Abstract
This paper presents an application of artificial neural networks (ANNs) to predict the behaviour of a large-span
retractable roof structure subjected to variable transverse and longitudinal horizontal forces resulting from movements of mobile segments with different moving speeds, variable wind speeds, and positions of the mobile roof segments (open, semi-open, and closed). Firstly, geometrically nonlinear displacements of the rail track for two movable segments located on two parallel spatial truss arches with a span of 224 m are analysed using a finite element method. Subsequently, ANNs are trained and tested to replace the nonlinear finite element analysis and predict the required structural response characteristics. Results obtained using
the proposed ANNs are compared to the results obtained using the standard nonlinear finite element method to demonstrate the effectiveness and robustness of artificial intelligence techniques. A significant conclusion is that the developed feedforward multilayer perceptron neural network with the 42-79-42-42 topology can adequately predict the behaviour of the retractable roof structure subjected to variable conditions. Results confirm the mathematical correctness and physical relevance of the presented approach. The neural network model can improve the control of the nonlinear behaviour of retractable roof structures.
Abstract
The present paper provides a comprehensive review of the progress of the inelastic seismic behavior of structures under multicomponent earthquake ground motion. A significant share of the existing literature in this field is limited to unidirectional ground motion, which may considerably underestimate the seismic demand. Particularly, the literature on the effect of the interaction of deformations along two orthogonal directions of columns in the post-elastic ranges is relatively small. The pros and cons of existing studies on this particular aspect are explained in detail, highlighting the need for incorporation of the presence of axial force in columns. Further, the present study examines the code provisions to consider the effect of multicomponent ground motion. In addition, a brief overview of the influence of the angle of incidence of ground motion on structures is also provided. The need for further research on pushover analysis (a less computationally intensive alternative to time history analysis) is also highlighted. Finally, computational research may be supplemented by at least a few experimental research though it is too costly. Challenges in different walks of post-elastic range behavior are presented concisely. Thus, the study may be helpful to the researcher working in this field.
Address
Md A. Hussain: Department of Civil Engineering, Indian Institute of Technology Bombay, Powai 400076, India
Sekhar C. Dutta: Department of Civil Engineering, Indian Institute of Technology (ISM) Dhanbad, 826004, India
Abstract
The axial bearing capacity of concrete columns confined by stirrups is a significant mechanical property in design and evaluation of concrete structures. However, due to the complicate mechanics between concrete, longitudinal reinforcement and stirrup, it is relatively difficult to accurately estimate the axial bearing capacity of confined concrete columns through traditional analysis methods. A new and robust approach for predicting axial bearing capacity of confined concrete columns using artificial neural networks (ANN) was explored in this paper with a reliable test database containing 208 specimens conducted in this paper and published literatures. The test database had a large application range, covering unconfined concrete compressive strength with 27-115.9 MPa and stirrup yield strength with 288-1000 MPa. Seven key parameters were considered as input variables, including size of core concrete, unconfined concrete compressive strength, stirrup volumetric ratio, stirrup yield strength, longitudinal reinforcement ratio, yield strength of longitudinal reinforcement, and confinement type. Moreover, to optimize ANN model configuration, the importance of different input parameters on axial bearing capacity of confined concrete columns was investigated. Furthermore, ANN calculating formulas about axial bearing capacity of concrete columns confined
by stirrups were established based on the proposed ANN model. Finally, the ANN model and the proposed ANN calculating formulas were evaluated and verified compared with experimental results and four existing theoretical models, indicating that the proposed calculating formulas have enough accuracy, reliability and application in predicting of the axial bearing capacity of confined concrete columns.
Address
Chongchi Hou, Kaixuan Wang, Tianbei Kang: School of Civil Engineering, Shenyang Jianzhu University, 25 Hunnan Middle Road, Shenyang, China
Wenzhong Zheng: School of Civil Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin, China
Abstract
Segmented reinforced concrete (RC) beams composed of precast concrete and built on site became a necessary because it is important to quickly create RC bridges, walls, roofs, stairs and culverts. Utilizing the flat joint type, near surface mounted (NSM) technology is created and implemented in pre-cast segmented RC beams for limiting the connections between portions. The primary goal of this study is enhancing segmented beams' (SBs) flexural performance. A four-point bending test was performed on each SB, with a constant bending applied to the joint location. The SBs had the same internal reinforcement and are composed of two comparable components. The variables under examination included the length, quantity, and distribution of NSM rods, along with the utilization of end anchorages. Compared to non-segmented beam, the first cracking load out and in the interface of SBs was 20-44% and 56-156% greater, respectively. Increasing the length of NSM rods from 10 to 20 times their diameter resulted in improvements in SBs' ultimate load, ultimate deflection, stiffness, and toughness of 55%, 95%, 62%, and 56%, respectively. As a result of the NSM rods' end anchoring, the SB's ultimate load and toughness increased by 22 and 40 %, respectively, in comparison to SB without any anchoring at NSM rod end. The SB's maximum load and stiffness were enhanced by 25% and 31%, respectively, as a result of the tension side's anchor being used at the interface.
