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

Xu, Susu; Zhang, Lin; Zhang, Pei; Noh, Hae Young

doi:10.3389/fbuil.2017.00022

Cited by 10 publications

(4 citation statements)

References 53 publications

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“…Xu et al [25] introduced a new method to monitor the buildings next railway to calculate the vibrations induce from moving trains and improved that method agreed with the field measurements by 93 % in positive rate and 80 % negative rate.…”

Section: Introductionmentioning

confidence: 98%

Mitigation of train-induced vibrations on nearby high-rise buildings by open or geofoam-filled trenches

Kontoni¹,

Farghaly²

2020

J. vibroeng.

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The vibrations induced by moving trains especially in close distances with high-rise buildings can be destructive. The high technology of wilding rails induced a high train velocity which is associated with high vibrations. The buildings near the railways suffer from the train-induced vibrations. In this paper, a 3D FEM model was constructed to study the train-induced vibrations on a nearby high-rise building (HRB), show its response and investigate the most suitable technique to mitigate the effect of the train-induced vibrations by an open trench or a geofoam-filled trench. Three trench parameters were investigated to enhance the mitigation performance, the distance from the trench to the HRB, the trench depth and the use of either open (empty) trench or geofoam-filled trench. The geofoam-filled trench technique improved the dynamic response of the structure. Thus, trenches filled with geofoam can be considered a protection technique for high-rise buildings constructed near moving trains.

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Section: Introductionmentioning

confidence: 98%

Mitigation of train-induced vibrations on nearby high-rise buildings by open or geofoam-filled trenches

Kontoni¹,

Farghaly²

2020

J. vibroeng.

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“…According to the application type, there are two main directions of technical diagnostics: monitoring and diagnostics of rotating machinery, [1][2][3][4] and structural health monitoring (SHM), which is defined as a process of implementing a damage detection and characterisation strategy for various engineering structures. [5][6][7][8] According to the used technique, diagnostic methods can be divided into: destructive technique when maximum load or fatigue test of samples are performed 9,10 ; and non-destructive technique when the state of the structure is evaluated without causing damage to it. 11,12 Due to this advantage, the nondestructive technique has become the most common technique for technical diagnostics nowadays.…”

Section: Introductionmentioning

confidence: 99%

“…Drive-by BHM is also referred to as the vehicle scanning method 5 or indirect structural health monitoring. [6][7][8][9][10] Vehicle vibrations contain information about the vehicle-bridge interaction (VBI) and thus can indirectly inform us of the dynamic characteristics of the bridge for damage diagnosis. 10,11 This is a scalable sensing approach with low-cost and low-maintenance requirements because each instrumented vehicle can efficiently monitor multiple bridges.…”

Section: Introductionmentioning

confidence: 99%

HierMUD: Hierarchical multi-task unsupervised domain adaptation between bridges for drive-by damage diagnosis

Liu

Bergés

et al. 2022

Structural Health Monitoring

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Monitoring bridges through vibration responses of drive-by vehicles enables efficient and low-cost bridge maintenance by allowing each vehicle to inspect multiple bridges and eliminating the needs for installing and maintaining sensors on every bridge. However, many existing drive-by monitoring approaches are based on supervised learning models that require massive labeled data from every bridge. It is expensive and time-consuming, if not impossible, to obtain these labeled data. Furthermore, directly applying a supervised learning model trained on one bridge to new bridges would result in low accuracy due to the shift between different bridges’ data distributions. Moreover, when we have multiple tasks (e.g., damage detection, localization, and quantification), the distribution shifts become more challenging than having only one task because different tasks have distinct distribution shifts and varying task difficulties. To this end, we introduce HierMUD, the first Hierarchical Multi-task Unsupervised Domain adaptation framework that transfers the damage diagnosis model learned from one bridge to a new bridge without requiring any labels from the new bridge. Specifically, our framework learns a hierarchical neural network model in an adversarial way to extract features that are informative to multiple tasks and invariant across multiple bridges. To match distributions over multiple tasks, we design a new loss function based on a newly derived generalization risk bound to adaptively assign higher weights to tasks with more shifted distributions. To learn multiple tasks with varying task difficulties, we split them into easy-to-learn and hard-to-learn tasks based on their distributions. Then, we formulate a feature hierarchy to utilize more learning resources to improve the hard-to-learn tasks’ performance. We evaluate our framework with experimental data from 2 bridges and 3 vehicles. We achieve up to 2X better performance than baseline methods, including average accuracy of 95% for damage detection, 93% for localization, and 0.38 lbs mean absolute error for quantification.

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“…In recent years, many data-driven earthquake-induced building damage diagnosis methods based on structural vibration sensing have been developed and received much attention [3,4]. By combining advanced machine learning techniques in a supervised fashion, these intelligent datadriven methods utilize historical structural response data and corresponding true damage state (label) to train building-specific models for structural damage diagnosis [5,6,7,8]. The data-driven methods can be extended to various application scenarios as they get rid of the requirements of prior knowledge about building structures.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Transfer Between Buildings for Post-Earthquake Damage Diagnosis Without Historical Data

Xu¹,

Noh²

2019

Structural Health Monitoring 2019

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Automated structural damage diagnosis after earthquakes is important for improving the efficiency of disaster response and rehabilitation. In conventional data-driven frameworks which use machine learning or statistical models, structural damage diagnosis models are often constructed using supervised learning. The supervised learning requires historical structural response data and corresponding damage states (i.e., labels) for each building to learn the building-specific damage diagnosis model. However, in post-earthquake scenarios, historical data with labels are often not available for many buildings in the affected area. This makes it difficult to construct a damage diagnosis model. Further, directly using the historical data from other buildings to construct a damage diagnosis model for the target building would lead to inaccurate results. This is because each building has unique physical properties and thus unique data distribution.To this end, we introduce a new framework to transfer the model learned from other buildings to diagnose structural damage states in the target building without any labels. This framework is based on an adversarial domain adaptation approach that extracts domain-invariant feature representations of data from different buildings. The feature extraction function is trained in an adversarial way, which ensures that the extracted feature distributions are robust to changes in structures while being predictive of the damage states. With the extracted domain-invariant feature representations, the data distributions become consistent across different buildings. We evaluate our framework on both numerical simulation and field data collected from multiple building structures. The results show up to 90.13% damage detection accuracy and 84.66% damage quantification accuracy on simulation data, and up to 100% damage detection accuracy and 69.93% damage quantification accuracy when transferring from numerical simulation data to real-world experimental data, which outperforms the state-of-the-art benchmark methods.

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

Cited by 10 publications

References 53 publications

Mitigation of train-induced vibrations on nearby high-rise buildings by open or geofoam-filled trenches

Mitigation of train-induced vibrations on nearby high-rise buildings by open or geofoam-filled trenches

HierMUD: Hierarchical multi-task unsupervised domain adaptation between bridges for drive-by damage diagnosis

Knowledge Transfer Between Buildings for Post-Earthquake Damage Diagnosis Without Historical Data

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