In-service video-vibration monitoring for identification of walking patterns in an office floor

Footfall-induced vibrations in office floors can create significant problems for the occupants and facility owners. Such vibrations cannot be modeled by traditional load models since the vibration response of a floor depends on nondeterministic factors such as walking paths, pacing rates, stride lengths, busyness of the floor, interactions among the occupants, etc. In this work, a novel simultaneous video-vibration monitoring system has been developed in order to study the complex walking patterns in office floors. This system is able to capture occupants' movement on the monitored floor using cameras, extract their walking trajectories, and measure the vibration levels across the floor using wireless sensing units. The proposed system was installed and tested in an open workplace. The resulting trajectories were statistically analyzed to obtain useful information that reflect the actual walking patterns of the occupants. The output of the video monitoring exercise can be used in future studies to train a data-driven crowd model capable of simulating realistic scenarios of people movement on the floor. Copyright (2018) by International Institute of Acoustics & Vibration.All rights reserved.The authors would like to thank Qatar Rail for providing an access to the office floor monitored in this work. The financial support for this research was provided by Qatar National Research Fund, QNRF (a member of Qatar Foundation) via the National Priorities Research Program (NPRP), Project Number: NPRP 8-836-2-353. The statements made herein are solely the responsibility of the authors

An Overview of Deep Learning Methods Used in Vibration-Based Damage Detection in Civil Engineering

This paper presents a brief overview of vibration-based damage identification studies based on Deep Learning (DL) in civil engineering structures. The presence, type, size, and propagation of structural damage on civil infrastructure have always been a topic of research. In the last couple of decades, there has been a significant shift in the damage detection paradigm when the advancements in sensing and computing technologies met with the ever-expanding use of artificial neural network algorithms. Machine-Learning (ML) tools enabled researchers to implement more feasible and faster tools in damage detection applications. When an artificial neural network has more than three layers, it is typically considered as a ?deep? learning network. Being an important accomplishment of the ML era, DL tools enable complex systems which are made of several layers to learn implementations of data with outstanding categorization and compartmentalization capability. In fact, with proper training, a DL tool can operate directly with the unprocessed raw data and help the algorithm produce output data. Competitive capabilities like this led DL algorithms perform very well in complicated problems by dividing a relatively large problem into much smaller and more manageable portions. Specifically for damage identification and localization on civil infrastructure, Convolutional Neural Networks (CNNs) and Unsupervised Pretrained Networks (UPNs) are the known DL tools published in the literature. This paper presents an overview of these studies.Scopu

Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art

This paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric applications of ML accompanied with analytical and/or experimental studies. While the ML tools help the system learn from the data fed into, the computer enhances the task with the learned information without any programming on how to process the relevant data. As such, the performance level of ML-based damage identification methodologies depends on the feature extraction and classification steps, especially on the classifier choices for which the characteristic nature of the acceleration signals is recorded in a feasible way. Yet, there are several issues to be discussed about the existing ML procedures for both parametric and nonparametric applications, which are presented in this paper.Scopu

Ground-borne vibration investigation by model-ling the tunnel-soil interaction using a finite element package

In 2013, Qatar Rail announced major rail projects that include an urban rail network for the city of Doha with lines running on the surface and underground which are expected to be in operation by 2020. Railway systems are known as attractive means of transportation that can be implemented to solve traffic problems in urban areas. However, they are associated with noise and vibration that cause disturbance, not only to passengers, but also to occupants of nearby buildings. The purpose of this paper is to contribute to the literature by developing an understanding on the dynamic tunnel-soil interaction and the propagation of waves in the ground. In this work, a Finite Element model has been developed that accounts for the specific details of tunnel and ground by a commercial FE software, Abaqus 6-14. The software is used first to model a single isolated tunnel in 2D (plane strain) with point load (corresponding to line load for the 3D case). Then, the software is used to model 3D tunnel under a line load. The results for the 2D and 3D models were found to be matching each other as well as with results from other models reported in the literature. A 2D plane strain model is then developed for a tunnel embedded in a half space and a good agreement was observed when comparing the results with those reported in the literature. Finally, the FE package was used to explore the effect of tunnel shape on the propagation of ground-borne vibration. Several FE models of circular, oval, square, and rectangular tunnels embedded in a multi-layered medium representing Qatar soil were created and analyzed. The numerical results revealed that tunnel shape influences the dynamic tunnel-soil interaction. 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling. All rights reserved.The financial support for this research was provided by Qatar university Grant, Project Number: QUUG-CENG-CAE-17/18-2. The statements made herein are solely the responsibility of the authors.Scopu

