The Performance of a Modified Harmony Search Algorithm in the Structural Identification and Damage Detection of a Scaled Offshore Wind Turbine Laboratory Model

Jahjouh, Mahmoud; Rolfes, Raimund

doi:10.1007/978-3-319-97773-7_18

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…Recent optimization studies such as [20] and [21] show that HS is a superior optimization algorithm with promising potential. The gradient-free nature of the HS algorithm enabled it to achieve satisfying results even for the most complex optimization problems, such as structural identification and damage detection [22][23][24]. Thus, an improved version of the harmony search algorithm was chosen in this study.…”

Section: The Harmony Search Algorithmmentioning

confidence: 99%

An experience based artificial neural network in the design optimization of steel frames

Jahjouh

2022

Eng. Res. Express

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The design of steel frames is an iterative process relying on the experience and decisions of the designer to achieve economical and safe designs. With recent advances in artificial intelligence, particularly, artificial neural networks, it became possible to train such networks to simulate an experienced designer. Thus, the aim of this contribution is to investigate the possibility of artificial neural networks gaining design experience and using such experience in predicting adequate and economical designs. To achieve this aim, an adaptive harmony search algorithm is used to obtain the optimum structural design of two-dimensional steel frames. Those designs are, in a sense, considered an experience, which are then used in training artificial neural networks. The trained networks are finally used in predicting the optimum solution of new problem variants. In total, 18684 samples based on 3114 two-dimensional frames were used to train multiple feed forward artificial neural networks, with a training, validation and testing ratios of 70%, 15% and 15%, respectively. The trained networks' performance was verified, and used in design predictions on interpolated and extrapolated cases. Considering the designs suggested by the artificial neural networks, 99% were adequate in the case of network verification. Furthermore, 97% and 93% of designs were adequate in the case of interpolation and extrapolation. Thus, artificial neural networks are able to learn from the design experience and provide good approximations for designs of variants even outside the training set. Such findings encourage the development of artificial intelligence assisted design systems that are capable of suggesting optimum or near-optimum designs for two-dimensional frames. Also, it could encourage further research for three-dimensional steel frames and more complex steel structure systems.

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Section: The Harmony Search Algorithmmentioning

confidence: 99%

An experience based artificial neural network in the design optimization of steel frames

Jahjouh

2022

Eng. Res. Express

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“…Optimization algorithms were successful in many aspects related to civil engineering problems. A modified version of the harmony search algorithm was used in (Jahjouh and Nackenhorst, 2016b) to identify damage in a wind turbine structure. The optimal design of trusses using a hybrid version of particle swarm optimization, ray optimization and harmony search (HRPSO) was presented in (Kaveh and Javadi, 2014b) and compared to other metaheuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Truss structures were also optimized using a hybridized particle swarm optimizer, ant colony and harmony search in (Kaveh and Talatahari, 2009). Other optimization algorithm applications can be found in (Kaveh and Abadi, 2010;Kaveh and Shakouri, 2010;Kaveh and Ahangaran, 2012;Jahjouh et al, 2013;Jahjouh, 2022b;Jahjouh and Nackenhorst, 2016a;Jahjouh, 2022a;Jahjouh and Nackenhorst, 2015;Alghamri et al, 2022;Jahjouh and Rolfes, 2018).…”

Section: Introductionmentioning

confidence: 99%

Simplified vs. Full-Scale Onshore Wind Turbine Models: A Comparative Analysis

Jahjouh

2022

Preprint

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With the growth in demand for renewable energy, the number, size and production capabilities of wind turbines have grown significantly. Such turbines are subjected to high force fluctuations and are prone to damage, thus requiring a robust and accurate structural identification scheme. Since the modeling of a full scale wind turbine is a rather complex matter, a simplified model for onshore wind turbine tower identification is used in this contribution, followed by a structural identification and damage detection of the turbine tower using a variety of sensor, noise and damage scenarios. The studied wind turbine is an idling land-based version of a baseline 5-MW reference wind turbine developed by the national renewable energy laboratory (NREL). More than 1300 test setups were considered and the results obtained were satisfying in terms of accuracy and robustness and encourage the investigation of more complex identification problems such as, but not limited to, soil-structure interaction and the output-only identification of wind turbines, e.g. when input excitations are unknown.

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“…A rapid survey of the optimization literature produced over the last 15 years reveals that GA [44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62], DE [63,64,65,66,67,68,69,70,71,72,73,74,75], SA [76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97], HS [98,99,100,101,102,103,104,105,106,107,108,109,110,…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

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The Performance of a Modified Harmony Search Algorithm in the Structural Identification and Damage Detection of a Scaled Offshore Wind Turbine Laboratory Model

Cited by 6 publications

References 39 publications

An experience based artificial neural network in the design optimization of steel frames

An experience based artificial neural network in the design optimization of steel frames

Simplified vs. Full-Scale Onshore Wind Turbine Models: A Comparative Analysis

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

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