A modified Artificial Bee Colony algorithm for structural damage identification under varying temperature based on a novel objective function

2020 ◽  
Vol 88 ◽  
pp. 122-141 ◽  
Author(s):  
Zhenghao Ding ◽  
Kangsheng Fu ◽  
Wu Deng ◽  
Jun Li ◽  
Lu Zhongrong
Keyword(s):  
Objective Function ◽  
Structural Damage ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Damage Identification ◽  
Bee Colony ◽  
Structural Damage Identification

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

2020 ◽  
Vol 20 (11) ◽  
pp. 2050124
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Łukasz Jankowski ◽  
Haibin Zhang
Keyword(s):  
Data Fusion ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Identification Accuracy ◽  
Fe Model ◽  
Local Damage ◽  
Approximate Models ◽  
Structural Damage Identification

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

scholarly journals Green Product Module Partition Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

2019 ◽  
Vol 301 ◽  
pp. 00021
Author(s):  
Wei Wei ◽  
Yang Zhan
Keyword(s):  
Life Cycle ◽  
Objective Function ◽  
Correlation Matrix ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Green Product ◽  
Multi Objective ◽  
Bee Colony ◽  
Axiomatic Design Theory ◽  
Module Partition

Modular design is an important design method in the mass customization for manufacturing industry. The purpose of this paper is to meet diverse market demands while reducing the impact of products on the ecological environment. Firstly, aiming at the product life cycle process, this paper summarizes the problems encountered in each stage of the product, and introduces five green product module partition principles. Then, through the component correlation matrix, the resource greenness objective function based on the whole life cycle and the polymerization degree objective function based on the component correlation matrix are established respectively by the axiomatic design theory which makes the product mapping from functional domain to structural domain. Next, an improved artificial bee colony algorithm is proposed. Based on the artificial bee colony algorithm, the algorithm applies congestion strategy and fast nondominated sorting strategy to solve the module partition problem of product platform with multi-objective optimization, and a uniformly distributed pare to solution set is generated. Through above steps, the optimization results of module partition are obtained. Finally, an application example of aircraft tail horizontal stabilizer parts is given, and the advantages of the algorithm are proved by comparing with other algorithms.

Structural Damage Identification Based on Model Updating

2006 ◽  
Author(s):  
T. Yin ◽  
L. Yu ◽  
H. P. Zhu
Keyword(s):  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Frame Structure ◽  
Mode Shapes ◽  
Fe Model ◽  
Portal Frame ◽  
Structural Damage Identification ◽  
Portal Frame Structure

This paper presents a new method for structural damage identification based on the finite element (FE) model updating techniques. First, an objective function is defined as minimizing the sum of differences between the experimental and analytical modal data (natural frequencies and mode shapes), which is set as a nonlinear least-squares problem with bound-constrains. The trust-region approach is then used to solve the minimization problem in order to make this optimization process more robust and reliable. In addition, the expansion and weighting of the original objective function are investigated so that the presented method can be well applied into the damage identification of more real structures. Finally, a numerical simulation model of two-story portal frame structure is adopted to evaluate the efficiency of the proposed technique when both the single and multiple damage cases are set up in the model. Some important issues are also discussed in this paper. The illustrated results show that the single and multiple damages on the two-story portal frame structure can be well identified by the proposed method.

scholarly journals Investigation of optimal designs for concrete cantilever retaining walls in different soils

2020 ◽  
Vol 11 (2) ◽  
pp. 39 ◽  
Author(s):  
Esra Uray ◽  
Serdar Çarbaş ◽  
İbrahim Hakkı Erkan ◽  
Murat Olgun
Keyword(s):  
Objective Function ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Retaining Wall ◽  
Carbon Dioxide Emission ◽  
Retaining Walls ◽  
Optimization Method ◽  
Stability Conditions ◽  
Bee Colony ◽  
Cantilever Retaining Walls

In this paper, the investigation of the optimum designs for two types of concrete cantilever retaining walls was performed utilizing the artificial bee colony algorithm. Stability conditions like safety factors sliding, overturning and bearing capacity and some geometric instances due to inherent of the wall were considered as the design constraints. The effect of the existence of the key in wall design on the objective function was probed for changeable properties of foundation and backfill soils. In optimization analysis, wall concrete weight which directly affect parameters such as carbon dioxide emission and the cost was considered as the objective function and analyzes were performed according to different discrete design variables. The optimum concrete cantilever retaining wall designs satisfying constraints of stability conditions and geometric instances were obtained for different soil cases. Optimum designs of concrete cantilever retaining wall with the key were attained in some soil cases which were not found the feasible optimum solution of the concrete cantilever retaining wall. Results illustrate that the artificial bee colony algorithm was a favorable metaheuristic optimization method to gain optimum designs of concrete cantilever retaining wall.

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