Health monitoring and field‐testing have been two emerging technologies for investigating the real‐world behaviors of high‐rise buildings. The five primary motivations of the two monitoring‐oriented methods are first presented. The four fundamental steps of the data‐driven process are followingly discussed. Then, the state‐of‐the‐art structural health monitoring systems on four representative super‐tall buildings and the relevant data analysis methodologies are reviewed, with the summary of the corresponding observations from data interpretation. The recent practice on seismic monitoring and data‐informed structural evaluation are discussed. Especially, the tradeoff problem on parametric identification for data‐driven modeling of real‐life structures is presented. Finally, the future of health monitoring and field‐testing of high‐rise buildings is presented with several potential issues highlighted.

A methodology of data-driven damage state quantification with a probability estimation for structural hysteresis of RC columns is presented in this paper. The knowledge learned from a large-volume structural behavior database is fully considered for developing monitoringoriented damage indicators, with the established relationship between data-driven damage prediction and physics-based damage state evaluation. In the present study, a database of the hysteresis behavior of 1015 RC columns with different design parameters is first generated by adopting OpenSees, with categorization according to the primary design parameter of the axial load ratio. Four limit states of seismic performance with the corresponding values of the proposed damage index are calculated to generate an informative mapping between critical damage states and damage index values. By fitting probabilistic models on the grouped data of damage indices, the exceeding probabilities of damage states corresponding to damage index values can be obtained. Illustrative examples of full-scale RC columns with cyclic loading and shaking table tests are adopted to illustrate the proposed performance-based damage evaluation process.

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This paper introduces a probability-based damage state evaluation methodology for shear walls deriving from a data-driven calculation. A previously proposed damage quantification index, formulated in the time domain, which is capable of tracking damage progression based on the availability of structural monitoring data, is adopted here for the quantification of structural hysteresis damages. The probability of the structure lying in a specific damage state is determined on the basis of the derived damage index and the limit state definitions, yielding valuable information for postearthquake decisions. In this study, a database of the hysteretic behavior of 1,000 shear walls, considering different structural parameters, is generated utilizing OpenSees. Four limit states of seismic performance are defined based on material properties and in relation to the simulated stress and strain data. Accordingly, the exceeding probabilities of damage can be estimated by fitting statistical models to the damage index values grouped in terms of the axial load ratio. Followingly, an informative mapping, between the monitoring-derived damage index and postearthquake damage levels, is established considering structural uncertainty. Illustrative examples, including two shear walls subjected to cyclic loading and a seven-story shear wall slice subjected to a shaking table test, are investigated to show the capability and feasibility of the proposed performance evaluation method on damage state evaluation.