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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
Alabbasi, S., Hussein, M., Abdeljaber, O. & Avci, O. (2020). A numerical and experimental investigation of a special type of floating-slab tracks. Engineering structures, 215, Article ID 110734.
Open this publication in new window or tab >>A numerical and experimental investigation of a special type of floating-slab tracks
2020 (English)In: Engineering structures, ISSN 0141-0296, E-ISSN 1873-7323, Vol. 215, article id 110734Article in journal (Refereed) Published
Abstract [en]

Floating-Slab Tracks (FST) are predominantly used for mitigating railway-induced vibrations where the concrete slab is mounted on soft resilient bearings to provide vibration isolation. This paper presents a research study on the dynamic behavior of a special type of FST used in the recently built subway system in Doha, Qatar. The special FST has a continuous concrete slab with periodic grooves. Therefore, the track can be modeled as a periodic structure with a slab unit having two elements with different cross-sections. Extensive numerical and experimental investigations were conducted on a multi-unit full-scale mockup track representing the special FST. A fast running model based on the Dynamic Stiffness Method was developed and examined, in an initial numerical exercise, against a detailed Finite Element model for a track with a finite length. In the experimental campaign, a test was performed with an impact hammer to identify the actual vibration response of the mockup track. Results from the experimental investigations were then used for model updating of the fast running model. The model updating process was carried out according to an automated hybrid optimization approach that combines genetic algorithms with a local search method. Finally, the updated model was extended to an infinite model to investigate the influence of varying grooves thickness on the dynamic behavior of the special track with infinite length for both bending and torsion scenarios. The investigations suggested that reducing the thickness below 50% of the full thickness of the slab significantly affects the dynamic behavior of the special FST.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Floating-slab tracks, Railway vibrations, Train vibrations, Numerical modeling, Experimental modal testing, Genetic algorithms, Optimization
National Category
Infrastructure Engineering
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-94705 (URN)10.1016/j.engstruct.2020.110734 (DOI)
Available from: 2020-05-10 Created: 2020-05-10 Last updated: 2020-05-12Bibliographically 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
Abdeljaber, O., Younis, A. & Alhajyaseen, W. (2020). Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks. The Arabian Journal for Science and Engineering
Open this publication in new window or tab >>Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks
2020 (English)In: The Arabian Journal for Science and Engineering, ISSN 1319-8025Article in journal (Refereed) Epub ahead of print
Abstract [en]

This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Convolutional neural networks, minimum jerk method, Vehicles tracking
National Category
Transport Systems and Logistics
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-94704 (URN)10.1007/s13369-020-04546-y (DOI)
Available from: 2020-05-10 Created: 2020-05-10 Last updated: 2020-05-12
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
Dorn, M., Abdeljaber, O. & Klaeson, J. (2019). Structural Health Monitoring of House Charlie. Linnaeus University, Faculty of Technology Department of Building and Technology
Open this publication in new window or tab >>Structural Health Monitoring of House Charlie
2019 (English)Report (Other academic)
Abstract [en]

House Charlie is an office building located in Växjö, Sweden, with approx. 5,700 m2 area on four floors, fitting 3,700 m2 of office space, and 2,000 m2 of restaurants and conference rooms. The load-bearing structure is a column-beam system made from glued laminated timber (Glulam) with the flooring made from cross-laminated timber (CLT). The house is equipped with a net-work of sensors which were already installed during the construction phase. The design of the network was done in collaboration between the Department of Building Technology from Linnaeus University and SAAB, in close contact Videum and JSB, the owner and constructor, respectively. In the network, two sensor cards collect data from the sensor (displacement, relative humidity, temperature, vibrations, as well as weather station data) which is accessible via a 3G-router from the outside. Except for power supply the network is work-ing independently from the buildings facilities. The building was erected and the network installed during spring 2018, since then the network is providing data. The report describes the measurement network and its sensors as well as their positioning within the building. Additionally the results are presented for the time-span July 2018-December 2019 as well as an interpretation of the first 1.5 years of run-time are given.

Place, publisher, year, edition, pages
Linnaeus University, Faculty of Technology Department of Building and Technology, 2019. p. 52
Keywords
Structural HEalth Monitoring, Timber Engineering, dynamics, moisture
National Category
Building Technologies
Research subject
Technology (byts ev till Engineering), Civil engineering
Identifiers
urn:nbn:se:lnu:diva-89907 (URN)
Available from: 2020-04-20 Created: 2020-04-20 Last updated: 2020-04-24Bibliographically 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
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