A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm

In this study, a two-stage damage detection procedure using a new approximation of modal strain energy (MSE) value is developed to boost the accuracy of structural damage localization in plate-type structures. The MSE is calculated using the center differential method for both intact and damaged states. The new formulas to approximate MSE are developed based on the nine-node isometric quadrilateral element that eliminates boundary conditions’ effect on damage detection results. The modal assurance criteria (MAC) is used as an indicator to choose the mode shapes with the best damage detection results. A global and a local MSE are proposed to enhance the accuracy of detecting the location of structural damage. An aluminum plate is used to verify the effectiveness of the proposed procedure with several damage scenarios. The results show that the new estimation of MSE value combined with MAC and the two-step MSE procedure can improve the accuracy of identifying the damage location compared to the original MSE procedure.KeywordsDamage detectionModal assurance criteriaModal strain energyPlate-type structureStructural health monitoring

In this paper, a novel two-stage structural damage detection method using modal flexibility and strain energy-based index (MFSEBI) and modified teaching–learning-based optimization (MTLBO) algorithm is proposed. In the first stage, a novel damage index (MFSEBI) is proposed based on the combination of two structural modal properties including modal strain energy of elements and diagonal members of the structural flexibility matrix to identify the suspected damaged elements. The performance of this indicator is compared with the performance of an indicator derived from the modal strain energy. As the change in the structural modal properties is also affected by environmental variations, in this study, the effect of varying environmental conditions on the performance of the MFSEBI is also examined. In the second stage, the modal response of the structure is used and subsequently updated by MLTBO to estimate the damage extents of the suspected elements. At this stage, the MTLBO algorithm is proposed by enhancing the teaching and learning quality of the TLBO algorithm using new updating mechanisms of each learner position. The performance of the MTLBO and TLBO algorithms is compared in terms of the accuracy of the results and convergence rate. The results of three numerical examples indicate a better performance of the MFSEBI index in damage localization using a smaller number of mode shapes. These results also demonstrate the higher accuracy of the estimated damage extents and the faster convergence rate of the objective function using MTLBO, even with the consideration of measurement noise effects.

The paper presents a damage identification methodology utilizing a model order reduction and a two-stage approach for truss structures. For that aim, a second-order Neumann series expansion is employed to condense structural physical characteristics owing to the limitation of a number of degrees of freedom (DOFs) measured by sensors. In the first step of the two-stage method, the location of potentially damaged candidates is determined via a modal strain energy based index (MSEBI), whilst the remaining one aims to accurately calculate the real damage extent via an inverse optimization problem. A recently developed adaptive hybrid evolutionary firefly algorithm (AHEFA) is utilized as an optimizer to resolve such optimization problems. A 31-bar truss is tested to verify the accuracy of the proposed paradigm.

Large-scale space structures, such as telescopes or spacecrafts, require suitable in-situ assembly technologies in order to overcome the limitations on payload size and mass of current launch vehicles. In many application scenarios, manual assembly by astronauts is either highly cost-inefficient or not feasible at all due to orbital constraints. However, (semi-) autonomous robotic assembly systems may provide the means to construct larger structures in space in the near future. Modularity is a key concept for such structures, and also for reducing costs in novel spacecraft designs. The advantage of the modular approach lies in the capability to generate a high number of unique assets from a reduced number of building blocks. Thus, spacecrafts can be easily adapted to particular use cases, and could even be reconfigured during their lifetime using a robotic manipulation system. These ideas lie at the core of our current EU project MOSAR (MOdular Spacecraft Assembly and Reconfiguration). Teleoperating a space robotic system from Earth to assemble $a$ modular structure is not straightforward. Major difficulties are related to time delays, communication losses, limited control modalities, and low immersion for the operator. Autonomous robotic operations are then preferred, and with this goal we propose $a$ fully autonomous system for planning in-space assembly tasks. Our system is able to generate assembly and reconfiguration plans for modular structures in terms of high-level actions that can autonomously be executed by $a$ robot. Through multiple simulation layers, the system automatically verifies the feasibility and correctness of action sequences created by the planner. The layers implement different levels of abstraction, hierarchically stacked to detect infeasible transitions and initiate replanning at an early stage. Levels of abstraction increase in complexity, ranging from a basic geometric description of the spacecraft, over kinematics of the robotic setup, to full representations of the actions. The system reuses information from failed checks in all layers to avoid similar situations during replanning. We use a hybrid approach where symbolic reasoning is combined with considerations of physical constraints to generate a holistic sequence of actions. We demonstrate our planner for large space structures in a simulation environment. In particular, we consider the reconfiguration of a given modular structure, i.e. disassemble parts and reassemble them in a new configuration. The adaptability of our planning system is shown by executing the assembly plans on robots with different sets of skills and in scenarios with simulated hardware failures.

