Hesheng Tang

Identification of Structural Systems Using Particle Swarm Optimization

Abstract Particle swarm optimization (PSO) is a new heuristic method that has yielded promising results for solving complex optimization problems. Its advantages are a simple structure, ease of use, quality of solution, and robustness. This paper utilizes the PSO algorithm for parameter estimation of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. Simulation results for identifying the parameters of multiple degree-of-freedom (DOF) linear and nonlinear structural systems are presented to demonstrate the effectiveness of the proposed method.

Parameter estimation using a CLPSO strategy

As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications for solving complex optimization problems in different fields mainly for various continuous optimization problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper utilizes an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimization problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.

Optimum Design of Truss Structures Based on Differential Evolution Strategy

A novel optimization method based on a Differential Evolution (DE) strategy for designing low-weight truss structures is presented for both continuous and discrete variables. Applications of this technique on the optimization of a benchmark-type truss structure with continuous and discrete variables are given to evaluate its effectiveness. Results are compared with various classical and evolutionary optimization methods which show that the proposed procedure based on DE outperforms other methods and can effectively be applied to the optimization problems of truss structures with both continuous and discrete design variables.

Structural Damage Detection Using Auxiliary Particle Filtering Method

Structural damage identification is an important objective of health monitoring for civil infrastructures. Frequently, damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, an auxiliary particle filtering (APF) method is applied to track a dynamic system with sudden parameter changes. In the APF, the importance density is proposed as a mixture density that depends upon the past state and the most recent observations, and hence which has a good time-tracking ability that is more suitable for tracking the nonstationary system than the conventional particle filters. Simulation results for tracking the sudden parameter changes of nonlinear hysteretic structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting the structural damages.Structural damage identification is an important objective of health monitoring for civil infrastructures. Frequently, damage to a structure may be reflected by a change of some system parameters, such as a degradation of the stiffness. In this paper, an auxiliary particle filtering (APF) method is applied to track a dynamic system with sudden parameter changes. In the APF, the importance density is proposed as a mixture density that depends upon the past state and the most recent observations, and hence which has a good time-tracking ability that is more suitable for tracking the nonstationary system than the conventional particle filters. Simulation results for tracking the sudden parameter changes of nonlinear hysteretic structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting the structural damages.

Dynamics of Real Structure in Fresh, Damaged and Reinforced States in Comparison with Shake Table and Simulation Models

Abstract The dynamics of a real three-story structure were studied based on changes of its natural frequency when beams and braces were removed, to simulate damage, and returned, to simulate reinforcement. Total 81 steps simulating different structural states were adopted for these testes. For comparison, laboratory shake table experiments were performed with 1/20-scale models, for the same 81 steps. In addition, numerical simulations of the real structure were also carried out over the same 81 steps for comparison. The change in natural frequency for the three methods, together with the influence of temperature and humidity, showed interesting tendencies, which prove important and meaningful for the development of structural health monitoring systems using dynamic data.

Auxiliary particle filtering for structural system identification

The most common choice of importance density is the transition prior density function for particle filter (it is also known as SIR filter, Monte Carlo filter, Bayesian bootstrap filter, condensation, etc.), since it is intuitive and simple to implement, but using the prior as the importance density suffers from drawback of without any knowledge of the observations, and hence the state space is explored without direct knowledge of the observations, maybe lead to poor performance for the particle filtering. To accomplish this, it is necessary to incorporate the current observation in the importance density. In this paper, we propose an auxiliary particle filter (APF) method to identify a non-stationary dynamic system with abrupt change of system parameters. In the APF, the importance density is proposed as a mixture density that depends upon the past state and the most recent observations, and hence which has a good time tracking ability is more suitable for tracking the non-stationary system than the conventional particle filters. The numerical simulations confirm effectiveness of the proposed method for the structural system identification.

