Artificial Intelligence-Based Prediction Models for Optimal Design of Tuned Mass Dampers in Damped Structures Subjected to Different Excitations

2021 ◽  
pp. 2150120
Author(s):  
Sadegh Etedali ◽  
Zohreh Khosravi Bijaem ◽  
Nader Mollayi ◽  
Vahide Babaiyan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
White Noise ◽  
Frequency Ratio ◽  
Damping Ratio ◽  
Mass Ratio ◽  
Optimal Frequency ◽  
Inference System ◽  
Main Structure ◽  
Optimal Damping

Tuned mass damper (TMD) is a type of energy absorbers that can mitigate the vibrations of the main system if its frequency and damping ratios are well adjusted. By adopting simple assumptions on the structure and loadings, many analytical and empirical relationships have been presented for the estimation of the parameters for TMDs. In this research, methods based on the artificial intelligence (AI) techniques are proposed for optimal tuning of the TMD parameters of the main damped-structure for three kinds of loadings: white-noise base acceleration, external white-noise force, and harmonic base acceleration. For this purpose, a dataset using the cuckoo search (CS) optimization algorithm is created. The performance of the proposed methods based on the radial basis function (RBF) neural network, feed-forward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF) techniques are evaluated by some statistical indicators. The results show the proper performance of these methods for the optimal estimation of the TMD parameters. Overall, the ANFIS method results in best matching with the observed dataset. Moreover, the simulation results indicate that the TMD’s optimal frequency ratio is reduced, while its optimal damping ratio is increased, against the increase in the TMD mass ratio of the main structure subjected to harmonic base acceleration. This trend with a less slope is observed for the optimal frequency ratio of the TMD in the main structure subjected to external white-noise force; however, the optimal damping ratio of the TMD is independent of its mass ratio in this case. Similar results are obtained for the main structure subjected to white-noise base acceleration.

Optimization of Dynamic Vibration Absorbers for Suppressing Sound Radiation of Plate Structures

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Guanghe Huo ◽  
Zhaobo Chen ◽  
Yinghou Jiao ◽  
Xuezhi Zhu
Keyword(s):  
Fixed Points ◽  
Frequency Ratio ◽  
Vibration Suppression ◽  
Damping Ratio ◽  
Sound Radiation ◽  
Thin Plates ◽  
Optimal Frequency ◽  
Radiation Control ◽  
Dynamic Vibration ◽  
Analytical Expressions

Abstract Dynamic vibration absorber (DVA) is a practical tool used for sound and vibration suppression in the specific frequency band. The parameters of DVAs should be optimally tuned to obtain the best sound and vibration suppression application effects. When the DVAs are used for structural vibration reduction, DVAs’ two parameters which are the optimal frequency ratio and damping ratio have simple analytical expressions. However, the concise analytical expressions of the DVAs that are used for suppressing the structural sound radiations have not been reported. First, this paper investigates the characteristics of DVAs in suppressing sound radiation from thin plates. Second, the fixed points’ phenomenon of the sound radiations of the plate carrying DVAs is revealed. In addition, the classical fixed points’ theory is extended into the optimization process of the DVAs that are used for sound radiation control of the plate. The analytical expression of the optimal frequency ratio, as well as the damping ratio optimization method of the DVA, is simultaneously proposed. Third, the installation position of DVAs is also presented to obtain a better acoustic radiation effect. Finally, the numerical simulations are performed to verify the availability of the method. It is showed that the best sound radiation control effect could be obtained by adopting the optimization means proposed in this paper.

scholarly journals Optimal Design of a Damped Single Degree of Freedom Platform for Vibration Suppression in Harmonically Forced Undamped Systems

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Jimmy S. Issa
Keyword(s):  
Objective Function ◽  
Frequency Response Function ◽  
Damping Ratio ◽  
Optimal Solution ◽  
Vibration Absorber ◽  
Primary System ◽  
Mass Ratio ◽  
Optimal Damping ◽  
Mass Ratios ◽  
Invariant Points

Vibration reduction in harmonically forced undamped systems is considered using a new vibration absorber setup. The vibration absorber is a platform that is connected to the ground by a spring and damper. The primary system is attached to the platform, and the optimal parameters of the latter are obtained with the aim of minimizing the peaks of the primary system frequency response function. The minimax problem is solved using a method based on invariant points of the objective function. For a given mass ratio of the system, the optimal tuning and damping ratios are determined separately. First, it is shown that the objective function passes through three invariant points, which are independent of the damping ratio. Two optimal tuning ratios are determined analytically such that two of the three invariant points are equally leveled. Then, the optimal damping ratio is obtained such that the peaks of the frequency response function are equally leveled. The optimal damping ratio is determined in a closed form, except for a small range of the mass ratio, where it is calculated numerically from two nonlinear equations. For a range of mass ratios, the optimal solution obtained is exact, because the two peaks coincide with the two equally leveled invariant points. For the remaining range, the optimal solution is semiexact. Unlike the case of the classical absorber setup, where the absorber performance increases with increasing mass ratios, it is shown that an optimal mass ratio exists for this setup, for which the absorber reaches its utmost performance. The objective function is shown in its optimal shape for a range of mass ratios, including its utmost shape associated with the optimal mass ratio of the setup.

