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  • 1.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps2016In: Journal of Architectural Engineering, ISSN 1076-0431, E-ISSN 1943-5568, Vol. 22, no 2Article in journal (Refereed)
    Abstract [en]

    This study presentes a new nonparametric structural damage detection algorithm that integrates self-organizing maps with a pattern-recognition neural network to quantify and locate structural damage. In this algorithm, self-organizing maps are used to extract a number of damage indices from the ambient vibration response of the monitored structure. The presented study is unique because it demonstrates the development of a nonparametric vibration-based damage detection algorithm that utilizes self-organizing maps to extract meaningful damage indices from ambient vibration signals in the time domain. The ability of the algorithm to identify damage was demonstrated analytically using a finite-element model of a hot-rolled steel grid structure. The algorithm successfully located the structural damage under several damage cases, including damage resulting from local stiffness loss in members and damage resulting from changes in boundary conditions. A sensitivity study was also conducted to evaluate the effects of noise on the computed damage indices. The algorithm was proved to be successful even when the signals are noise-contaminated.

  • 2.
    Abdeljaber, Osama
    et al.
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Avci, Onur
    Do, Ngoan Tien
    Gul, Mustafa
    Celik, Ozan
    Catbas, Necati
    Quantification of Structural Damage with Self-Organizing Maps2016In: Structural Health Monitoring, Damage Detection & Mechatronics, 2016, Vol. 7Conference paper (Other academic)
    Abstract [en]

    One of the main tasks in structural health monitoring process is to create reliable algorithms that are capable of translating the measured response into meaningful information reflecting the actual condition of the monitored structure. The authors have recently introduced a novel unsupervised vibration-based damage detection algorithm that utilizes self-organizing maps to quantify structural damage and assess the overall condition of structures. Previously, this algorithm had been tested using the experimental data of Phase II Experimental Benchmark Problem of Structural Health Monitoring, introduced by the IASC (International Association for Structural Control) and ASCE (American Society of Civil Engineers). In this paper, the ability of this algorithm to quantify structural damage is tested analytically using an experimentally validated finite element model of a laboratory structure constructed at Qatar University.

  • 3.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Inman, Daniel
    University of Michigan, USA.
    Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks2016In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 363, p. 33-53Article in journal (Refereed)
    Abstract [en]

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

  • 4.
    Abdeljaber, Osama
    et al.
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Avci, Onur
    Inman, Daniel
    Genetic algorithm use for internally resonating lattice optimization: case of a beam-like metastructure2016In: Dynamics of Civil Structures, 2016, Vol. 2Conference paper (Other academic)
    Abstract [en]

    Metamaterial inspired structures, or metastructures, are structural members that incorporate periodic or non-periodic inserts. Recently, a new class of metastructures has been introduced which feature chiral lattice inserts. It was found that this type of inserts has frequency bandgaps which can be tuned by altering the geometry of the chiral lattice. Previous studies have shown that inserting non-periodic chiral lattices inside a beam-like structure results in efficient vibration attenuation at low frequencies. In the study presented in this paper, a genetic algorithm based optimization technique is developed to automatically generate chiral lattices which are tuned to suppress vibration in a flexible beam-like structure. Several parameters are incorporated in the optimization process such as the radius of circular nodes and characteristic angle as well as the spacing and distribution of circular inserts. The efficiency of the …

  • 5.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Inman, Daniel
    University of Michigan, USA.
    Optimization of chiral lattice based metastructures for broadband vibration suppression using genetic algorithms2016In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 369, p. 50-62Article in journal (Refereed)
    Abstract [en]

    One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.

  • 6.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Kiranyaz, Serkan
    Qatar University, Qatar.
    Boashash, Boualem
    Qatar University, Qatar; The University of Queensland, Herston, Australia.
    Sodano, Henry
    University of Michigan, USA.
    Inman, Daniel
    University of Michigan, USA.
    1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data2018In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 275, p. 1308-1317Article in journal (Refereed)
    Abstract [en]

    Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract “hand-crafted” features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.

