An aircraft accident database of human experience in evacuation derived from aviation accident reports

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in aviation safety. However, such models have a heavy dependency on real evacuation data. The Fire Safety Engineering Group of the University of Greenwich is undertaking a large data extraction exercise in order to address this issue. This paper describes the extraction and application of data from aviation accident reports. To aid in the storage and analysis of the raw data, a computer database known as AASK (Aircraft Accident Statistics and Knowledge) is under development. AASK is being developed to store human observational and anecdotal data contained in accident reports and interview transcripts. AASK currently contains information from 25 survivable aviation accidents covering the period 04/04/77 to 06/08/95, involving some 2415 passengers, 2210 survivors, 205 fatalities and accounts from 669 people. Copyright © 1999 John Wiley & Sons, Ltd.

Local likelihood smoothing of sample extremes

Trends in sample extremes are of interest in many contexts, an example being environmental statistics. Parametric models are often used to model trends in such data, but they may not be suitable for exploratory data analysis. This paper outlines a semiparametric approach to smoothing example extremes, based on local polynomial fitting of the generalized extreme value distribution and related models. The uncertainty of fits is assessed by using resampling methods. The methods are applied to data on extreme temperatures and on record times for the womens 3000m race.

Routine maintenance and sustainability of existing social housing

Improving the sustainability of the housing stock is a major challenge facing the UK social housing sector. UK social housing accounts for approximately 18% of total UK housing and generates maintenance costs in the region of 1.25 billion pounds per annum. The extent to which routine maintenance can be used as a vehicle to improve the overall sustainability (social, environmental and economic) of existing social housing is one focus of a 5 year EPSRC funded research programme. This paper reports the findings of a questionnaire survey examining current social housing maintenance practices and attitudes towards sustainability. The research found that, whilst the stock condition survey is the favoured format for determining maintenance need and economics the basis for priority setting; neither systematically addresses wider sustainability issues; and, whilst cost is a major barrier to more sustainable solutions being adopted, landlords are able and have the desire to improve their practices.

Routine maintenance and sustainability of existing social housing

Presently the UK social housing stock accounts for approximately 18% of the total UK housing with maintenance costs in the region of £1.25 billion per annum. In terms of its impact on the environment, housing is generally responsible for approximately 27% of the UK’s CO2 emissions. The extent to which routine maintenance (planned preventative and responsive) can be used as a vehicle to improve the overall sustainability (social, environmental and economic) of existing social housing is one focus of a 5 year EPSRC funded research programme. This paper reports on the findings of a series of in-depth interviews with social housing providers examining current social housing maintenance practices and attitudes towards sustainability. This paper will report the initial findings of interviews and outline a new performance based multi-criteria maintenance model from which an AHP hierarchy will be presented, integrating the principles of sustainability into maintenance strategies