Large-scale high-resolution probabilistic seismic and multi-hazard risk assessments depend critically on how exposure is represented and how appropriately vulnerability functions are assigned to heterogeneous building stocks. While hazard modelling has achieved a high degree of harmonisation and vulnerability modelling has achieved a high level of computational maturity, the exposure\u2013vulnerability interface remains manual, typology-driven and difficult to scale consistently. In Barcelona and across Catalonia, vulnerability functions derived from single buildings or limited samples have frequently been extrapolated across portfolios using coarse classifications. When hazard, exposure and damage-to-loss models are held constant, this practice produces substantial spreads in portfolio loss metrics. It first quantifies that variability through a controlled vulnerability-only sensitivity analysis for Barcelona. Using a consistent probabilistic seismic hazard backbone and unified damage-to-loss mapping, published vulnerability-function sets are interchanged while all other components remain fixed. Results show that Average Annual Loss (AAL) varies from approximately \u20ac25.6 million to \u20ac348.0 million depending solely on the selected vulnerability dataset, demonstrating order-of-magnitude sensitivity attributable to function assignment. A focused pilot in the Eixample district confirms that detailed, manual reassignment (e.g., corner versus centre block positions) improves local realism but does not scale to city- or regional-level portfolios.<\/p>\n
To address this structural limitation, the second step moves from ad-hoc assignment toward repeatable and transparent exposure. The thesis develops a geometry-driven exposure structuring workflow through Building Footprint Analysis, Classification and Grouping (BFA\/BFC\/BFG). High-resolution cadastral building footprints from Catalonia are used to extract dimensions and are transformed into interpretable, reproducible plan-shape classes using previously computed shape metrics to define sides-based classes. Results from comparative analysis of several shape metrics show that simple compactness measures agree on regular rectangles and squares but blur important differences for elongated, irregular and opening-based plans. This reveals redundancy among scalar compactness indices and limited discriminatory power for articulated and opening-based geometries, highlighting the need for an alternative capable of preserving fine-grained distinctions relevant to structural behaviour.<\/p>\n
The Building Footprint Analysis, Recognition and Classification framework (BFARC-YOLO) builds on this basis to enable scalable AI-augmented exposure classification. Approximately 1.8 million building footprints are rendered as oriented silhouettes and progressively trained using ~0.34% labelled samples. The final model achieves mean average precision (mAP@0.5:0.95) \u2248 0.96 across 44 fine-grained classes. GPU optimisation reduces regional inference time from over 490 hours (CPU baseline) to ~72 hours, demonstrating operational feasibility. Interoperability with the GEM taxonomy enables direct integration into established risk platforms and a lightweight web interface supports auditable batch inference.<\/p>\n
Rather than developing novel fragility functions, this thesis offers modelling infrastructure. It creates a replicable bridge from cadastral geometry to vulnerability-aligned exposure classification, minimising subjectivity in vulnerability assignment and allowing for scalable, transparent portfolio risk modelling. By reframing exposure as a structured, learning-enabled modelling layer, the study lays the groundwork for future fragility calibration, and next-generation multi-hazard risk assessments for dense and heterogeneous cities, regions, countries and the global scale.<\/p>\n
PhD Advisor:<\/strong><\/p>\n ABSTRACT Large-scale high-resolution probabilistic seismic and multi-hazard risk assessments depend critically on how exposure is represented and how appropriately vulnerability functions are assigned to heterogeneous building stocks. While hazard modelling has achieved a high degree of harmonisation and vulnerability modelling has achieved a high level of computational maturity, the exposure\u2013vulnerability interface remains manual, typology-driven and […]<\/p>\n","protected":false},"author":9,"featured_media":259553,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","slim_seo":{"title":"Defensa de tesi doctoral: \u201cUn marc escalable augmentat amb IA per a l\u2019avaluaci\u00f3 d\u2019alta resoluci\u00f3 de l\u2019exposici\u00f3 i del risc s\u00edsmic\u201d de Samar Momin - CIMNE","description":"ABSTRACT Large-scale high-resolution probabilistic seismic and multi-hazard risk assessments depend critically on how exposure is represented and how appropriat"},"footnotes":""},"categories":[244],"tags":[],"class_list":["post-259532","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-defensa-de-tesi"],"acf":{"date":"20260706","start_date":null,"finish_date":null,"time":"16:00:00","place":"Sala 002, Edifici C1, Campus Nord de la UPC (Barcelona)","attendance":"In person","online_streaming":"","registration":""},"_links":{"self":[{"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/posts\/259532","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/comments?post=259532"}],"version-history":[{"count":3,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/posts\/259532\/revisions"}],"predecessor-version":[{"id":259554,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/posts\/259532\/revisions\/259554"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/media\/259553"}],"wp:attachment":[{"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/media?parent=259532"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/categories?post=259532"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cimne.com\/ca\/wp-json\/wp\/v2\/tags?post=259532"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
\n
CANDIDATE<\/strong><\/h5>\n
![\"Samar](\"https:\/\/cimne.com\/wp-content\/uploads\/2026\/07\/Samar_Momin_200x200.jpg\")
Samar Momin is a PhD candidate in the Disaster Risk and Resilience<\/a> research group, part of the Structural and Particle Mechanics<\/a> research cluster. His research focuses on developing YOLO\u2011based computer vision models for disaster risk reduction, with interests spanning earthquake engineering, hazard modelling, and multi\u2011risk assessment. He also brings experience as an independent consultant on hazard and risk assessment projects.[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"