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Abdeljaber, O., Hussein, M., Avci, O., Davis, B. & Reynolds, P. (2020). A novel video-vibration monitoring system for walking pattern identification on floors. Advances in Engineering Software, 139, Article ID 102710.
Open this publication in new window or tab >>A novel video-vibration monitoring system for walking pattern identification on floors
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2020 (English)In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 139, article id 102710Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2020
National Category
Building Technologies
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-90285 (URN)10.1016/j.advengsoft.2019.102710 (DOI)
Available from: 2019-11-27 Created: 2019-11-27 Last updated: 2019-12-18Bibliographically approved
Avci, O., Abdeljaber, O., Kiranyaz, S. & Inman, D. (2020). Control of Plate Vibrations with Artificial Neural Networks and Piezoelectricity. In: Chad WalberPatrick WalterSteve Seidlitz (Ed.), Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. Paper presented at The 37th IMAC, A Conference and Exposition on Structural Dynamics (pp. 293-301). Springer
Open this publication in new window or tab >>Control of Plate Vibrations with Artificial Neural Networks and Piezoelectricity
2020 (English)In: Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 / [ed] Chad WalberPatrick WalterSteve Seidlitz, Springer, 2020, p. 293-301Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer, 2020
Series
Conference Proceedings of the Society for Experimental Mechanics Series, ISSN 2191-5644, E-ISSN 2191-5652 ; 7
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-89755 (URN)10.1007/978-3-030-12676-6_26 (DOI)978-3-030-12675-9 (ISBN)978-3-030-12676-6 (ISBN)
Conference
The 37th IMAC, A Conference and Exposition on Structural Dynamics
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-12-20Bibliographically approved
Avci, O., Abdeljaber, O., Kiranyaz, S. & Inman, D. (2020). Convolutional Neural Networks for Real-Time and Wireless Damage Detection. In: Shamim Pakzad (Ed.), Dynamics of Civil Structures: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. Paper presented at The 37th IMAC, A Conference and Exposition on Structural Dynamics, 28-31 January, 2019, Orlando, Florida (pp. 129-136). Springer
Open this publication in new window or tab >>Convolutional Neural Networks for Real-Time and Wireless Damage Detection
2020 (English)In: Dynamics of Civil Structures: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 / [ed] Shamim Pakzad, Springer, 2020, p. 129-136Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer, 2020
Series
Conference Proceedings of the Society for Experimental Mechanics Series, ISSN 2191-5644, E-ISSN 2191-5652 ; 2
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-89750 (URN)10.1007/978-3-030-12115-0_17 (DOI)978-3-030-12114-3 (ISBN)978-3-030-12115-0 (ISBN)
Conference
The 37th IMAC, A Conference and Exposition on Structural Dynamics, 28-31 January, 2019, Orlando, Florida
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-12-20Bibliographically approved
Avci, O., Abdeljaber, O., Kiranyaz, S. & Inman, D. (2020). Structural Health Monitoring with Self-Organizing Maps and Artificial Neural Networks. In: Topics in Modal Analysis & Testing: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. Paper presented at The 37th IMAC, A Conference and Exposition on Structural Dynamics, 28-31 January, 2019, Orlando, USA (pp. 237-246). Springer
Open this publication in new window or tab >>Structural Health Monitoring with Self-Organizing Maps and Artificial Neural Networks
2020 (English)In: Topics in Modal Analysis & Testing: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019, Springer, 2020, p. 237-246Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer, 2020
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-89754 (URN)10.1007/978-3-030-12684-1_24 (DOI)978-3-030-12683-4 (ISBN)978-3-030-12684-1 (ISBN)
Conference
The 37th IMAC, A Conference and Exposition on Structural Dynamics, 28-31 January, 2019, Orlando, USA
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-12-20Bibliographically approved
Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O. & Gabbouj, M. (2019). 1-D Convolutional Neural Networks for Signal Processing Applications. In: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing: May 12–17, 2019 Brighton Conference Centre Brighton, United Kingdom. Paper presented at ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May, 2019, Brighton (pp. 8360-8364). IEEE
Open this publication in new window or tab >>1-D Convolutional Neural Networks for Signal Processing Applications
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2019 (English)In: 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing: May 12–17, 2019 Brighton Conference Centre Brighton, United Kingdom, IEEE, 2019, p. 8360-8364Conference paper, Published 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 …