  • 7.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Kiranyaz, Serkan
    Qatar University, Qatar.
    Gabbouj, Moncef
    Tampere University of Technology, Finland.
    Inman, Daniel
    University of Michigan, USA.
    Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks2017In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 388, p. 154-170Article in journal (Refereed)
    Abstract [en]

    Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

  • 8.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Kiranyaz, Serkan
    Qatar University, Qatar.
    Inman, Daniel
    University of Michigan, USA.
    Optimization of linear zigzag insert metastructures for low-frequency vibration attenuation using genetic algorithms2017In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 84, no Part A, p. 625-641Article in journal (Refereed)
    Abstract [en]

    Vibration suppression remains a crucial issue in the design of structures and machines. Recent studies have shown that with the use of metamaterial inspired structures (or metastructures), considerable vibration attenuation can be achieved. Optimization of the internal geometry of metastructures maximizes the suppression performance. Zigzag inserts have been reported to be efficient for vibration attenuation. It has also been reported that the geometric parameters of the inserts affect the vibration suppression performance in a complex manner. In an attempt to find out the most efficient parameters, an optimization study has been conducted on the linear zigzag inserts and is presented here. The research reported in this paper aims at developing an automated method for determining the geometry of zigzag inserts through optimization. This genetic algorithm based optimization process searches for optimal zigzag designs which are properly tuned to suppress vibrations when inserted in a specific host structure (cantilever beam). The inserts adopted in this study consist of a cantilever zigzag structure with a mass attached to its unsupported tip. Numerical simulations are carried out to demonstrate the efficiency of the proposed zigzag optimization approach.

  • 9.
    Abdeljaber, Osama
    et al.
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Hussein, Mohammed
    Avci, Onur
    Davis, Brad
    Reynolds, Paul
    A novel video-vibration monitoring system for walking pattern identification on floors2020In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339Article in journal (Refereed)
    Abstract [en]

    Walking-induced loads on office floors can generate unwanted vibrations. The current multi-person loading models are limited since they do not take into account nondeterministic factors such as pacing rates, walking paths, obstacles in walking paths, busyness of floors, stride lengths, and interactions among the occupants. This study proposes a novel video-vibration monitoring system to investigate the complex human walking patterns on floors. The system is capable of capturing occupant movements on the floor with cameras, and extracting walking trajectories using image processing techniques. To demonstrate its capabilities, the system was installed on a real office floor and resulting trajectories were statistically analyzed to identify the actual walking patterns, paths, pacing rates, and busyness of the floor with respect to time. The correlation between the vibration levels measured by the wireless sensors and the trajectories extracted from the video recordings were also investigated. The results showed that the proposed video-vibration monitoring system has strong potential to be used in training data-driven crowd models, which can be used in future studies to generate realistic multi-person loading scenarios.

  • 10.
    Abdeljaber, Osama
    et al.
    Qatar University, Qatar.
    Sassi, Sadok
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Kiranyaz, Serkan
    Qatar University, Qatar.
    Ibrahim, Abdelrahman
    Qatar University, Qatar.
    Gabbouj, Moncef
    Tampereen University of Technology, Finland.
    Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring2019In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 66, no 10, p. 8136-8147Article in journal (Refereed)
    Abstract [en]

    This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.

  • 11.
    Abdeljaber, Osama
    et al.
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Younis, Adel
    Qatar University.
    Avci, Onur
    Qatar University.
    Gul, Mustafa
    Ozan, Celik
    Zhang, H
    Dynamic Testing of a Laboratory Stadium Structure2016Conference paper (Other academic)
    Abstract [en]

    Studies with large physical models are a vital link between the theoretical work and field applications provided that these models are designed to represent real structures where various types and levels of uncertainties can be incorporated. While comprehensive analytical and laboratory joint studies are ongoing at Qatar University, University of Central Florida and University of Alberta, this paper presents the initial findings of dynamic testing at Qatar University. A laboratory stadium structure (grandstand simulator) has been constructed at Qatar University. Capable of housing thirty spectators, Qatar University grandstand simulator is arguably the largest laboratory stadium in the world. The structure is designed in a way that several different structural configurations can be tested in laboratory conditions to enable researchers to test newly developed damage detection algorithms. The study presented in this paper covers the finite element modeling and modal testing of the test structure.

  • 12. Avci, Avci
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Kiranyaz, Serkan
    Inman, Daniel
    Vibration suppression in metastructures using zigzag inserts optimized by genetic algorithms2017In: Shock & Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics & Optics, 2017, Vol. 7Conference paper (Other academic)
    Abstract [en]

    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.

