Narrative modelling: A comparison of high and low mass dwelling solutions in Afghanistan and Peru

Abstract

Displaced populations are housed in various constructions, including lightweight predesigned structures. Theoretically, self-built heavyweight structures should ensure better temperatures and be closer to cultural norms. To examine this directly for the first time, lightweight pre-designed solutions are compared with high-mass self-built alternatives in Afghanistan and Peru, via monitoring, dynamic simulation, occupant surveys, the Shelter Assessment Matrix (SAM) and ShelTherm. Lightweight solutions increase peak summer temperatures, but only by 2°C, but reduce minimum temperatures by up to 5°C. Simulations only provided a qualitatively similar time series to the monitoring, because identical homes showed a large variance in temperatures. This questions the benefit of simulation compared to approaches which concentrate on whether shelters exacerbate or ameliorate external temperatures. In addition, a dwelling provides more than comfort, it supports family life, which is best addressed by tools like SAM, not thermal simulation. Hence it might be ideal to recommend high-mass self-build if possible, and to focus modelling efforts on qualitative aspects of simulation time-series, such as whether the building suppresses or exacerbates external conditions, and equally on psycho-cultural aspects. The term narrative modelling is introduced to describe this new approach which will be of direct benefit to the humanitarian community. Building simulation engineers have much to offer the humanitarian shelter sector, however they are not often brought into play in a disaster. Hence, we suggest a practical role they can take is in examining strategies before disasters and in creating knowledge or analysis methods that aid agency staff can apply on the ground. Here we showcase this approach. It is clear that although dynamic thermal simulation is highly useful, psycho-social aspects are equally important, thus engineers are likely to need to use tools that consider such aspects in order to maximize the usefulness of their conclusions.