Key Words
flexure behavior; near surface mounted; precast concrete; segmental beams; toughness
Address
Sabry Fayed: Department of Civil Engineering, Faculty of Engineering, Kafrelsheikh University, Egypt
Yasin Onuralp Özkiliç: Department of Civil Engineering, Necmettin Erbakan University, 42090 Konya, Turkey
Emrah Madenci: Department of Civil Engineering, Necmettin Erbakan University, 42090 Konya, Turkey; Department of Technical Sciences, Western Caspian University, Baku 1001, Azerbaijan
Jinyan Shi: School of Civil Engineering, Central South University, Changsha 410075, China
Samar Khairy: Department of Civil Engineering, Higher institute for Engineering and Technology, Kafrelsheikh, Egypt
Abstract
This work analyzes the free vibration responses of a functionally graded carbon-nanotube reinforced composite (FGCNTRC) beam supported by Kerr substrates. Four carbon-nanotube (CNT) dispersion patterns following nonlinear forms are considered in the analysis. In the proposed model, the transverse displacement accounts for shear and bending, and the shear strain incorporates a correction function to refine its distribution across the beam's thickness. The mixture rule implements the estimation of the effective material properties. The kinematics is based on an improved first-order shear deformation theory (FSDT) framework. Deriving the governing equations of motion was through the application of Hamilton's principle. The obtained results are compared with those found in the literature to verify the current theory and its accuracy. The parametric
study examines the impact of Kerr foundation parameters, CNT's configuration, and volume fractions, and the exponent degree of nonlinearity on the vibration behaviors of an FG-CNTRC beam.
Address
Qais Gawah, Mazen Anwar Abdullah: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Eastern Province, Saudi Arabia
Mohammed A. Al-Osta: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Eastern Province, Saudi Arabia; Interdisciplinary Research Center for Construction and Building Materials, KFUPM, 31261 Dhahran, Saudi Arabia
Fouad Bourada: Material and Hydrology Laboratory, Faculty of Technology, Civil Engineering Department, University of Sidi Bel Abbes, Algeria
Abdeldjebbar Tounsi: Material and Hydrology Laboratory, Faculty of Technology, Civil Engineering Department, University of Sidi Bel Abbes, Algeria; Mechanical Engineering Department, Faculty of Science and Technology, University of Rélizane, Algeria
Abdelouahed Tounsi: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals,
31261 Dhahran, Eastern Province, Saudi Arabia; Interdisciplinary Research Center for Construction and Building Materials, KFUPM, 31261 Dhahran, Saudi Arabia; Material and Hydrology Laboratory, Faculty of Technology, Civil Engineering Department, University of Sidi Bel Abbes, Algeria
Murat Yaylaci: Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Turkey; Faculty of Turgut Kiran Maritime, Recep Tayyip Erdogan University, 53900, Rize, Turkey
Abstract
In this study, the lateral and longitudinal buckling loads are presented for Functionally Graded Plates (FGPs) with
one end fixed and the other end free. The properties of FGPs are calculated from the mixture rule and power law. Effects of the material order, material index, and size, location, numbers and shape of hole on the buckling loads are investigated. To verify the numerical results, theoretical solutions for some material index values are calculated for longitudinal buckling. Both buckling loads reduce with increased hole sizes, and they reach minimum values when the hole location is the closest to fixed end. Both buckling loads are minimum in plates of square hole, whose hole size is equal to hole dimensions of the other shapes. However,
both buckling loads of plates with triangular holes, whose hole areas are equal, are minimum compared to other plates. Therefore, considering other damage situations in terms of buckling loads, it would be better to prefer FGPs with circular holes. Also, to reduce the processing time, this problem is trained with artificial neural networks (ANN) and the ANN is used to obtain new results for different situations. It is seen that ANN data is compatible with FE results.