Thermal Resistance Modeling of Oscillating Heat Pipes for Nanofluids by Artificial Intelligence Approach

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
M. Malekan ◽  
A. Khosravi ◽  
H. R. Goshayeshi ◽  
M. E. H. Assad ◽  
J. J. Garcia Pabon
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Thermal Resistance ◽  
Heat Pipe ◽  
Fuzzy Inference ◽  
Working Fluid ◽  
Group Method ◽  
Feed Forward Neural Network ◽  
Inference System ◽  
Intelligent Models

In this study, thermal resistance of a closed-loop oscillating heat pipe (OHP) is investigated using experimental tests and artificial intelligence methods. For this target, γFe2O3 and Fe3O4 nanoparticles are mixed with the base fluid. Also, intelligent models are developed to predict the thermal resistance of the OHP. These models are developed based on the heat input into evaporator section, the thermal conductivity of working fluids, and the ratio of the inner diameter to length of OHP. The intelligent methods are multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) type neural network. Thermal resistance of the heat pipe (as a measure of thermal performance) is considered as the target. The results showed that using the nanofluids as working fluid in the OHP decreased the thermal resistance, where this decrease for Fe3O4/water nanofluid was more than that of γFe2O3/water. The intelligent models also predicted successfully the thermal resistance of OHP with a correlation coefficient close to 1. The root-mean-square error (RMSE) for MLFFNN, ANFIS, and GMDH models was obtained as 0.0508, 0.0556, and 0.0569 (°C/W) (for the test data), respectively.

scholarly journals Mechanism Analysis and Parameter Optimization of Mega-Sub-Isolation System

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangxiu Li ◽  
Ping Tan ◽  
Xiaojun Li ◽  
Aiwen Liu
Keyword(s):  
Frequency Ratio ◽  
Seismic Isolation ◽  
Structural Parameters ◽  
Damping Ratio ◽  
Great Influence ◽  
Mechanism Analysis ◽  
Control Performance ◽  
Optimal Frequency ◽  
Working Mechanism ◽  
Isolation System

The equation of motion of mega-sub-isolation system is established. The working mechanism of the mega-sub-isolation system is obtained by systematically investigating its dynamic characteristics corresponding to various structural parameters. Considering the number and location of the isolated substructures, a procedure to optimally design the isolator parameters of the mega-sub-isolation system is put forward based on the genetic algorithm with base shear as the optimization objective. The influence of the number and locations of isolated substructures on the control performance of mega-sub-isolation system has also been investigated from the perspective of energy. Results show that, with increase in substructure mass, the working mechanism of the mega-sub-isolation system is changed from tuned vibration absorber and energy dissipation to seismic isolation. The locations of the isolated substructures have little influence on the optimal frequency ratio but have great influence on the optimal damping ratio, while the number of isolated substructures shows great impact on both the optimal frequency ratio and damping ratio. When the number of the isolated substructures is determined, the higher the isolated substructures, the more the energy that will be consumed by the isolation devices, and with the increase of the number of isolated substructures, the better control performance can be achieved.

scholarly journals Development of a new empirical correlation for predicting formation volume factor of reservoir oil using artificial intelligence

2021 ◽  
Vol 44 (4) ◽  
pp. 408-416
Author(s):  
E. V. Shakirova ◽  
A. A. Aleksandrov ◽  
M. V. Semykin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Fuzzy Inference ◽  
Empirical Correlation ◽  
Support Vector ◽  
Volume Factor ◽  
Inference System ◽  
Study Results ◽  
Artificial Neural

It is known that oil in reservoir conditions is characterized by the content of a certain amount of dissolved gas. As reservoir pressure decreases this gas is released from oil significantly changing its physical properties, primarily its density and viscosity. In addition, the oil volume also reduces, sometimes by 50–60 %. In this regard, when calculating reserves, it is necessary to justify the reduction amount of the reservoir oil volume when oil is extracted to the surface. For this purpose, the concept of formation volume factor of reservoir oil has been introduced. The formation volume factor of oil is considered one of the main characterizing parameters of crude oil. It is also required for modeling and predicting the characteristics of an oil reservoir. The purpose of the present work is to develop a new empirical correlation for predicting the formation volume factor of reservoir oil using artificial intelligence methods based on MATLAB software, such as: an artificial neural network, an adaptive neuro-fuzzy inference system, and a support vector machine. The article presents a new empirical correlation extracted from the artificial neural network based on 503 experimental data points for oils from the Eastern Siberia field, which was able to predict the formation volume factor of oil with the correlation coefficient of 0.969 and average absolute error of less than 1 %. The conducted study shows that the prediction accuracy of the desired parameter in the developed artificial intelligence model exceeds the accuracy of study results obtained by conventional statistical methods. Moreover, the model can be useful in the prospect of process optimization in field planning and development.

scholarly journals Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

2019 ◽  
Vol 9 (6) ◽  
pp. 1113 ◽  
Author(s):  
Dong Dao ◽  
Son Trinh ◽  
Hai-Bang Ly ◽  
Binh Pham
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Recycled Aggregates ◽  
Geopolymer Concrete ◽  
Mass Ratio ◽  
Adaptive Network ◽  
Inference System ◽  
Hybrid Artificial Intelligence

Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC.

scholarly journals Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

2017 ◽  
Vol 13 (3) ◽  
pp. 342 ◽  
Author(s):  
Alaá Rateb Mahmoud Al-shamasneh ◽  
Unaizah Hanum Binti Obaidellah
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Human Cancer ◽  
Imaging Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Inference System ◽  
Mortality And Morbidity ◽  
Artificial Intelligence Techniques

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

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