Developing a physics-informed and physics-penalized neural network model for preliminary design of multi-stage friction pendulum bearings

Habib, Ahed; Yıldırım, Umut

doi:10.1016/j.engappai.2022.104953

Cited by 23 publications

(8 citation statements)

References 62 publications

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“…For example, physics-informed NN models were developed to predict the structural instability, [108] design the key structural parameters, [109,110] and optimize the structural response. [111][112][113] Interfacial inverse design can be conducted on the contact surfaces of TENGs by physical patterning such as designing various artificially localized morphologies. For example, mechanical metamaterials, manmade structural materials assembled by numerous microstructures in a periodic manner, have recently been applied in TENGs to promote the electrical performance due to their structural programmability.…”

Section: Structural Design and Optimization For Contact Interfacesmentioning

confidence: 99%

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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Section: Structural Design and Optimization For Contact Interfacesmentioning

confidence: 99%

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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“…A straightforward method for assessing structures supported on QFP bearings under bidirectional near-fault seismic stress was also proposed by Keisha and Ghodrati Amiri . Habib and Yildirim (2022a) introduced an approach for preliminary designing QFP bearings using artificial neural networks. Sodha et al (2021) examined the performance of a linear visco-elastic model for QFP bearing performance.…”

Section: Introductionmentioning

confidence: 99%

Distribution of strong input energy in base-isolated structures with complex nonlinearity: a parametric assessment

Habib

Yıldırım²

2023

MMMS

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PurposeOver the past few decades, several base isolation systems have been developed to enhance the performance of structures under extreme earthquake shaking intensities. Recently, to achieve high energy dissipation capabilities, a new generation of multi-stage friction pendulum (FP) bearings known as the “Quintuple Friction Pendulum (QFP)” was introduced in the literature. With the help of its five effective pendula and nine operational regimes, this bearing's major benefits stem from its ability to accomplish complicated multi-stage adaptive behavior with smoothed loading and unloading when subjected to lateral forces.Design/methodology/approachWithin the assessment context, five finite element models of reinforced concrete frames supported on QFP isolators with different properties will be developed in OpenSees. Thereafter, a set of 60 earthquakes will be analyzed using the nonlinear time history analysis approach, and the impact of each ground motion record's properties will be evaluated.FindingsOverall, the study's findings have demonstrated that the characteristics of the isolator, combined with the type of earthquake being applied, have a substantial impact on the isolator's behavior.Originality/valueCurrently, no studies have examined the energy distribution of structural systems equipped with this type of isolation system while considering the influence of earthquake characteristics. Thus, this study is intended to extend the findings available in the literature by discussing and illustrating the distribution of strong ground motions input energy into highly nonlinear base-isolated systems that account for the bearing and superstructural materials' nonlinearity, geometric nonlinearity and leakage-prevented viscous damping nonlinearity. Besides, it investigates the influence of various earthquake characteristics on the energy dissipation of such buildings.

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“…Nowadays, an artificial neural network (ANN) is considered an efficient method for developing accurate estimation models for the rapid design and assessment of structural systems [7]. Over the last few decades, this approach has been applied extensively in the engineering field to solve numerical problems by constructing a model that maps the input and output of a given dataset [8]. Previously, ANN was used in various civil engineering problems, such as concrete mixtures' mechanical properties prediction [9,10,11,12], damage detection [13,14,15], structural response estimation [16,17,18], soil behavior modeling [19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Alqawasmeh¹

2023

GEOMATE

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The slope-stability analysis is one of the most important parameters for ensuring a safe design of road embankments. Currently, various traditional approaches to computing this variable can be seen in the literature. Among them, the finite element method is considered an accurate way to define the safety factor of road embankments. Previous research has investigated the capability of artificial neural networks for rapid safety-factor estimation to overcome the long process of modeling and calculations required in the aforementioned approach. However, most of these studies have focused on a single type of neural network and did not investigate the capabilities of other approaches. Therefore, this study is intended to evaluate the performance of various artificial neural network techniques in predicting the safety factor of road embankments. Within this context, the feed-forward back-propagation, cascade forward neural networks, and general regression neural network results will be compared and benchmarked against various methods used to predict this parameter. Moreover, it is intended to report the influence of neural network architecture on the accuracy of the estimation. Generally, the study results have shown that an artificial neural network provides a rapid and accurate method for calculating road embankments' safety factors. Besides, the best neural network model achieved a coefficient of determination of about 0.91 and a root mean square error of 0.236, which proves the efficiency of this technique. Moreover, the reliability assessment by comparing the neural network models against the traditional methods has shown that they provide better agreement with the finite element technique.

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Developing a physics-informed and physics-penalized neural network model for preliminary design of multi-stage friction pendulum bearings

Cited by 23 publications

References 62 publications

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Distribution of strong input energy in base-isolated structures with complex nonlinearity: a parametric assessment

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

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