The vulnerability assessment of architectural cultural heritage represents a challenging, however necessary, activity towards defining risk preparedness plans for historic urban environments and more resilient communities. Nevertheless, the singularities and specificities that historical constructions often present the need for customising/tailoring and adapting generalised and common vulnerability assessment approaches. The balance between detail and scale when surveying historical structures implies that the feasibility of achieving urban-scale data acquisitions depends primarily on adopting relatively simple models and descriptions. In this sense, several simplified (often parametric) approaches have been developed for assessing the seismic vulnerability of historical constructions based on a limited set of descriptors. Although the selection of the parameters that influence the seismic response of the structures have been drawn from empirical observations (e.g., in the aftermath of intense seismic events), such regressive analysis may be improved and facilitated by employing Machine Learning categorisation algorithms. This chapter investigates the usability of parameter-based screenings of historical cities for assembling an urban-scale database that is further used for assessing the analytical vulnerability of historical constructions based on existing intensity/damage models. Furthermore, the observations acquired in the aftermath of a strong seismic event (that of the 19th of September 2017 in Mexico) are herein used for calibrating the model using a Random Forest Classifier algorithm, achieving a more representative intensity/damage model and, therefore, representing an opportunity for obtaining typologically tailored seismic vulnerability models in the context of a feasible urban-scale survey and post-earthquake observations. These trained algorithms are valuable tools in supporting risk preparedness and management policies based on the results of rapid screenings for proactively identifying vulnerable assets and evaluating the impact of different mitigation measures.

In September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations.

The characterisation of the seismic vulnerability of historical constructions represents a complex problem in which the typological variability, the difficulty of performing reliable large-scale assessments and dealing with a large database all play a role. Nevertheless, reducing the uncertainty regarding the structural vulnerability of the existing building stock (mostly for small and/or isolated human settlements) is key for risk assessment and management. The present work proposes a novel approach based on the integration of a series of open-source tools for assembling a vulnerability-oriented database that is linked to a series of external services for increasing its capabilities. The database was implemented in a Geographical Information System (GIS) environment and contains the survey of a seismic vulnerability index for masonry constructions based on an adapted version of the GNDT-II approach. A customised Python-based software for reading, managing and editing the database is herein presented. This program allows the execution of the most typical operations with no assistance from the GIS environment, facilitating user interaction. Furthermore, the calculations regarding the vulnerability index and levels of damage have been implemented in this program. Alternatives for distributing the database are implemented and discussed, such as cloud-based distribution and the use of the Transactional Web Feature Service (WFS-T) protocol for its virtual publishing. The entire framework herein presented is a replicable and feasible workflow that can be set even with reduced infrastructure, allowing a progressive enlargement.

The seismic vulnerability assessment of large sets of constructions is meaningful for anticipating post-earthquake scenarios in the context of loss prevention and resilience. These activities must overcome several difficulties related to the size and heterogeneity of the building stock, becoming even more complex when addressed to historical and vernacular structures. The analysis of post-earthquake data has permitted the design of vulnerability assessment methods based on the identification of parameters related to the geometrical, structural and material features that rule the seismic behaviour of different typologies. A dataset of information about the 2017 Earthquakes in Mexico is used to discuss the suitability of a pre-established seismic vulnerability index approach. The compatibility between this approach and the information contained in the National Catalogue of Historical Monuments is the departure point for estimating levels of damage, which are then compared with those recorded in 2017. This implementation requires a series of adaptations and strategies, including the adjustment of the description of the parameters for the Mexican context and the development of a strategy for considering the uncertainty when the information is limited, offering an alternative more conservative output. Based on the results of the comparison between the levels of damage estimated using the vulnerability index approach adopted in this paper and those recorded in the aftermath of the 2017 Earthquakes in Mexico, it is plausible to confirm that this approach can be used in future seismic vulnerability assessment works in this region, provided that the limitations identified and discussed here are considered.