Effect of Sensor Error on the Assessment of Seismic Building Damage

TL;DR

This paper investigates how sensor errors affect the accuracy of seismic building damage assessments using IoT sensors, proposing a ZUPT technique to improve displacement estimates and analyzing the impact on damage classification.

Contribution

It introduces a zero velocity update method to reduce displacement errors and evaluates how sensor inaccuracies influence damage classification accuracy.

Findings

ZUPT technique reduces displacement estimation errors.

Sensor errors significantly impact damage classification accuracy.

Modeling sensor error helps improve assessment reliability.

Abstract

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an internet of things (IoT) network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to government standards. With this information, the building inventory can be classified into immediate occupancy (IO), life safety (LS) or collapse prevention (CP) categories. In this work, we propose a zero velocity update (ZUPT) technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio (IDR) distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy.