Multiple-Surrogate Models for Probabilistic Performance Assessment of Wind-Excited Tall Buildings under Uncertainties

Abstract

Performance-based design (PBD) is becoming a benchmark approach for design and performance assessment of civil structures because it facilitates communication between stakeholders and design professionals with decision metrics such as economic losses, downtime, and probabilistic cost functions. However, to reliably estimate the response of a building, it is necessary to consider uncertainties in structural parameters and external load characteristics, which makes the probabilistic assessment phase of PBD time consuming and often impractical. In wind-excited tall buildings, this aspect is combined with the long duration of typical wind events (in the order of hours) and the time required to conduct a single numerical simulation, further increasing the computational demand of the uncertainty analysis. As a solution, this paper presents a novel multiple-surrogate models framework for the probabilistic assessment of wind-excited tall buildings. In the proposed framework, the structural system is decomposed into a set of subsystems and multiple-surrogate models are used to map the uncertain building dynamic properties and wind load characteristics to its structural response, expressed in terms of peak floor acceleration and maximum story drift ratio. The proposed approach is demonstrated on a 40-story building, located in Boston, Massachusetts, and exposed to synthetic wind load time histories. The accuracy of the multiple-surrogate models to estimate the structural response of the building is first evaluated. Then, an application of the multiple-surrogate models to the cost assessment of the case study building is presented to demonstrate the capabilities of the proposed approach to probabilistically assess the performance of a structure and its potential integration in PBD. Results show that using the multiple-surrogate models framework represents a viable solution to handle the large dimensional probabilistic assessment of wind-excited tall buildings under uncertainties in dynamic properties and wind load characteristics.