Novel Framework for Vibration Serviceability Assessment of Stadium Grandstands Considering Durations of Vibrations

Annoying vibrations in grandstand structures have been receiving more attention due to the increasing slenderness of the architectural components and the complexity of the crowd loading for engineers. The vibration serviceability checks under these conditions become a challenge in the design and operation stages. Regarding human comfort, excessive vibrations due to occupant activities may affect comfort and/or cause panic, especially for passive occupants who do not participate in generating excitations. Although durations of excessive vibrations have been considered as one of the most important factors affecting occupant comfort, incorporating the vibration duration in the occupant comfort analysis has not been addressed yet. In addition, the currently available approaches using raw acceleration, weighted RMS acceleration, vibration dose values (VDV), and so on may not always be sufficient for serviceability assessment due to the lack of guided procedure for calculating the integration time and implementing the duration of vibration into the process. Therefore this study proposes a new parameter and framework for assessing human comfort which incorporates the duration of vibration with conventional data processing. The aim is to better examine vibration levels and the corresponding occupant response focusing on grandstand structures. A new parameter, the area of RMS (ARMS), is introduced using the running RMS values of acceleration weighted by the frequency weighting functions. Furthermore, perception ranges for human comfort levels based on the proposed parameter are presented. The experimental study reveals that the proposed framework can successfully address the impact of duration time on determining the levels of vibrations and comfort using the proposed parameter. 2017 American Society of Civil Engineers.The financial support for this research was provided by Qatar National Research Fund (QNRF; a member of the Qatar Foundation) via the National Priorities Research Program (NPRP), Project Number NPRP 6-526-2-218. The statements made herein are solely the responsibility of the authors.Scopu

Structural health monitoring with self-organizing maps and artificial neural networks

The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently developed a nonparametric structural damage detection algorithm for extracting damage indices from the ambient vibration response of a structure. The algorithm is based on self-organizing maps with a multilayer feedforward pattern recognition neural network. After the training of the self-organizing maps, the algorithm was tested analytically under various damage scenarios based on stiffness reduction of beam members and boundary condition changes of a grid structure. The results indicated that proposed algorithm can successfully locate and quantify damage on the structure.Scopu

Convolutional neural networks for real-time and wireless damage detection

Structural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable computational power which makes the utilization of centralized techniques relatively infeasible for wireless sensor networks. In this paper, the authors present a novel Wireless Sensor Network (WSN) based on One Dimensional Convolutional Neural Networks (1D CNNs) for real-time and wireless structural health monitoring (SHM). In this method, each CNN is assigned to its local sensor data only and a corresponding 1D CNN is trained for each sensor unit without any synchronization or data transmission. This results in a decentralized system for structural damage detection under ambient environment. The performance of this method is tested and validated on a steel grid laboratory structure.Scopu

Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications

Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the application of the classical methods in real-time structural health monitoring applications. Furthermore, in many cases, the hand-crafted features extracted by the classical methods fail to accurately characterize the acquired signal, resulting in poor classification performance. In an attempt to overcome these issues, this paper presents a novel, fast and accurate structural damage detection and localization system utilizing one dimensional convolutional neural networks (CNNs) arguably for the first time in SHM applications. The proposed method is capable of extracting optimal damage-sensitive features automatically from the raw acceleration signals, allowing it to be used for real-time damage detection. This paper presents the preliminary experiments conducted to verify the proposed CNN-based approach.Scopu

Vibration suppression in metastructures using zigzag inserts optimized by genetic algorithms

Metastructures are known to provide considerable vibration attenuation for mechanical systems. With the optimization of the internal geometry of metastructures, the suppression performance of the host structure increases. While the zigzag inserts have been shown to be efficient for vibration attenuation, the geometric properties of the inserts affect the suppression performance in a complex manner when attached to the host structure. This paper presents a genetic algorithm based optimization study conducted to come up with the most efficient geometric properties of the zigzag inserts. The inserts studied in this paper are simply cantilever zigzag structures with a mass attached to the unsupported tips. Numerical simulations are run to show the efficiency of the optimization process.Scopu