A fast-convergent approach for damage assessment using CMA-ES optimization algorithm and modal parameters

Ghahremani, Behzad; Bitaraf, Maryam; Rahami, H.

doi:10.1007/s13349-020-00397-1

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…Typically, physics-based methods rely on finite element (FE) models that may or may not be updated with field data [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Yu et al [ 26 ] created a FE model of the Aizhai suspension bridge to detect damage and the model was updated using the information collected from 112 sensors of various types.…”

Section: Introductionmentioning

confidence: 99%

“…Giagopoulos et al [ 30 ] proposed a framework for fatigue damage identification using operational experimental measurements and a high-fidelity FE model. Ghahremani et al [ 31 ] applied an evolutionary optimization algorithm to update numerical FE models of truss and frame structures. Schommer et al [ 32 ] investigated SHM on a pre-stressed concrete beam based on the created FE model and static and dynamic responses.…”

Section: Introductionmentioning

confidence: 99%

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Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis

Ghahremani

Enshaeian

Rizzo

2022

Sensors

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This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis

Ghahremani

Enshaeian

Rizzo

2022

Sensors

Self Cite

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“…Aval and Mohebian [62] detected member and joint damage in plane truss and plane frame using improved biology migration algorithm. Ghahremani et al [63] utilized covariance matrix adaptation-evolution strategy (CMA-ES) method to detect damages in a plane truss and a plane frame structure and compared its performance with GA and PSO. Recently, Barman et al [64] compared the damage estimation capacity of several variants of swarm-based search algorithms for truss structures.…”

Section: Literature Reviewmentioning

confidence: 99%

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Kundu

Mishra

Maity

2021

Archit. Struct. Constr.

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Optimization algorithms are primarily responsible for efficiency in vibration-based damage detection particularly when utilizing the inverse approach. A complex problem of damage detection tends to converge into local minima, generated by a false damaged state which produces a response that is almost similar to the actual damaged state. Hence, there is a need for an efficient and accurate soft computing technique that can find the global minima or the actual damaged state. Recently, the teaching-learning based optimization (TLBO) algorithm has become quite popular due to its superior performance especially when compared to other metaheuristic algorithms. In this paper, damage estimation capability of the TLBO for frame structures and a benchmark problem of cantilever beam is studied and comparisons are made with some established soft computing techniques. TLBO is observed to produce better results relative to the other artificial intelligence-based techniques used for structural health monitoring.

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“…It is assumed that damage reduces stiffness of the elements, and the mass is constant during the life-cycles (Ghahremani et al, 2020). Stiffness of damaged structures with nel number of elements and damage parameter d i ( i = 1, 2, …, nel ) are formulated by equation (1)…”

Section: Theoretical Backgroundmentioning

confidence: 99%

An effective method for damage assessment based on limited measured locations in skeletal structures

Ghannadi

Kourehli

2020

Advances in Structural Engineering

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This article proposes a new damage detection method using Modal Test Analysis Model and artificial neural networks. A challenge in damage detection problems is lack of measured degrees of freedom, as well as limitations of attached sensors. Modal Test Analysis Model has been used in order to estimate unmeasured degrees of freedom. An experimental cantilever beam was used to show Modal Test Analysis Model’s efficiency in estimation of unmeasured mode shapes. To solve the inverse problem of damage detection, mode shapes estimated by Modal Test Analysis Model were used as inputs, and characteristics of the damage served as outputs of the artificial neural network. The sensitivity analysis carried out for each example showing the performance of artificial neural network after mode shape expansion was efficiently improved. Three numerical examples for plane and space truss structures are considered, in order to verify effectiveness of the proposed method. Results demonstrate a high accuracy of Modal Test Analysis Model and artificial neural network for structural damage detection.

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A fast-convergent approach for damage assessment using CMA-ES optimization algorithm and modal parameters

Cited by 17 publications

References 38 publications

Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis

Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

An effective method for damage assessment based on limited measured locations in skeletal structures

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