The development of Gaussian process regression for effective regional post‐earthquake building damage inference

• Published in: Computer-aided civil and infrastructure engineering Vol. 36; no. 3; pp. 264 - 288
• Main Authors: Sheibani, Mohamadreza, Ou, Ge
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.03.2021
• Subjects: Algorithms; Buildings; Clustering; Earthquake damage; Earthquakes; Gaussian process; Ground motion; Hypotheses; Impact damage; Machine learning; Probabilistic inference; Random sampling; Reconnaissance; Regional development; Statistical analysis; Structural damage
• ISSN: 1093-9687; 1467-8667
• DOI: 10.1111/mice.12630

Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.