  • 13.
    Avci, Onur
    et al.
    Qatar University, Qatar.
    Abdeljaber, Osama
    Qatar University, Qatar.
    Self-organizing maps for structural damage detection: a novel unsupervised vibration-based algorithm2016In: Journal of performance of constructed facilities, ISSN 0887-3828, E-ISSN 1943-5509, Vol. 30, no 3Article in journal (Refereed)
    Abstract [en]

    The study presented in this paper is arguably the first study to use a self-organizing map (SOM) for global structural damage detection. A novel unsupervised vibration-based damage detection algorithm is introduced using SOMs in order to quantify structural damage. In this algorithm, SOMs are used to extract a number of damage indices from the random acceleration response of the monitored structure in the time domain. The summation of the indices is used as an indicator which reflects the overall condition of the structure. The ability of the algorithm to quantify the overall structural damage is demonstrated using experimental data of Phase II experimental benchmark problem of structural health monitoring.

  • 14. Avci, Onur
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Kiranyaz, Serkan
    Boashash, Boualem
    Sodano, Henry
    Inman, Daniel
    Efficiency Validation of One Dimensional Convolutional Neural Networks for Structural Damage Detection Using A SHM Benchmark Data2018Conference paper (Other academic)
    Abstract [en]

    In this paper, a novel one dimensional convolution neural network (1D-CNN) based structural damage assessment technique is validated with a benchmark study published by IASC-ASCE Structural Health Monitoring Task Group in 2003. In contrast with predominant machine learning based structural damage detection techniques of the literature, the technique shown in this paper runs without manual feature extraction or preprocessing stages. It runs directly on the raw vibration data. In CNNs, the stages of feature extraction and feature classification are merged into one stage; therefore, the proposed technique is efficient, feasible and economical. Utilizing the optimal features learned by 1D CNNs, the proposed CNN-based technique considerably improves the classification efficiency and accuracy. The performance improvement of the proposed technique is assessed by calculating the “Probability of Damage” values for damage estimations. The unseen structural damage cases between the two extreme end structural cases (zero damage and total damage) were successfully identified. Consequently, it is validated that the improved CNN-based technique is efficient since it predicted the level of damage consistently with the structural damage cases defined in the existing benchmark.

  • 15.
    Avci, Onur
    et al.
    Qatar University, Qatar.
    Abdeljaber, Osama
    Qatar University, Qatar.
    Kiranyaz, Serkan
    Qatar University, Qatar.
    Hussein, Mohammed
    Qatar University, Qatar.
    Inman, Daniel
    University of Michigan, USA.
    Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks2018In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 424, p. 158-172Article in journal (Refereed)
    Abstract [en]

    Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

  • 16. Avci, Onur
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Kiranyaz, Serkan
    Inman, Daniel
    Control of Plate Vibrations with Artificial Neural Networks and Piezoelectricity2020In: Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing, 2020, Vol. 7Conference paper (Other academic)
    Abstract [en]

    This paper presents a method for active vibration control of smart thin cantilever plates. For model formulation needed for controller design and simulations, finite difference technique is used on the cantilever plate response calculations. Piezoelectric patches are used on the plate, for which a neural network based control algorithm is formed and a neurocontroller is produced to calculate the required voltage to be applied on the actuator patch. The neurocontroller is trained and run with a Kalman Filter for controlling the structural response. The neurocontroller performance is assessed by comparing the controlled and uncontrolled structural responses when the plate is subjected to various excitations. It is shown that the acceleration response of the cantilever plate is suppressed considerably validating the efficacy of the neurocontroller and the success of the proposed methodology.

  • 17. Avci, Onur
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Kiranyaz, Serkan
    Inman, Daniel
    Convolutional Neural Networks for Real-Time and Wireless Damage Detection2020In: Dynamics of Civil Structures, 2020, Vol. 2Conference paper (Other academic)
    Abstract [en]

    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.

  • 18. Avci, Onur
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Kiranyaz, Serkan
    Inman, Daniel
    Structural Damage Detection in Real Time: Implementation of 1D Convolutional Neural Networks for SHM Applications2017In: Structural Health Monitoring & Damage Detection, Springer, 2017, Vol. 7Conference paper (Other academic)
    Abstract [en]

    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.

