Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization

Nanda, Bharadwaj; Maity, Damodar; Maiti, Dipak Kumar

doi:10.1016/j.amc.2014.05.115

Cited by 33 publications

(17 citation statements)

References 27 publications

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“…Parameter identification using RM has employed various optimization algorithms such as genetic algorithms (Chou and Ghaboussi, 2001) and particle-swarm optimization (Nanda et al, 2014). RM has also been performed by trial and error to estimate optimal parameters (Sanayei et al, 2012).…”

Section: Residual Minimization (Rm)mentioning

confidence: 99%

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

Drira

Pai

Smith

2021

Front. Built Environ.

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In sensed buildings, information related to occupant movement helps optimize functions such as security, energy management, and caregiving. Due to privacy needs, non-intrusive sensing approaches for tracking occupants inside buildings, such as vibration sensors, are often preferred over intrusive strategies that involve use of cameras and wearable devices. Current sensor-based occupant-localization approaches are data-driven techniques that do not account for structural behavior and limited to slabs on grade. Varying-rigidity floors and inherent variability in walking gaits lead to ambiguous interpretations of floor vibrations when performing model-free occupant localization. In this paper, an extensive analysis of vibrations induced by a range of occupants is described. Firstly, the need for a structural-behavior model for occupant localization is assessed using two full-scale case studies. Structural behavior is found to significantly influence floor vibrations induced by footstep impacts. Since a simple relationship between distances from footstep-impact to sensor locations cannot be assured, the use of physics-based models is necessary for accurate occupant localization. Secondly, measured data are interpreted using physics-based models and information related to uncertainties from multiple sources. There are two types of uncertainties: modelling uncertainties and measurement uncertainties, including variability in walking gaits. Error-domain model falsification (EDMF) and residual minimization (RM) are model-based approaches for data interpretation. Unlike RM, EDMF explicitly accounts for the presence of systematic errors in parameters and overall model bias. In this paper, model-based occupant localization is carried out using EDMF and RM on a full-scale case study. By explicitly accounting for the presence of uncertainties and the influence of structural behavior, EDMF, unlike RM, accurately reveals possible occupant locations on floor slabs.

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Section: Residual Minimization (Rm)mentioning

confidence: 99%

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

Drira

Pai

Smith

2021

Front. Built Environ.

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“…Consequently, the heuristic approaches have gained popularity in the past few years. Several swarmbased heuristic approaches, such as the genetic algorithm (GA) [18][19][20][21][22], simulated annealing (SA) [23,24], tabu search [25], standard particle swarm optimisation (SPSO) [26][27][28][29], unified particle swarm optimisation (UPSO) [30,31], artificial bee colony (ABC) [32,33], ant colony optimisation (ACO) [34][35][36], charged system search [37], and ant lion optimisation [38], as well as their hybrid versions [39][40][41][42] have been used in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring based on the hybrid ant colony algorithm by using Hooke–Jeeves pattern search

et al. 2019

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Structural health monitoring is crucial for the timely damage diagnosis of civil infrastructure. This paper explores the damage detection method based on the ant colony algorithm (ACO) by using Hooke-Jeeves (HJ) pattern search for intensification. The HJ is incorporated into the ACO to improve its performance in detecting damages. The damage is simulated by reducing the stiffness of the structural members, via elastic modulus reduction factor. Four civil engineering structures of varying complexity are analysed for low-and high-level damage scenarios to test the efficacy of the proposed approach. An inverse problem is formulated to minimise the objective function based on the frequency response function rather than using the frequency and mode-shape-based approach. The analysis results indicate that the proposed method can locate damages and identify their severity with higher precision than previously used GA, SPSO, and UPSO can.

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“…For solving the optimization problem, meta-heuristic optimization algorithms as intelligent search methods have been widely adopted. Many researchers successfully applied meta-heuristic search algorithms such as genetic algorithm (GA) [4], particle swarm optimization (PSO) [5], dierential evolution (DE) [6], imperialist competitive algorithm (ICA) [7] and ant colony optimization (ACO) [8] in the structural damage identication. More recently, Xu et al [9] studied a method based on cuckoo search (CS) algorithm for structural damage detection.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

Dinh-Cong

Pham-Duy²,

Nguyen-Thoi³

2018

J. ADV. ENG. COMPUT.

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The article presents an effective method for damage assessment of 2D frame structures using incomplete modal data by optimization procedure and model reduction technique. In this proposed method, the structural damage detection problem is defined as an optimization problem, in which a hybrid objective function and the damage severity of all elements are considered as the objective function and the continuous design variables, respectively. The teaching-learning-based optimization (TLBO) algorithm is applied as a powerful optimization tool to solve the problem. In addition, owing to the use of incomplete measurements, an improved reduction system (IRS) technique is adopted to reduce the mass and stiffness matrices of structural finite element model. The efficiency and robustness of the proposed method are validated with a 4-storey (3 bay) steel plane frame involving several damage scenarios without and with measurement noise. The obtained results clearly demonstrate that even the incompleteness and noisy environment of measured modal data, the present method can work properly in locating and estimating damage of the frame structure by utilizing only the first five incomplete modes' data. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization

Cited by 33 publications

References 27 publications

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

Structural health monitoring based on the hybrid ant colony algorithm by using Hooke–Jeeves pattern search

Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

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