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Showing 5 results for Objective Function

H. Mazaheri, H. Rahami, A. Kheyroddin,
Volume 8, Issue 3 (10-2018)
Abstract

Structural damage detection is a field that has attracted a great interest in the scientific community in recent years. Most of these studies use dynamic analysis data of the beams as a diagnostic tool for damage. In this paper, a massless rotational spring was used to represent the cracked sections of beams and the natural frequencies and mode shape were obtained. For calculation of rotational spring stiffness equivalent of uncracked and cracked sections, finite element models and experimental test were used. The damage identification problem was addressed with two optimization techniques of different philosophers: ECBO, PSO and SQP methods. The objective functions used in the optimization process are based on the dynamic analysis data such as natural frequencies and mode shapes. This data was obtained by developing a software that performs the dynamic analysis of structures using the Finite Element Method (FEM). Comparison between the detected cracks using optimization method and real beam shows an acceptable agreement.
S. M. Hosseini, Gh. Ghodrati Amiri, M. Mohamadi Dehcheshmeh,
Volume 10, Issue 1 (1-2020)
Abstract

Civil infrastructures such as bridges and buildings are prone to damage as a result of natural disasters. To understand damages induced by these events, the structure needs to be monitored. The field of engineering focusing on the process of evaluating the location and the intensity of the damage to the structure is called Structural Health Monitoring (SHM). Early damage prognosis in structures is the fundamental part of SHM. In fact, the main purpose of SHM is obtaining information about the existence, location, and the extent of damage in the structure. Since numerous structural damage detection problems can be solved as an inverse problem based on the proposed objective functions by using optimization algorithm, in this paper, related studies are investigated which discussing objective functions based on Modal Strain Energy (MSE) and flexibility methods including Modal Flexibility (MF), and Generalized Flexibility Matrix (GFM). To illustrate the extent of effectiveness of these objective functions based on the above-mentioned modal parameters, an efficiency index called Impact Factor (IF) is defined. Finally, the best objective function is introduced for each numerical case study based on IF by means of evaluating the obtained result.
A. Ghadimi Hamzehkolaei, A. Vafaeinejad, G. Ghodrati Amiri,
Volume 11, Issue 3 (8-2021)
Abstract

This paper presents an optimization-based model updating approach for structural damage detection and quantification. A new damage-sensitive objective function is proposed using a condensed form of the modal flexibility matrix. The objective function is solved using Chaotic Imperialist Competitive Algorithm (CICA), as an enhanced version of the original Imperialist Competitive Algorithm (ICA), and the optimal solution is reported as the damage detection results. The application of the CICA in vibration-based damage detection and quantification has been successfully investigated in a feasibility study published by the authors of the present paper and herein, its application is generalized for a case in which a complex (but more sensitive) objective function is utilized to formulate the damage detection problem as an inverse model updating problem. The method is validated by studying different damage patterns simulated on three numerical examples of the engineering structures. Comparative studies are carried out to evaluate the accuracy and repeatability of the proposed method in comparison with other vibration-based damage detection methods. The obtained results introduce the proposed damage detection approach as a robust method with high level of accuracy even in the presence of noisy inputs.
M. Danesh, A. Iraji , S. Jaafari,
Volume 11, Issue 4 (11-2021)
Abstract

The main object in optimizing reinforced concrete frames based on the performance is decreasing the initial cost or life cycle cost or total cost. The optimization performed here is with the requirement of satisfying story drifts and rotation of plastic hinges. However, this optimization may decrease seismic strength of the structure. Newton Meta-Heuristic Algorithm (NMA) was used to optimize three-, six-, and twelve-story reinforced concrete frames based on the performance and utilizing the cost objective function. The seismic parameters of the optimized frames were calculated. The results showed that the inter-story drifts at the performance level of LS controls the design. According to the results, the objective function for construction cost is not useful for the optimization of the reinforced concrete frames. Because the amounts of the over strength, the absorbed plastic energy, and the ductility factor for the optimized frames are low using the objective function for the construction cost.
F. Biabani, A. Razzazi, S. Shojaee, S. Hamzehei-Javaran,
Volume 12, Issue 3 (4-2022)
Abstract

Presently, the introduction of intelligent models to optimize structural problems has become an important issue in civil engineering and almost all other fields of engineering. Optimization models in artificial intelligence have enabled us to provide powerful and practical solutions to structural optimization problems. In this study, a novel method for optimizing structures as well as solving structure-related problems is presented. The main purpose of this paper is to present an algorithm that addresses the major drawbacks of commonly-used algorithms including the Grey Wolf Optimization Algorithm (GWO), the Gravitational Search Algorithm (GSA), and the Particle Swarm Optimization Algorithm (PSO), and at the same time benefits from a high convergence rate. Also, another advantage of the proposed CGPGC algorithm is its considerable flexibility to solve a variety of optimization problems. To this end, we were inspired by the GSA law of gravity, the GWO's top three search factors, the PSO algorithm in calculating speed, and the cellular machine theory in the realm of population segmentation. The use of cellular neighborhood reduces the likelihood of getting caught in the local optimal trap and increases the rate of convergence to the global optimal point. Achieving reasonable results in mathematical functions (CEC 2005) and spatial structures (with a large number of variables) in comparison with those from GWO, GSA, PSO, and some other common heuristic algorithms shows an enhancement in the performance of the introduced method compared to the other ones.
 

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