Abstract
A nonlinear vibrational analysis of sandwich curved panels having multi-scale face sheets has been performed in this
article based on differential quadrature method (DQM). All mechanical properties of multi-scale skins have been established in the context of three-dimensional Mori-Tanaka scheme for which the influences of glass fibers and random carbon nanotubes (CNTs) have been taken into account. The governing equations for sandwich the panel have been developed based upon thin shell formulation in which geometry nonlinearities have been taken into account. Next, DQ approach has been applied to solve the governing equations for determining the relationships of frequencies with deflections for curved panels. It will be demonstrated that the relationships of frequencies with deflections are dependent on the changing of CNT weight fractions,
fibers alignment, fibers volume, panel radius and skin thickness.
Key Words
curved panel; nanocomposite materials; nonlinear vibrations; numerical method; Shell theory
Address
Zhenming Cui: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210000, Jiangsu, China
Xin Cai: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210000, Jiangsu, China; College of Mechanics and Materials, Hohai University, Nanjing 210000, Jiangsu, China
H. Elhosiny Ali: Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia;
Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61413, P.O. Box 9004, Saudi Arabia; Department of Physics, Faculty of Science, Zagazig University, Egypt
Sami Muhsen: Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq
Abstract
In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together
with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALSSVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.
Key Words
active learning strategy; combined high and low cycle fatigue; least squares support vector machines; reliability assessment; turbine blade
Address
Juan Ma: Research Center of Applied Mechanics, School of Electro-Mechanical Engineering, Xidian University, Xi'an, 710071, P.R. China; Shaanxi Key Laboratory of Space Extreme Detection, Xidian University, Xi'an, P.R. China
Peng Yue: Research Center of Applied Mechanics, School of Electro-Mechanical Engineering, Xidian University, Xi'an, 710071, P.R. China; School of Mechanical Engineering, Xihua University, Chengdu, 610039, P.R. China
Wenyi Du: Shaanxi Key Laboratory of Space Extreme Detection, Xidian University, Xi'an, P.R. China
Changping Dai: Research Center of Applied Mechanics, School of Electro-Mechanical Engineering, Xidian University, Xi'an, 710071, P.R. China; Shaanxi Key Laboratory of Space Extreme Detection, Xidian University, Xi'an, P.R. China
Peter Wriggers: Institute of Continuum Mechanics, Leibniz University Hannover, 30167, Germany
Abstract
To realize the recycling utilization of waste concrete and alleviate the shortage of resources, 11 specimens of steel reinforced recycled concrete (SRRC) filled circular steel tube columns were designed and manufactured in this study, and the cyclic loading tests on the specimens of columns were also carried out respectively. The hysteretic curves, skeleton curves and performance indicators of columns were obtained and analysed in detail. Besides, the finite element model of columns was established through OpenSees software, which considered the adverse effect of recycled coarse aggregate (RA) replacement rates and the constraint effect of circular steel tube on internal RAC. The numerical calculation curves of columns are in good agreement with the experimental curves, which shows that the numerical model is relatively reasonable. On this basis, a series of nonlinear parameters analysis on the hysteretic behaviors of columns were also investigated. The results are as follows: When
the replacement rates of RA increases from 0 to 100%, the peak loads of columns decreases by 7.78% and the ductility decreases slightly. With the increase of axial compression ratio, the bearing capacity of columns increases first and then decreases, but the ductility of columns decreases rapidly. Increasing the wall thickness of circular steel tube is very profitable to improve the bearing capacity and ductility of columns. When the section steel ratio increases from 5.54% to 9.99%, although the bearing capacity of columns is improved, it has no obvious contribution to improve the ductility of columns. With the decrease of shear span ratio, the bearing capacity of columns increases obviously, but the ductility decreases, and the failure mode of columns develops into brittle shear failure. Therefore, in the engineering design of columns, the situation of small shear span ratio (i.e., short columns) should be avoided as far as possible. Based on this, the calculation model on the skeleton curves of columns was established by the theoretical analysis and fitting method, so as to determine the main characteristic points in the model. The effectiveness of skeleton curve model is verified by comparing with the test skeleton curves.