  • 19. Avci, Onur
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Kiranyaz, Serkan
    Inman, Daniel
    Structural Health Monitoring with Self-Organizing Maps and Artificial Neural Networks2020In: Topics in Modal Analysis & Testing, 2020, Vol. 8Conference paper (Other academic)
    Abstract [en]

    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.

  • 20.
    Catbas, Catbas
    et al.
    University of Central Florida, USA.
    Celik, Ozan
    University of Central Florida, USA.
    Avci, Onur
    Qatar University, Qatar.
    Abdeljaber, Osama
    Qatar University, Qatar.
    Gul, Mustafa
    University of Alberta, Canada.
    Do, Ngoan
    University of Alberta, Canada.
    Sensing and monitoring for stadium structures: a review of recent advances and a forward look2017In: Frontiers in built environment, E-ISSN 2297-3362, Vol. 38, no 3Article in journal (Refereed)
    Abstract [en]

    Stadiums like those used for sporting or concert events are distinct from other civil engineering structures due to several different characteristics. Some challenges mainly originate from the interaction with the human factor, as stadiums are subjected to both synchronized and random motion of large crowds. The investigations in the literature on this topic clearly state that stadiums designs are in urgent need of more reliable load quantification and modeling strategies, deeper understanding of structural response, generation of simple but efficient human–structure interaction models, and more accurate criteria for vibration acceptability. Although many esthetically pleasing and technologically advanced stadiums have been designed and constructed using structurally innovative methods, recent research on this field still calls for less conservative and more realistic designs. This article aims to highlight the recent advances in this field and to provide a follow-up to the literature review covering until 2008 (Jones et al., 2011a) on vibration serviceability of stadiums structures. The article will also discuss new sensing and monitoring techniques on load-time history measurements and their regeneration, as well as crowd motion, stadium health monitoring, and human comfort analysis. Operational effects of crowds on the dynamic properties are also discussed. The article concludes with a forward look on the recommended work and research for dynamic assessment of stadiums.

  • 21. Celik, Ozan
    et al.
    Do, Ngoan Tien
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Gul, Mustafa
    Avci, Onur
    Catbas, Necati
    Recent issues on stadium monitoring and serviceability: A review2016In: Dynamics of Coupled Structures, 2016, Vol. 7Conference paper (Other academic)
    Abstract [en]

    Unlike most of civil engineering structures whose static and dynamic responses are estimated accurately through several codes and guidance, stadiums reserve a distinctive place especially when it comes to their dynamic behavior. This difference takes its source from several factors such as influence of crowd size, motion and slenderness of the structure. The most noticeable form of this difference shows itself as excessive vibration levels which is actually a threat to the serviceability of these structures. Eventually, it becomes essential to carefully evaluate several steps of this particular problem starting from correct representation of crowd activity through accurate loadings and human-structure interaction models to arranging acceptable vibration serviceability limits. This publication intends to point out the newly developed techniques and discovered issues on several stages of the problem during the last decade.

  • 22.
    Do, Ngoan T.
    et al.
    University of Alberta, Canada.
    Gül, Mustafa
    University of Alberta, Canada.
    Abdeljaber, Osama
    Qatar University, Qatar.
    Avci, Onur
    Qatar University, Qatar.
    Novel framework for vibration serviceability assessment of stadium grandstands considering durations of vibrations2018In: Journal of Structural Engineering, ISSN 0733-9445, E-ISSN 1943-541X, Vol. 144, no 2Article in journal (Refereed)
    Abstract [en]

    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.

  • 23. Kiranyaz, Serkan
    et al.
    Ince, Turker
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Avci, Onur
    Gabbouj, Moncef
    1-D Convolutional Neural Networks for Signal Processing Applications2019Conference paper (Refereed)
    Abstract [en]

    1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the "Big Data" scale in order to prevent the well-known "overfitting" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction …

  • 24. Rjoub, Yousif
    et al.
    Abdeljaber, Osama
    Linnaeus University, Faculty of Technology, Department of Building Technology.
    Free and forced vibration of rectangular plates using the finite difference method2014In: Green Building, Materials and Civil Engineering, CRC Press, 2014Conference paper (Other academic)
1 - 24 of 24
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