Structural damage identification based on modified Artificial Bee Colony algorithm using modal data

Ding, Zhenghao; Yao, Renzhi; Li, Jun; Lu, Zhong-Rong

doi:10.1080/17415977.2017.1310855

Cited by 28 publications

(17 citation statements)

References 27 publications

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“…Before applying the proposed ALO-INM algorithm to the SDD problem, the computing performance of the ALO-INM algorithm should be tested and compared with the original ALO algorithm. In order to focus on assessing the local optimization ability of two algorithms, three classical mathematical benchmark functions (Ding et al, 2018; Pan et al, 2016; Yu and Li, 2014), that is, Sphere, Ackley, and composite Griewank as listed in Table 1, are employed for calculation. Among them, Sphere is a unimodal benchmark function, and Ackley is a multimodal benchmark function, while composite Griewank is a composite benchmark function.…”

Section: Methodologies Of Alo-inm Algorithmmentioning

confidence: 99%

“…Before applying the proposed ALO-INM algorithm to the SDD problem, the computing performance of the ALO-INM algorithm should be tested and compared with the original ALO algorithm. In order to focus on assessing the local optimization ability of two algorithms, three classical mathematical benchmark functions (Ding et al, 2018;Pan et al, 2016;Yu and Li, 2014), that is, Sphere, Ackley, and composite Griewank as listed in Each benchmark function is calculated 10 times, then two indices including convergence accuracy and computational efficiency are employed to assess and to compare the computing performance of two algorithms. Particularly, apart from parameters of the INM algorithm, the parameter setting for the ALO-INM algorithm is the same as the ones for the original ALO algorithm.…”

Section: Performance Assessment Of Alo-inm Algorithm Using Benchmark mentioning

confidence: 99%

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A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

Chen

2019

Advances in Structural Engineering

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Structural damage detection is the kernel technique in deploying structural health monitoring. The structural damage–detection technique using heuristic algorithms has been developed at an astounding pace over the past years. However, some existing structural damage–detection methods are prone to easily fall into the local optimum and to be unstable when they are applied to complex structures. In order to make full use of advantages of heuristic algorithms and overcome abovementioned shortcomings, a hybrid algorithm, which combines the ant lion optimizer with an improved Nelder–Mead algorithm, is proposed to solve the constrained optimization problem of complex structural damage detection. First, an objective function is established for damage identification using structural modal parameters, that is, frequencies and mode shapes. The solution to the objective function is accurately attained by a newly improved weighted trace lasso which can improve the computing performance and stability of procedure and reduce randomness of weighted coefficients. After assessing the computing performance of the proposed hybrid algorithm using three classical mathematical benchmark functions, two structural damage–detection numerical simulations and a laboratory verification are then conducted to fully assess the structural damage–detection capability of the proposed method. Meanwhile, the equivalent element stiffness-reduction model is introduced to estimate the real damage severities of cracks which are created in laboratory structures and to compare with the structural damage–detection results by the proposed method. The illustrated results show that the proposed hybrid algorithm can locate damage and quantify damage severity more accurately and stably with a good robustness to noise.

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Section: Methodologies Of Alo-inm Algorithmmentioning

confidence: 99%

“…Before applying the proposed ALO-INM algorithm to the SDD problem, the computing performance of the ALO-INM algorithm should be tested and compared with the original ALO algorithm. In order to focus on assessing the local optimization ability of two algorithms, three classical mathematical benchmark functions (Ding et al, 2018;Pan et al, 2016;Yu and Li, 2014), that is, Sphere, Ackley, and composite Griewank as listed in Each benchmark function is calculated 10 times, then two indices including convergence accuracy and computational efficiency are employed to assess and to compare the computing performance of two algorithms. Particularly, apart from parameters of the INM algorithm, the parameter setting for the ALO-INM algorithm is the same as the ones for the original ALO algorithm.…”

Section: Performance Assessment Of Alo-inm Algorithm Using Benchmark mentioning

confidence: 99%

A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

Chen

2019

Advances in Structural Engineering

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“…Finding the damage in a pipe structure using the eigensolution can be transformed into a complex optimization problem [32,33]. In recent years, iterative techniques based on the swarm intelligence have attracted considerable attention from researchers for solving complicated optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multiple Crack Detection in Thick-walled Pipes Using Artificial Bee Colony Algorithm

Banimahd¹,

Rahemi

2021

Period. Polytech. Civil Eng.

