and inevitably generates a lot of emotion among those rewarded or penalised. More specifically, the 'winners' tend to approve of the method of allocation and the 'losers' denigrate it as biased against their activities and generally unfair. An extraordinary press campaign has been consistently waged against research assessment and its methods by those involved in architectural education, which I will track over a decade and a half. What follows will question whether this campaign demonstrates the sophistication and superior judgment of those who have gone into print, or conversely whether its mixture of misinformation and disinformation reveals not just disenchantment and prejudice, but a naivety and a depth of ignorance about the fundamentals of research that is deeply damaging to the credibility of architecture as a research-based discipline. With the recent consultation process towards a new cycle of research assessment, the REF, getting under way, I aim to draw attention to the risk of repeating past mistakes. Copyright © Cambridge University Press 2010.

The pursuit of academic excellence and business engagement: Is it irreconcilable?

Universities currently need to satisfy the demands of different audiences. In light of the increasing policy emphasis on "third mission" activities, universities are attempting to incorporate these into their traditional missions of teaching and research. University strategies to accomplishing its traditional missions are well-honed and routinized, but the incorporation of the third mission is posing important strategic and managerial challenges for universities. This study explores the relationship between university-business collaborations and academic excellence in order to examine the extent to which academic institutions can balance these objectives. Based on data from the UK Research Assessment Exercise 2001 at the level of the university department, we find no systematic positive or negative relationship between scientific excellence and engagement with industry. Across the disciplinary fields reported in the 2001 Research Assessment Exercise (i. e. engineering, hard sciences, biomedicine, social sciences and the humanities) the relationship between academic excellence and engagement with business is largely contingent on the institutional context of the university department. This paper adds to the growing body of literature on university engagement with business by examining this activity for the social sciences and the humanities. Our findings have important implications for the strategic management of university departments and for higher education policy related to measuring the performance of higher education research institutions. © 2013 Akadémiai Kiadó, Budapest, Hungary.

Real Time Concrete Damage Visual Assessment for First Responders

First responders are in danger when they perform tasks in damaged buildings after earthquakes. Structural collapse due to the failure of critical load bearing structural members (e.g. columns) during a post-earthquake event such as an aftershock can make first responders victims, considering they are unable to assess the impact of the damage inflicted in load bearing members. The writers here propose a method that can provide first responders with a crude but quick estimate of the damage inflicted in load bearing members. Under the proposed method, critical structural members (reinforced concrete columns in this study) are identified from digital visual data and the damage superimposed on these structural members is detected with the help of Visual Pattern Recognition techniques. The correlation of the two (e.g. the position, orientation and size of a crack on the surface of a column) is used to query a case-based reasoning knowledge base, which contains apriori classified states of columns according to the damage inflicted on them. When query results indicate the column's damage state is severe, the method assumes that a structural collapse is likely and first responders are warned to evacuate.

Improving Pothole Recognition through Vision Tracking for Automated Pavement Assessment

Pavement condition assessment is essential when developing road network maintenance programs. In practice, pavement sensing is to a large extent automated when regarding highway networks. Municipal roads, however, are predominantly surveyed manually due to the limited amount of expensive inspection vehicles. As part of a research project that proposes an omnipresent passenger vehicle network for comprehensive and cheap condition surveying of municipal road networks this paper deals with pothole recognition. Existing methods either rely on expensive and high-maintenance range sensors, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In our previous work we created a pothole detection method for pavement images. In this paper we present an improved recognition method for pavement videos that incrementally updates the texture signature for intact pavement regions and uses vision tracking to track detected potholes. The method is tested and results demonstrate its reasonable efficiency.

A classification of data quality assessment methods

Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development.