Key Words
composite columns; concrete filled steel tube; finite element analysis; hysteresis behaviors; steel reinforced recycled concrete
Address
Hui Ma: State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, China; School of Civil Engineering and Architecture, Xi'an University of Technology, Xi'an, China
Guoheng Zhang: State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, China
A. Xin: Qinghai Building and Materials Research Co., Ltd., China; Qinghai Provincial Key Laboratory of Plateau Green Building and Eco-community, China
Hengyu Bai: State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, China
Abstract
The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.
Abstract
During bolted flange assembly, the contact stiffness of some areas of the joint surface may be low due to the elastic interaction. In order to improve the contact stiffness at the lowest position of bolted flange, the correlation model between the initial bolt pre-tightening force and the contact stiffness of bolted flange is established in this paper. According to the stress distribution model of a single bolt, an assumption of uniform local contact stiffness of bolted flange is made. Moreover, the joint surface is divided into the compressive stress region and the elastic interaction region. Based on the fractal contact theory, the relationship model of contact stiffness and contact force of the joint surface is proposed. Considering the elastic interaction coefficient method, the correlation model of the initial bolt pre-tightening force and the contact stiffness of bolted flange is established. This model can be employed to reverse determine the tightening strategy of the bolt group according to working conditions. As a result, this provides a new idea for the digital design of tightening strategy of bolt group for contact stiffness of bolted flange. The tightening strategy of the bolted flange is optimized by using the correlation model of initial bolt pretightening force and the contact stiffness of bolted flange. After optimization, the average contact stiffness of the joint surface increased by 5%, and the minimum contact stiffness increased by 6%.
Key Words
bolted flanges; compressive stress zone of bolt; contact stiffness; elastic interaction coefficient method; elastic interaction zone; single bolt stress distribution model
Address
Weiliang Zuo, Zhifeng Liu: Key Laboratory of CNC Equipment Reliability, Ministry of Education, Changchun 130022, Jilin Province, China; School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, Jilin Province, China
Yongsheng Zhao, Nana Niu, Mingpo Zheng: Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing 100124, China
Abstract
Linear Elastic Fracture Mechanics (LEFM) has been developed by applying stress analysis to determine the stress intensity factor (SIF, K). The finite element method (FEM) is widely used as a standard tool for evaluating the SIF for various crack configurations. The prediction accuracy can be achieved by applying an adaptive Delaunay triangulation combined with a FEM. The solution can be solved using either direct or iterative solvers. This work adopts the element-by-element preconditioned conjugate gradient (EBE-PCG) iterative solver into an adaptive FEM to solve the solution to heal problem size constraints that exist when direct solution techniques are applied. It can avoid the formation of a global stiffness matrix of a finite element model. Several numerical experiments reveal that the present method is simple, fast, and efficient compared to conventional sparse direct solvers. The optimum convergence criterion for two-dimensional LEFM analysis is studied. In this paper, four sample problems of a two-edge cracked plate, a center cracked plate, a single-edge cracked plate, and a compact tension specimen is used to evaluate the accuracy of the prediction of the SIF values. Finally, the efficiency of the present iterative solver is summarized by comparing the computational time for all cases.
