Identifying fire risk in urban centres is instrumental for supporting informed decision-making and outlining efficient vulnerability mitigation strategies. Historic centres are particularly complex in this regard due to the high density of combustible materials in these areas, the favourable fire propagation conditions between buildings, and the complex urban morphology, which makes the evacuation of inhabitants difficult in case of a fire emergency. Recent safety regulations tend not to be fully applicable to historic city centres, where the specificities of the buildings, together with the need to safeguard their heritage value, make the rules for new buildings incompatible. For that reason, an adaptation of current evaluation methods is required to assure the safety of these places. The present paper aims to contribute to this topic by presenting and discussing the results obtained from the application of a simplified fire risk assessment methodology to a representative part of the historic city centre of Aveiro, Portugal. Data were collected through fieldwork building inspections and the results were mapped using a Geographic Information System tool. The study reveals that around 63% of the assessed buildings have a level of fire risk greater than the level of risk which is acceptable for buildings with this type of use and value. Based on the work developed, different mitigation strategies are suggested and compared. Finally, the results obtained in this work are compared with results published for historic urban areas with similar characteristics.

Autoclaved aerated concrete (AAC) masonry is widely used in civil construction but requires further investigation. Hence, this experimental study evaluated three types of interface treatment between the reinforced concrete structure and AAC masonry, in scale, after a uniaxial compression resistance test. The types of interface treatment considered are reinforcement with steel bars, with rough polymeric cementitious mortar, and without treatment. The maximum load capacity, displacements, and occurrence of cracks were analysed. The results showed that the maximum individual load capacity did not significantly differ among the examined groups. However, the analysis of the displacements and cracks showed that the group with steel reinforcement had the smallest displacements and largest cracks. This behaviour is owing to the greater solidarity of forces conferred by steel reinforcement.

Climate change and human interventions can boost wildfires. Although naturally happening, massive events are becoming more frequent and severe. In Portugal’s mainland, many rural settlements are populated mainly by older people, and uninhabited houses are frequently poorly conserved. This combination leaves the Wildland–Urban Interface (WUI) dangerously exposed to the fires. Pursuing the understanding of WUI areas, this study applies the Wildland–Urban Interface Index (WUIX) assessment methodology to an area severely affected by the massive 2017 wildfire of Pedrógão Grande, Leiria, Portugal. The primary objective of this study was to compare the results from WUIX with the areas burned during the fire event. As a result, maps of WUI effect were generated, visually pointing to villages with higher severity compared to the others. A statistical analysis was performed in three villages from the region to validate the results by comparing the accuracy of the results obtained to the actual damages. The results point out a high correlation between the WUIX and the real scenario despite the apparent challenges in determining the variations in different types of fire effect. Finally, the WUIX results align with the data from the Pedrógão Grande wildfire, showing that some are promising in conjunction with other wildfire indicators.

Wildland-Urban Interface (WUI) areas are critical zones where human settlements and wildland fuels intersect, resulting in heightened wildfire risk and increased potential for losses. This study presents a two‑phase review of literature where artificial intelligence (AI) is applied in wildfire and WUI studies, combining bibliometric mapping for wildfires and a thematic content analysis for WUI. In Phase One, over 3500 publications indexed in Scopus were analysed, searching generally for AI applications in wildfire research to investigate trends. This analysis revealed four dominant clusters: deep learning around computer vision, remote sensing-driven analysis, Random Forests and ensembles with environmental variables, and decision-support prediction. For this phase, the results suggested a notable rise in machine learning after 2016, potentially driven by increased computational capacity and the availability of high-resolution remote sensing, aligned to concerns about the growing impacts of wildfires. Phase Two focused on 52 peer‑reviewed studies explicitly addressing AI for WUI contexts. They were categorised into five thematic areas: risk and vulnerability, human behaviour and decision support, vegetation and fuel, detection and monitoring, and fire behaviour and spread. Models such as random forests, convolutional neural networks, and traditional statistical integrate various datasets, including satellite and UAV/LiDAR imagery, GIS information, and topographic-climatic and socio-economic indicators. The results indicate that AI has contributed to accurate models in spatial resolution and predictive risk for WUI areas, but faces challenges such as simplified WUI maps, fragmented and scarce data, limited model generalisation, and interpretability. Overall, this review identifies opportunities for detailed and diverse WUI mapping, explainable AI, and interdisciplinary collaboration providing means for actionable strategies for WUI communities.