Occupant density impact on building maintenance: Data-driven approach for university buildings

Building maintenance needs in public buildings depend on occupant activities and presence. Research should understand how different types of occupant density patterns can be used to forecast the likelihood of specific kinds of maintenance requests. This research adopts a data-driven approach to evaluate experimental-based correlations between maintenance work orders number (relating to a set of Italian university buildings as a relevant case study) and occupant density, thanks to exceptional conditions due to COVID-19 pandemic, which significantly altered building use. Results offer a power-law-based correlation model, confirming that the reduction of occupant density in the COVID-19 lock-down phases impacted the number and perceived severity, but not the typologies, of maintenance work orders. The retrieved correlation model occupant could be directly used to define and prioritize maintenance strategies given occupant density. Future research could use the model to define outsourcing and contract definitions starting from historical data on maintenance actions.

Sustainable and resilient strategies for touristic cities against COVID-19: An agent-based approach.

Touristic cities will suffer from COVID-19 emergency because of its economic impact on their communities. The first emergency phases involved a wide closure of such areas to support "social distancing" measures (i.e. travels limitation; lockdown of (over)crowd-prone activities). In the "second phase", individual's risk-mitigation strategies (facial masks) could be properly linked to "social distancing" to ensure re-opening touristic cities to visitors. Simulation tools could support the effectiveness evaluation of risk-mitigation measures to look for an economic and social optimum for activities restarting. This work modifies an existing Agent-Based Model to estimate the virus spreading in touristic areas, including tourists and residents' behaviours, movement and virus effects on them according to a probabilistic approach. Consolidated proximity-based and exposure-time-based contagion spreading rules are included according to international health organizations and previous calibration through experimental data. Effects of tourists' capacity (as "social distancing"-based measure) and other strategies (i.e. facial mask implementation) are evaluated depending on virus-related conditions (i.e. initial infector percentages). An idealized scenario representing a significant case study has been analysed to demonstrate the tool capabilities and compare the effectiveness of those solutions. Results show that "social distancing" seems to be more effective at the highest infectors' rates, although represents an extreme measure with important economic effects. This measure loses its full effectiveness (on the community) as the infectors' rate decreases and individuals' protection measures become predominant (facial masks). The model could be integrated to consider other recurring issues on tourist-related fruition and schedule of urban spaces and facilities (e.g. cultural/leisure buildings).

A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria.

University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders' needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible "infectors". While waiting for the vaccine, as for other public buildings, policy-makers' measures to limit virus outbreaks combine individual's strategies (facial masks), occupants' capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission confirmed by international health organizations. The building occupancy is defined according to university scheduling, identifying the main "attraction areas" in the building (classrooms, break-areas). Scenarios are defined in terms of occupants' densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users' comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants' density are thus provided to help stakeholders in organizing users' presences in the building during the pandemic. ELECTRONIC SUPPLEMENTARY MATERIAL ESM: supplementary material is available in the online version of this article at 10.1007/s12273-021-0770-2.