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An analytical method for diagnosis of cracks in thick-walled pipes with a circular hollow section is investigated in this study. In the proposed method, the defect is assumed to be a non-leaking crack, which is modeled by a massless linear spring with infinitesimal length at the crack location. In order to find the cracks in the pipe, the vibration-based method related to the modal properties of the pipe is utilized. In the modal analysis, the mass and stiffness matrices influence the dynamic properties of the pipe. It is assumed that the mass matrix remains unchanged after the crack initiation, while the corresponding stiffness matrix changes. The stiffness matrix of a cracked element can be formulated by the finite element method with two unknown parameters: location and depth of the crack. Using the eigensolution for an undamped dynamic system to formulate the objective function yields to a complicated optimization problem, which can be solved by an iterative numerical optimization method. Among the optimization approaches, the Artificial Bee Colony (ABC) algorithm is a simple and flexible technique for minimizing the objective function. In this paper, the analytical model is utilized to find the size and position of cracks in a pipe using the ABC algorithm and subsequently some numerical examples are examined in order to assess the accuracy of the method. The results show that the proposed method is able to acceptably estimate the location and depth of multiple cracks in the straight pipes as well as curved ones.

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“…The algorithm has been modified to increase the convergence speed many times for constrained and real-parameter optimization problems [4][5]. In recent years, the ABC algorithm has been successfully implemented in civil engineering optimization problems such as optimum design of braced steel frame [6], pavement resurfacing problem [7], the structural damage detection problem [8], evaluation of the compressive strength of concrete specimens using laboratory experiments [9], optimization of the cost of project schedules in construction [10].…”

Section: Introductionmentioning

confidence: 99%

Cost Estimation Models for the Reinforced Concrete Retaining Walls

Dağdeviren

Kaymak

2020

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Betonarme istinat duvarları, karayolu, demiryolu, bina vb. birçok inşaat mühendisliği projesinde inşa edilmektedir. Betonarme istinat duvarlarının tasarımında birçok farklı tasarım kısıtlaması göz önünde bulundurulmalıdır. Geleneksel yaklaşımda, tasarım değişkenleri optimum tasarımı sağlamak için deneme yanılma işlemi ile birçok kez kontrol edilir, bu nedenle proje yöneticileri zamandan tasarruf etmek için optimizasyon teknikleri kullanmak durumundadır. İnşaat mühendisliği projelerindeki diğer bir önemli konu, ihale süreci için inşaat öncesi proje maliyetinin doğru olarak tahmin edilmesidir. Çalışmanın ilk aşamasında, duvar yükseklikleri, sürşarj yükleri ve dolgu zemininin içsel sürtünme açılarının farklı kombinasyonlarında, sağlam zemin tabakasına oturan betonarme istinat duvarı için 125 optimizasyon problemi modifiye yapay arı koloni algoritması kullanılarak analiz edilmiş ve minimum maliyetler belirlenmiştir. Daha sonra, duvarın minimum maliyet tahmini için çoklu regresyon ve yapay sinir ağı modelleri sunulmuştur. Önerilen modellerden elde edilen maliyet tahminleri, modifiye yapay arı koloni algoritması tarafından hesaplanan değerlerle büyük ölçüde uyumludur. Tahmin edilen ve hesaplanan minimum maliyetler arasındaki hata değerleri neredeyse sıfırdır. Sonuçlar, önerilen modellerin, sağlam zemin tabakasına oturan betonarme istinat duvarlarının minimum maliyet tahmini için başarıyla kullanılabileceğini göstermektedir.

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Structural damage identification based on modified Artificial Bee Colony algorithm using modal data

Cited by 28 publications

References 27 publications

A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

Multiple Crack Detection in Thick-walled Pipes Using Artificial Bee Colony Algorithm

Cost Estimation Models for the Reinforced Concrete Retaining Walls

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