Key Words
finite element method; iterative solver; LEFM; stress intensity factor
Address
Manat Hearunyakij and Sutthisak Phongthanapanich: Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand
Abstract
Flexural capacity prediction is a challenging problem for externally prestressed concrete beams (EPCBs) due to the unbonded phenomenon between the concrete beam and external tendons. Many prediction equations have been provided in previous research but typically ignored the differences in deformation mode between internal and external unbonded tendons. The availability of these equations for EPCBs is controversial due to the inconsistent deformation modes and ignored secondorder effects. In this study, the deformation characteristics and collapse mechanism of EPCB are carefully considered, and the ultimate deflected shape curves are derived based on the simplified curvature distribution. With the compatible relation between external tendons and the concrete beam, the equations of tendon elongation and eccentricity loss at ultimate states are derived, and the geometric interpretation is clearly presented. Combined with the sectional equilibrium equations, a rational and simplified flexural capacity prediction method for EPCBs is proposed. The key parameter, plastic hinge length, is emphatically discussed and determined by the sensitivity analysis of 324 FE analysis results. With 94 collected laboratory-tested results, the effectiveness of the proposed method is confirmed, and comparisons with the previous formulas are made. The results show the better prediction accuracy of the proposed method for both stress increments and flexural capacity of EPCBs and the main reasons are discussed.
Address
Wu-Tong Yan: School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China; China Railway Economic and Planning Research Institute Co., Ltd., Beijing 100038, China
Liang-Jiang Chen: China Railway Economic and Planning Research Institute Co., Ltd., Beijing 100038, China
Bing Han: School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Laboratory of Safety and Risk Management on Transport Infrastructures, Ministry of Transport, PRC, Beijing 100044, China
Feng Wei: China State Railway Group Co., Ltd., Beijing 100084, China
Hui-Bing Xie, Jia-Ping Yu: School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Abstract
This paper considers dynamic impedance functions and presents a detailed analysis of the soil plasticity influence on the pile-group foundation dynamic response. A three-dimensional finite element model is proposed, and a calculation method considering the time domain is detailed for the nonlinear dynamic impedance functions. The soil mass is modeled as continuum elastoplastic solid using the Mohr-Coulomb shear failure criterion. The piles are modeled as continuum solids and the slab as a structural plate-type element. Quiet boundaries are implemented to avoid wave reflection on the boundaries. The model and method of analysis are validated by comparison with those published on literature. Numerical results are presented in terms of
horizontal and vertical nonlinear dynamic impedances as a function of the shear soil parameters (cohesion and internal friction angle), pile spacing ratio and frequencies of the dynamic signal.
Address
Kamal Gheddar: LMGHU Laboratory, University of 20 Août 1955-Skikda, 21000 Skikda, Algeria
Badreddine Sbartai: LMGE Laboratory, University of Badji Mokhtar, 23000 Annaba, Algeria
Salah Messioud: LGCE Laboratory, University of Jijel, 18000 Jijel, Algeria
Daniel Dias: 3SR Laboratory, University of Grenoble-Alpes, France
Abstract
The practice of using encased steel-concrete columns in medium to high-rise structures has expanded dramatically in recent years. The study evaluates existing methodologies and codal guidelines for estimating the ultimate load-carrying characteristics of concrete-encased short columns experimentally. The present condition of composite column design methods was analyzed using the Egyptian code ECP203-2007, the American Institute of Steel Construction's AISC-LRFD-2010, Eurocode EC-4, the American Concrete Institute
Address
N. Divyah: Department of Civil Engineering, PSG Institute of Technology and Applied Research, Coimbatore, 641 062 Tamilnadu, India
R. Prakash: Department of Civil Engineering, Alagappa Chettiar Government College of Engineering and Technology,
Karaikudi, 630 003, Tamilnadu, India
S. Srividhya: Department of Civil Engineering, Varuvan Vadivelan Institute of Technology, Dharmapuri, 636703, Tamilnadu, India
A. Sivakumar: Department of Mechanical Engineering, Varuvan Vadivelan Institute of Technology, Dharmapuri, 636703, Tamilnadu, India
Abstract
The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.
Key Words
bridges; deep neural network; earthquake engineering; fragility analysis; seismic response
Address
Hyojoon An and Jong-Han Lee: Department of Civil Engineering, Inha University, Incheon 22212, Republic of Korea
