Development of fragility functions as a damage classification/prediction method for steel moment‐resisting frames using a wavelet‐based damage sensitive feature

Noh, Hae Young; Lignos, Dimitrios G.; Nair, K. Krishnan; Kiremidjian, Anne S.

doi:10.1002/eqe.1151

Cited by 59 publications

(42 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[24][25][26]42 In recent years, people start to explore the method to utilize structural vibration for human monitoring. 2,9,19,21,27,29,33 Structural vibration sensing has been used for various indoor occupant monitoring purposes, 9,19 including person localization, 2, 21, 33 occupancy estimation, 27 and occupant identification.…”

Section: Related Workmentioning

confidence: 99%

Occupant traffic estimation through structural vibration sensing

et al. 2016

Self Cite

View full text Add to dashboard Cite

The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of ±0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

show abstract

Section: Related Workmentioning

confidence: 99%

Occupant traffic estimation through structural vibration sensing

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…This compactness results in easier event detection because fewer features can represent the event of interest. Wavelet analysis has been widely applied as a promising tool to extract structural dynamic characteristics in structural health monitoring and other related fields (Chang, 1999;Hera and Hou, 2004;Taha et al, 2006;Noh et al, , 2011Noh et al, , 2012Mirshekari et al, 2015Mirshekari et al, , 2016aPan et al, 2015aPan et al, ,b, 2016Lam et al, 2016). Similarly, we use wavelet to extract structural dynamic characteristics that change with train activities.…”

Section: Extract Wavelet-based Featuresmentioning

confidence: 99%

An Information-Theoretic Approach for Indirect Train Traffic Monitoring Using Building Vibration

Zhang

et al. 2017

Front. Built Environ.

Self Cite

View full text Add to dashboard Cite

This paper introduces an indirect train traffic monitoring method to detect and infer real-time train events based on the vibration response of a nearby building. Monitoring and characterizing traffic events are important for cities to improve the efficiency of transportation systems (e.g., train passing, heavy trucks, and traffic). Most prior work falls into two categories: (1) methods that require intensive labor to manually record events or (2) systems that require deployment of dedicated sensors. These approaches are difficult and costly to execute and maintain. In addition, most prior work uses dedicated sensors designed for a single purpose, resulting in deployment of multiple sensor systems. This further increases costs. Meanwhile, with the increasing demands of structural health monitoring, many vibration sensors are being deployed in commercial buildings. Traffic events create ground vibration that propagates to nearby building structures inducing noisy vibration responses. We present an information-theoretic method for train event monitoring using commonly existing vibration sensors deployed for building health monitoring. The key idea is to represent the wave propagation in a building induced by train traffic as information conveyed in noisy measurement signals. Our technique first uses wavelet analysis to detect train events. Then, by analyzing information exchange patterns of building vibration signals, we infer the category of the events (i.e., southbound or northbound train). Our algorithm is evaluated with an 11-story building where trains pass by frequently. The results show that the method can robustly achieve a train event detection accuracy of up to a 93% true positive rate and an 80% true negative rate. For direction categorization, compared with the traditional signal processing method, our information-theoretic approach reduces categorization error from 32.1 to 12.1%, which is a 2.5× improvement.

show abstract

“…Binning of the ice rate data would result in the conditional probability being strongly influenced by the bin size. The conditional probability of the ice rate equal to or exceeding a specific level of the ice rate given a value of the wind direction is provided by Equation (8) (Noh et al 2011). (8) where (wdir j ) is the conditional probability of the ice rate given the jth wind direction (wdir j ), I(ir m ≥ IR i ) is an indicator function which is 1 if ir m ≥ IR i and 0 if ir m < IR i , K j (wdir m ) or K j (wdir n ) is the kernel for the mth and nth value of the wind direction and ir m is the mth value of the ice accretion given in kg/m/h.…”

Section: Ice Accumulation Ratesmentioning

confidence: 99%