| 1. |
- Abdeljaber, Osama, et al.
(författare)
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1-D CNNs for structural damage detection : verification on a structural health monitoring benchmark data
- 2018
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 275, s. 1308-1317
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Abdeljaber, Osama, et al.
(författare)
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A novel video-vibration monitoring system for walking pattern identification on floors
- 2020
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Ingår i: Advances in Engineering Software. - : Elsevier. - 0965-9978 .- 1873-5339. ; 139
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Abdeljaber, Osama, et al.
(författare)
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Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks
- 2016
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Ingår i: Journal of Sound and Vibration. - : Elsevier. - 0022-460X .- 1095-8568. ; 363, s. 33-53
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Abdeljaber, Osama, et al.
(författare)
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Analysis of the Trajectories of Left-turning Vehicles at Signalized Intersections
- 2020
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Ingår i: Transportation Research Procedia. - : Elsevier BV. ; , s. 1288-1295, s. 1288-1295
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Konferensbidrag (refereegranskat)abstract
- Internationally, an annual number of more than a million fatalities are caused by road traffic crashes, with particularly signalized intersections being crash prone locations within the highway system. An accumulation of conflicts between drivers is caused by the different movements (through and turning) from different directions at the intersection; hence, studying the trajectories of turning vehicles is an important step towards improving traffic safety performance of these facilities. In view of that, the current paper aims at providing further insight into the behaviour of left-turning vehicles (right-hand traffic rule) at signalized intersections in the State of Qatar. At first, a total of 44 trajectories of free-flowing vehicles were manually extracted from a recorded video for a single approach of Lekhwair signalized intersection in Doha City, State of Qatar. After that, the extracted trajectories were statistically analysed in an attempt to explore the factors affecting the path of left-turning vehicles at signalized intersections. The results suggest that the characteristics of the extracted paths are significantly related to the vehicle’s entry speed, minimum speed throughout its turning manoeuvre, and the lateral distance between the exit point and the curb (i.e., targeted exit lane). Provided that the speed parameters can be fairly an indication to the driving behaviour, it can be concluded that the driver’s attitude plays an important role in drawing the manoeuvre of a turning vehicle as does the pre-selection of the exit lane. Finally, the effort presented in this paper can be regarded as a way forward towards understanding the behaviour of turning vehicles at signalised intersection in the State of Qatar.
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| 5. |
- Abdeljaber, Osama, et al.
(författare)
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Automatic estimation of annual ring profiles in Norway spruce timber boards using optical scanning and deep learning
- 2023
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Ingår i: Computers & structures. - : Elsevier. - 0045-7949 .- 1879-2243. ; 275
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Tidskriftsartikel (refereegranskat)abstract
- In softwood species, annual ring width correlates with various timber characteristics, including the density and modulus of elasticity along with bending and tensile strengths. Knowledge of annual ring profiles may contribute to more accurate machine strength grading of sawn timber. This paper proposes a fast and accurate method for automatic estimation of ring profiles along timber boards on the basis of optical scanning. The method utilizes two 1D convolutional neural networks to determine the pith location and detect the surface annual rings at multiple cross-sections along the scanned board. The automatically extracted rings and pith information can then be used to estimate the annual ring profile at each cross-section. The proposed method was validated on a large number of board cross-sections for which the pith locations and radial ring width profiles had been determined manually. The paper also investigates the potential of using the automatically estimated average ring width as an indicating property in machine strength grading of sawn timber. The results indicated that combining the automatically estimated ring width with other prediction variables can improve the accuracy of bending and tensile strength predictions, especially when the grading is based only on information extracted from optical and laser scanning data.(C) 2022 The Author(s). Published by Elsevier Ltd.
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| 6. |
- Abdeljaber, Osama, et al.
(författare)
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Dynamic Testing of a Laboratory Stadium Structure
- 2016
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Ingår i: Geotechnical and Structural Engineering Congress 2016. - Reston, VA : American Society of Civil Engineers (ASCE). - 9780784479742 ; , s. 1719-1728
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 7. |
- Abdeljaber, Osama, et al.
(författare)
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Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks
- 2020
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Ingår i: Arabian Journal for Science and Engineering. - Heidelberg : Springer. - 2193-567X .- 2191-4281 .- 1319-8025. ; 45, s. 8011-8025
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Abdeljaber, Osama, et al.
(författare)
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Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring
- 2019
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Ingår i: IEEE Transactions on Industrial Electronics. - : IEEE. - 0278-0046 .- 1557-9948. ; 66:10, s. 8136-8147
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Abdeljaber, Osama, et al.
(författare)
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Genetic algorithm use for internally resonating lattice optimization : case of a beam-like metastructure
- 2016
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Ingår i: Dynamics of Civil Structures. - Cham : Springer. - 9783319297507 - 9783319297514 ; , s. 289-295
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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 …
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| 10. |
- Abdeljaber, Osama, et al.
(författare)
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Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps
- 2016
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Ingår i: Journal of Architectural Engineering. - : Elsevier. - 1076-0431 .- 1943-5568. ; 22:2
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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