Structural Health Monitoring (SHM) systems have been extensively implemented to deliver data support and safeguard structural safety in structural integrity management context. SHM relies on data that can be noisy in large amounts or scarce. Little work has been done on SHM data quality (DQ). Therefore, this article suggests SHM DQ indicators and recommends deterministic and probabilistic SHM DQ metrics to address uncertainties. This will allow better decision-making for structural integrity management.Therefore, first, the literature on DQ indicators and measures is thoroughly examined. Second, and for the first time, necessary SHM DQ indicators are identified, and their definitions are tailored.Then SHM deterministic simplified DQ metrics are suggested, and more essentially probabilistic metrics are offered to address the embedded uncertainties and to account for the data flow.A generic example of a bridge with permanent and occasional monitoring systems is provided. It helps to better understand the influence of SHM data flow on the choice of DQ metrics and allocated probability distribution functions. Finally, a real case example is provided to test the feasibility of the suggested method within a realistic context.

Byblos, a Lebanese City, a UNESCO world heritage and one of the oldest continuously inhabited cities, is a member of the 100 Resilient Cities project. To help mitigate its stresses and chocks, Byblos is implementing its resilience strategy enclosing the five pillars: Connected, Resourceful, Peaceful, Cultural, and Thriving. Since the earthquake threat is not selected in this strategy, this study proposes to complement the 100 RC efforts and aims to urge Byblos Municipality authorities and governmental institutions to act based on concrete results offered, to provide emergency, prevention and recovery plans for the city and to enhance the civil protection. Therefore, first, we present the Byblos resilience strategy, identifying the city stresses and shocks, discussing its seismic threat and presenting complementary measures. Second, we model structural building damage, casualties, and buildings direct economic damage, including structural and nonstructural damage. Finally, results and recommendations are offered for an improved resilience strategy.