Advancements in Full-Scale Monitoring Hardware for Improved Modeling of Tall Buildings: A System Behavior Perspective

Doctoral Dissertation


The structural design of tall, slender buildings requires the use of analytical and scaled models, which are influenced by the myriad of simplifying assumptions used to create them. While these models are used to predict the structure’s performance and thereby dictate the structural design of the building, the accuracy of the modeling techniques used in the design of tall buildings are rarely every verified despite the tremendous life safety and economic implications of these signature structures. Such a lack of validation leads to over-conservative assumptions and presents a barrier to the formalization of performance-based design guidelines.

The scale, cost and complexity of tall buildings prohibits the use of full-scale validation tests employed by other engineering disciplines. This leaves full-scale monitoring as the only feedback mechanism to quantify the accuracy of the scaled and analytical models used to predict the performance of tall buildings in the design stage. This research responds by first presenting advances to the hardware used to capture the movement of tall buildings. The dissertation details the development and validation of a rapidly re-deployable accelerometer module. These modules will enable the collection of full-scale data from a wide variety of tall buildings employing different load transfer mechanisms through a community-based framework. Second, the use of tiltmeters to capture the low-frequency components of a structure’s movement is developed through both laboratory testing and in-situ full-scale monitoring. The research then shifts towards designer-facing objectives. First, new global and local behavioral descriptors are developed to quantify the degree of cantilever action for the overall structure and for discrete locations along the structure’s height. Second, the research explores how this hardware can be used in hybrid installations with accelerometers to determine total displacement algorithm tied to this nuanced understanding of in-situ behavior. This enables the capture of both the high-frequency and low-frequency components of a structure’s total displacement, while also using the respective outputs to quantify different deformation mechanisms such as the degree of lateral sway and overturning. Third, the global behavioral descriptor is used to create a predictive damping model, demonstrating the relationship between a structure’s degree of cantilever action and its energy dissipation capacity. Finally, the dissertation utilizes both the global and local behavioral descriptors in a new framework for parameter selection as a part of the model updating process for a tall building’s finite element model. This analysis also investigates the inevitable impact of limited sensor density in full-scale monitoring campaigns on the results of the model updating process.

Over the course of the dissertation, several changes to conventional wisdom surrounding full-scale monitoring of tall buildings are presented. First, installation locations for tiltmeter sensing technology should focus on the lower elevations of the structure. Second, there exists a trade-off between the utilization of more efficient axial load paths in the structure (higher degree of cantilever action) and the structure’s ability to dissipate energy. Finally, when utilizing full-scale monitoring data for model updating applications, sensor arrays should include sensors towards the base of the structure rather than focusing solely on higher elevations of the building.


Attribute NameValues
Author Andrew Bartolini
Contributor Tracy Kijewski-Correa, Research Director
Contributor Ahsan Kareem, Committee Member
Contributor Alexandros Taflanidis, Committee Member
Contributor Dae Kun Kwon, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Civil and Environmental Engineering and Earth Sciences
Degree Name Doctor of Philosophy
Banner Code

Defense Date
  • 2018-12-07

Submission Date 2019-01-22
Record Visibility and Access Public
Content License
  • All rights reserved

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