A multi-objective optimization algorithm for Bouc–Wen–Baber–Noori model to identify reinforced concrete columns failing in different modes

2021 ◽  
pp. 146442072110200
Author(s):  
Zele Li ◽  
Mohammad Noori ◽  
Ying Zhao ◽  
Chunfeng Wan ◽  
Decheng Feng ◽  
...  
Keyword(s):  
Experimental Data ◽  
Reinforced Concrete ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Concrete Columns ◽  
Multi Objective Optimization ◽  
Hysteresis Model ◽  
Restoring Force ◽  
Rc Columns ◽  
Multi Objective

Concrete columns are the most important load-bearing components in civil structures. The potential damage in reinforced concrete (RC) columns could be categorized into three different failure modes: flexural shear (FS) failure, flexural-failure (FF), and shear failure (SF). The corresponding hysteresis loops for each mode differ significantly. Therefore, a multi-parameter hysteretic restoring force model is needed to describe the hysteretic energy dissipation phenomenon and behavior. Identification of the optimal parameter values of a multi-parameter hysteresis model of RC columns under different failure modes is essential in the evaluation of structural inelastic seismic performance. In this paper, a multi-objective optimization algorithm called NSGA-II is employed to identify the parameters of Bouc–Wen–Baber–Noori model (BWBN) hysteresis model, this model has been used for describing the response and modelling restoring force behavior in several structural and mechanical engineering systems, that can fully describe the hysteretic restoring force characteristics of RC columns. An objective function for the restoring force is proposed to identify the parameters of BWBN model. In order to ensure the accuracy of identification, based on the sensitivity analysis, the parameters distribution law of RC columns in different failure modes is obtained. Furthermore, the reference values under different failure modes are proposed. The results presented in this paper will significantly reduce the calculation of subsequent identification. Twelve groups of experimental data are randomly selected to verify the feasibility of the above algorithm. It is demonstrated that using the multi-objective optimization algorithm leads to better identification accuracy with minimum prior experience. Performance of the algorithm is verified using simulated and experimental data. The experimental data of the RC columns were collected from the database of the Pacific Earthquake Engineering Research Center.

Seismic assessment of reinforced concrete columns with short lap splices

2021 ◽  
pp. 875529302199483
Author(s):  
Eyitayo A Opabola ◽  
Kenneth J Elwood
Keyword(s):  
Reinforced Concrete ◽  
Failure Mode ◽  
Failure Modes ◽  
Concrete Columns ◽  
Seismic Assessment ◽  
Rc Columns ◽  
Lap Splices ◽  
Displacement Response ◽  
Force Displacement ◽  
Probable Failure

Existing reinforced concrete (RC) columns with short splices in older-type frame structures are prone to either a shear or bond mechanism. Experimental results have shown that the force–displacement response of columns exhibiting these failure modes are different from flexure-critical columns and typically have lower deformation capacity. This article presents a failure mode-based approach for seismic assessment of RC columns with short splices. In this approach, first, the probable failure mode of the component is evaluated. Subsequently, based on the failure mode, the force–displacement response of the component can be predicted. In this article, recommendations are proposed for evaluating the probable failure mode, elastic rotation, drift at lateral failure, and drift at axial failure for columns with short splices experiencing shear, flexure, or bond failures.

SETTING A DIAGRAM APPROACH TO CALCULATING VIBRATED, CENTRIFUGED AND VIBROCENTRIFUGED REINFORCED CONCRETE COLUMNS WITH A VARIATROPIC STRUCTURE

2020 ◽  
pp. 22-34
Author(s):  
Л. Р. Маилян ◽  
С. А. Стельмах ◽  
Е. М. Щербань ◽  
М. П. Нажуев
Keyword(s):  
Experimental Data ◽  
Reinforced Concrete ◽  
Bearing Capacity ◽  
Cross Section ◽  
Concrete Columns ◽  
Reinforced Concrete Columns ◽  
The Cross ◽  
Concrete Elements ◽  
Diagram Approach

Состояние проблемы. Железобетонные элементы изготавливаются, как правило, по трем основным технологиям - вибрированием, центрифугированием и виброцентрифугированием. Однако все основные расчетные зависимости для определения их несущей способности выведены, исходя из основного постулата - постоянства и равенства характеристик бетона по сечению, что реализуется лишь в вибрированных колоннах. Результаты. В рамках диаграммного подхода предложены итерационный, приближенный и упрощенный способы расчета несущей способности железобетонных вибрированных, центрифугированных и виброцентрифугированных колонн. Выводы. Расчет по диаграммному подходу показал существенно более подходящую сходимость с опытными данными, чем расчет по методике норм, а также дал лучшие результаты при использовании дифференциальных характеристик бетона, чем при использовании интегральных и, тем более, нормативных характеристик бетона. Statement of the problem. Reinforced concrete elements are typically manufactured according to three basic technologies - vibration, centrifugation and vibrocentrifugation. However, all the basic calculated dependencies for determining their bearing capacity were derived using the main postulate, i.e., the constancy and equality of the characteristics of concrete over the cross section, which is implemented only in vibrated columns. Results. Within the framework of the diagrammatic approach, iterative, approximate and simplified methods of calculating the bearing capacity of reinforced concrete vibrated, centrifuged and vibrocentrifuged columns are proposed. Conclusions. The calculation according to the diagrammatic approach showed a significantly better convergence with the experimental data than that using the method of norms, and also performs better when using differential characteristics of concrete than when employing integral and particularly standard characteristics of concrete.

scholarly journals Experimental Research on Reinforced Concrete Columns Strengthened with Steel Jacket and Concrete Infill

2021 ◽  
Vol 11 (9) ◽  
pp. 4043
Author(s):  
Aleksandar Landović ◽  
Miroslav Bešević
Keyword(s):  
Compressive Strength ◽  
Reinforced Concrete ◽  
Cross Section ◽  
Experimental Research ◽  
Failure Modes ◽  
Steel Tube ◽  
Rc Columns ◽  
Rc Column ◽  
Steel Jacket ◽  
Ductile Behavior

Experimental research on axially compressed columns made from reinforced concrete (RC) and RC columns strengthened with a steel jacket and additional fill concrete is presented in this paper. A premade squared cross-section RC column was placed inside a steel tube, and then the space between the column and the tube was filled with additional concrete. A total of fourteen stub axially compressed columns, including nine strengthened specimens and five plain reinforced concrete specimens, were experimentally tested. The main parameter that was varied in the experiment was the compressive strength of the filler concrete. Three different concrete compression strength classes were used. Test results showed that all three cross-section parts (the core column, the fill, and the steel jacket) worked together in the force-carrying process through all load levels, even if only the basic RC column was loaded. The strengthened columns exhibited pronounced ductile behavior compared to the plain RC columns. The influence of the test parameters on the axial compressive strength was investigated. In addition, the specimen failure modes, strain development, and load vs. deformation relations were registered. The applicability of three different design codes to predict the axial bearing capacity of the strengthened columns was also investigated.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

scholarly journals An effective docking strategy for virtual screening based on multi-objective optimization algorithm

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Honglin Li ◽  
Hailei Zhang ◽  
Mingyue Zheng ◽  
Jie Luo ◽  
Ling Kang ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Optimization Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective
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