Multi-objective differential evolution for truss design optimization with epistemic uncertainty:

A robust multi-objective optimization method for truss optimum design is presented. In the robust design, materials and loads are assumed to be affected by epistemic uncertainties (imprecise or lack of knowledge). Uncertainty quantification using evidence theory in optimum design subject to epistemic uncertainty is undertaken. In addition to a functional objective, an evidence-based plausibility measure of failure of constraint satisfaction is minimized to formulate the robust design into a multi-objective optimization problem. In order to alleviate the computational difficulties in the evidence theory-based uncertainty quantification analysis, a combined strategy of differential evolution-based interval optimization method and parallel computing technique is proposed. A population-based multi-objective differential evolution optimization algorithm is designed for searching robust Pareto front. Two truss structures with shape and sizing optimum design problems are presented to demonstrate the effectiveness and applicability of the proposed method.

Uncertainty quantification using evidence theory in concrete fatigue damage prognosis

Fatigue failure is the main failure mode of mechanical components in the research of engineering structures. As fatigue life may be a basis for the fatigue reliability design, it is very important to predict it for the normal usage of the structure. Uncertainties rooted in physical variability, data uncertainty and modeling errors of the fatigue life prediction analysis. Furthermore, the predicted life of concrete structures in civil engineering field will be more obviously uncertain than other engineering structures. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, there are limitations in using only one framework (probability theory) to quantify the uncertainty in the concrete fatigue life prediction problem because of the impreciseness of data or knowledge. Therefore the study of uncertainty theory in the prediction of fatigue life is very necessary. This study explores the use of evidence theory for concrete fatigue life prediction analysis in the presence of epistemic uncertainty. The empirical formula S-N curve and the Paris law based on the fracture mechanics are selected as the fatigue life prediction models. The evidence theory is used to quantify the uncertainty present in the models parameters. The parameters in fatigue damage prognosis model are obtained by fitting the available sparse experimental data and then the uncertainty in these parameters is taken into account. In order to alleviate the computational difficulties in the evidence theory based uncertainty quantification (UQ) analysis, a differential evolution (DE) based interval optimization method is used for finding the propagated belief structure. The object of the current study is to investigate uncertainty of concrete fatigue damage prognosis using sparse experimental data in order to explore the feasibility of the approach. The proposed approach is demonstrated using the experimental results of the plain concrete beams and the steel fibred reinforced concrete beams.

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

In this paper, an adaptive immune clone selection algorithm for multi-objective optimization (AICSAMO) is proposed. A novel adaptive polynomial mutation operator with dynamic mutation probability is employed in AICSAMO. This adaptive mutation operator executes a rapid global search at the earlier stage of the algorithm and a fine-tuning search at the later stage of the algorithm, which adopts generation-dependent parameters to improve the convergence speed and global optimum searching ability. The effectiveness of AICSAMO is evaluated through the truss sizing and shape optimization problems of a 10-bar plane truss and a 25-bar space truss. According to the comparison of AICSAMO with various multi-objective optimization algorithms developed recently, the simulation results illustrate that AICSAMO has remarkable performance in finding a wider spread of optimal solutions and in maintaining better uniformity of the solutions with better convergence.

Performance Study of an Eight-story Steel Building Equipped with Oil Dampers Damaged During the 2011 Great East Japan Earthquake Part 1: Structural Identification and Damage Reasoning

Abstract Oil dampers installed on the first floor of an eight-story steel building were completely destroyed during the 2011 Great East Japan Earthquake. It is believed to be the first time in the world that real oil dampers in service failed due to earthquakes. Before this failure event, the actual performance of buildings that use oil dampers during catastrophic earthquakes has never been verified. Investigating the cause of the damage of the oil dampers is thus necessary and urgent. In this paper, a comprehensive identification was conducted to rebuild the numerical model of this damped structure equipped with/without damaged oil dampers using the measurement data of the installed monitoring system. Furthermore, the damage process of the oil dampers was postulated based on the identification and simulation results. The limit states of the oil dampers were studied. Based on the damages of the dampers and connection, the oil dampers experienced the displacement limit state when the allowable displacement limit was surpassed and the central cylinder pushed against the abutment. The insufficient stroke limit is the main cause of the collision between the damper and the abutment on the floor, which finally led to the failure of the oil dampers.