Deep learning for image-based structural element damage assessments in post-earthquake buildings: a systematic review

Abstract Deep learning (DL) is increasingly used to support image-based post-earthquake damage assessment of local structural elements in buildings, yet the existing literature remains inconsistent in task formulation, dataset design, model evaluation, performance enhancement and field deployment readiness. This paper presents a systematic literature review conducted in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) framework, covering 46 primary studies published between 2015 and 2025 on DL-based structural element level post-earthquake damage assessment. Using a transparent protocol for study identification, screening, eligibility assessment, quality appraisal, and data extraction, the review synthesises the literature through a task-aware taxonomy comprising four DL configurations: damage classification, damage segmentation, damage detection and estimation of quantitative damage severity. The review examines how these task configurations relate to damage assessment targets, local structural elements, DL architectures, data acquisition methods, data modalities, data quality, preprocessing pipelines, evaluation protocols, and model enhancement strategies, including transfer learning, data augmentation, synthetic data generation and multimodal fusion. The synthesis reveals key gaps in the under representation of several structural elements and failure mechanisms, limited use of multimodal and realistic field data, inconsistent task specific evaluation protocols, weak evidence of model generalisation to unseen data, and the absence of structurally grounded damage assessment criteria. To improve dependable field deployment, the review highlights the need for data efficient models, stronger multimodal learning, stricter evaluation on unseen field data, and integration with real-time engineer facing assessment workflows, eventually supporting development of disaster-resilient build environments.