A Bayesian Monte Carlo framework for risk-targeted seismic separation gaps in adjacent fixed-base and base-isolated buildings
摘要
Seismic pounding between adjacent buildings remains a critical concern, particularly when base isolation is employed and near-fault (NF) ground motions dominate the response. This study develops a Bayesian probabilistic framework to determine minimum separation gaps for adjacent reinforced concrete buildings with fixed-base (FB) and base-isolated (BI) supports. Two Reinforced Concrete (RC) (10-storey and 8-storey) were analyzed in four adjacency configurations under 44 far-field (FF) and NF ground motions, scaled to PGAs from 0.1 to 1.2 g. More than 12,000 nonlinear time-history analyses with gaps ranging from 25 to 500 mm detected pounding via nonlinear gap elements. Pounding probabilities were quantified using Bayesian updating with Jeffreys prior and Monte Carlo bootstrapping to derive posterior medians, 95% confidence intervals, and non-achievement rates. Results show that far-field (FF) excitations typically demand gaps of less than 150 mm, while near-fault (NF) motions require gaps exceeding 300 mm, especially for base-isolated (BI) configurations. BI–FB pairs consistently exhibit the highest risk, with confidence intervals (CIs) exceeding 500 mm at high PGAs, and non-achievement rates up to 1.7% for NF-FS 10FB-8BI. This highlights that uniform code-prescribed separations (e.g., 250 mm) underestimate NF-FS demands by up to 40%, emphasizing the need for probabilistic, site- and system-specific gap design to ensure robust performance, particularly near active fault zones.