Description
The climate-change effects are becoming ever more significant in the management of archaeological sites, which in itself are notoriously a particularly complex system within the heritage preservation matter. The decay trend can be heavily affected by extreme and/or fast changes of weather conditions. While short-term solutions are not always available to avoid disasters, pro-active maintenance actions can be instead carried out to successful control the effects of the weather conditions fast change and to mitigate the extremes effects. A reliable knowledge and prevision of the decay evolution represents in this within the key point to design a fruitful maintenance plan. To this scope, a detailed heritage condition assessment requires in-depth survey and analysis, which are not typically compatible with the economical and time resources generally available in complex archaeological sites. Otherwise, faster and cheaper surveys, even if carried out with innovative technologies, do not allow a detailed assessment. An innovative monitoring approach has been developed and applied in the Archaeological Park of Pompeii (Southern Italy). The methodology considers multi-scale (data and time resolution) and multi-level (assessment accuracy) approaches consistent with the master data and the informative system already developed in the Park. In particular, it consists of three assessment level: Local (LA), General (GA) and Detailed (DA) Screening. LA is multi-yearly provided to lead an extensive heritage condition knowledge, by means of expeditious on-site surveys carried out by expert teams. GA is monthly provided to lead a sufficient knowledge about the overall heritage conditions and it is carried out by means of drones and artificial intelligence (AI) applications. GA can be also feasibly achieved to quickly manage emergency conditions. DS is developed punctually to in deep assess and resolve recognized critical local conditions, also with the support of monitoring devices. The proposed approach makes use of WebGIS, IoT and Digital Twins to describe the heritage health conditions and to develop predictive models to support pro-active maintenance policies.