Place, publisher, year, edition, pages
IEEE, 2019
Series
Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing., E-ISSN 1520-6149
National Category
Signal Processing
Research subject
Physics, Waves and Signals
Identifiers
urn:nbn:se:lnu:diva-89756 (URN)10.1109/ICASSP.2019.8682194 (DOI)9781479981311 (ISBN)9781479981328 (ISBN)
Conference
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May, 2019, Brighton
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-12-18Bibliographically approved
Abdeljaber, O., Sassi, S., Avci, O., Kiranyaz, S., Ibrahim, A. & Gabbouj, M. (2019). Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring. IEEE transactions on industrial electronics (1982. Print), 66(10), 8136-8147
Open this publication in new window or tab >>Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring
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2019 (English)In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 66, no 10, p. 8136-8147Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Reliability and Maintenance
Research subject
Technology (byts ev till Engineering), Industrial economy
Identifiers
urn:nbn:se:lnu:diva-88123 (URN)10.1109/TIE.2018.2886789 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-28Bibliographically approved
Abdeljaber, O., Avci, O., Kiranyaz, S., Boashash, B., Sodano, H. & Inman, D. (2018). 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing, 275, 1308-1317
Open this publication in new window or tab >>1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data
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2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 275, p. 1308-1317Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering Computer Sciences
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88125 (URN)10.1016/j.neucom.2017.09.069 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-12Bibliographically approved
Avci, O., Abdeljaber, O., Kiranyaz, S., Boashash, B., Sodano, H. & Inman, D. (2018). Efficiency Validation of One Dimensional Convolutional Neural Networks for Structural Damage Detection Using A SHM Benchmark Data. In: 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling: . Paper presented at 25th International Congress on Sound and Vibration: Hiroshima Calling, ICSV 2018; Hiroshima; Japan; 8-12 July 2018 (pp. 4600-4607). International Institute of Acoustics and Vibration (IIAV)
Open this publication in new window or tab >>Efficiency Validation of One Dimensional Convolutional Neural Networks for Structural Damage Detection Using A SHM Benchmark Data
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2018 (English)In: 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling, International Institute of Acoustics and Vibration (IIAV) , 2018, p. 4600-4607Conference paper, Published 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.

Place, publisher, year, edition, pages
International Institute of Acoustics and Vibration (IIAV), 2018
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:lnu:diva-89751 (URN)9781510868458 (ISBN)
Conference
25th International Congress on Sound and Vibration: Hiroshima Calling, ICSV 2018; Hiroshima; Japan; 8-12 July 2018
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-12-20Bibliographically approved
Do, N. T., Gül, M., Abdeljaber, O. & Avci, O. (2018). Novel framework for vibration serviceability assessment of stadium grandstands considering durations of vibrations. Journal of Structural Engineering, 144(2)
Open this publication in new window or tab >>Novel framework for vibration serviceability assessment of stadium grandstands considering durations of vibrations
2018 (English)In: Journal of Structural Engineering, ISSN 0733-9445, E-ISSN 1943-541X, Vol. 144, no 2Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2018
National Category
Other Civil Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88126 (URN)10.1061/(ASCE)ST.1943-541X.0001941 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-18Bibliographically approved
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M. & Inman, D. (2018). Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks. Journal of Sound and Vibration, 424, 158-172
Open this publication in new window or tab >>Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks
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2018 (English)In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 424, p. 158-172Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science; Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-88124 (URN)10.1016/j.jsv.2018.03.008 (DOI)
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0530-9552

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