Structural health monitoring (SHM) based on long-term safety and efficacy has attracted much attention. The success of SHM methods depends on the information content of the measurements; in addition, there are challenges in processing SHM in terms of the optimal number and location of sensors. In this study, we provide a convenient method for structural damage detection using vibration data from plane steel frames. We apply 1-D convolutional neural networks (1D-CNNs) to present an influential sensor selection analysis to maintain performance and optimize the number and location of sensors. Finding the damage location based on the optimal number and location of sensors will significantly reduce the detection time and the number of installed sensors. The proposed structural damage detection method uses stress analysis to find positions with large displacements, then divides the detection zones, selects highly discriminate sensors, analyzes the influence of sensor combinations within each zone to achieve optimization of sensor number as well as position selection, and establishes CNN models of the overall structure for damage detection of the joints. The proposed method was practically validated on benchmark data from the Qatar University Grandstand Simulator (QUGS). The experimental results show that the sensor demand rate is only 16.67% compared to the previous optimal number and location of sensors with no restrictions on the mounting position. Moreover, only four CNN models are required to predict damage/undamaged for the entire structure (30 connections). The accuracy of damage detection is improved to 96.62% using sensor combinations with influence.

With the aim to decrease the uncertainties of structural damage detection, two fusion models are presented in this paper. The first one is a weighted and selective fusion method for combing the multi-damage detection methods based on the integration of artificial neural network, Shannon entropy and Dempster-Shafer (D-S) theory. The second one is a D-S based approach for combing the damage detection results from multi-sensors data sets. Numerical study on the Binzhou Yellow River Highway Bridge and an experimental of a 20-bay rigid truss structure were carried out to validate the uncertainties decreasing ability of the proposed methods for structural damage detection. The results show that both of the methods proposed are useful to decrease the uncertainties of damage detection results.

The future space structures are relatively large, flimsy, and lightweight. As a result, they are more easily affected or distortion by space environments compared to other space structures. This study examines the structural integrity of a large scale space structure. A new design of transient temperature field analysis method of the developable reflector on orbit environment is presented, which simulates physical characteristic of developable antenna reflector with a high precision. The different kinds of analysis denote that different thermal elastic characteristics of different materials. The three-dimension multi-physics coupling transient thermal distortion equations for the antenna are founded based on the Galerkins method. For a reflector on geosynchronous orbit, the transient temperature field results from this method are compared with these from NASA. It follows from the analysis that the precision of this method is high. An experimental system is established to verify the control mechanism with IEBIS and thermal sensor technique. The shape control experiments are finished by measuring and analyzing developable tube. Results reveal that the temperature levels of the developable antenna reflector alternate greatly in the orbital period, which is about ±120℃ when considering solar flux ,earth radiating flux and albedo scattering flux.

The [Formula: see text] regularization is usually used to deal with the problems of under-determinacy and measurement noise for the conventional sensitivity-based model updating damage detection methods. However, the [Formula: see text] regularization technique often provides overly smooth solutions and thus cannot exhibit the sparsity of the structural damage due to the promotion of the 2-norm term on smoothness. In the study, a structural damage detection method is proposed based on an improved modal flexibility sensitivity function and an iterative reweighted [Formula: see text] (IR[Formula: see text] regularization. Specifically, the sensitivity function is established by introducing changes in the mode shapes into the derivative of eigenvalue and can be applied to identify the localized damage more accurately. Additionally, IR[Formula: see text] regularization is proposed to deal with the ill-posed problem of damage detection in a noisy environment. The proposed IR[Formula: see text] regularization is compared with the [Formula: see text] and [Formula: see text] regularizations through a numerical and an experimental examples. The numerical and experimental results indicate that the IR[Formula: see text] regularization can more accurately locate and quantify the single and multiple damages under the noise situation. The maximum identification errors are only 5.16% and 5.67%, respectively. Moreover, compared to the basic modal flexibility sensitivity function, the improved function is more sensitive to the damage. The maximum identification error of the improved function is less than 6%, while the relative errors are significantly larger in the basic function.

Conflation of modal strain energy, spectral finite element, and machine learning techniques for damage detection in multi-span steel girder bridges with variable sections

Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

Base on the underground central business district project in the south economic development zone of Changchun, this research studied seismic performance of a large-scale complex underground commercial space structure under the severe earthquake action. A three-dimensio nal dynamic model was established with the finite element program MIDAS/GTS. The response of the main structure was obtained using time history analysis method, including the storey relative displacement and storey drift angle between adjacent structures. The numerical results show that the structure can satisfy the requirements of the seismic performance under the severe earthquake action. The results could be regarded as a valuable reference in design practice.

Attitude control actuator scaling laws for orbiting solar reflectors

Flexible-link robots are an important class of manipulators which uses lightweight arms for assembly, civil infrastructure, bridge/vehicle systems and large-scale space structures. The wide spread of these systems in various application areas has ensured that the modeling of flexible elements and the control of vibration have received a great deal of attention in recent years. In this work it is analyzed a two DOF parallel robot with two flexible links used for pick and place operations, developing a control strategy based on piezoelectric actuators and strain gauges or accelerometers embedded in the links. The analysis of the robot behaviour and the development of the controller, have been carried out on an electromechanical model built in Matlab® and co-simulated with the multibody flexible model built in Adams®. Results show positive confirmation of the study and encourage future tests on the real manipulator to validate the multibody model and the control strategy.

In this chapter, we consider some constructive perturbation methods for solving various control problems which amplify the techniques described in previous chapters. These methods arise from new problems of precise and efficient control of complex engineering systems, such as manipulation robots, large-scale space structures, etc. Control of these systems requires taking account of numerous perturbations of various physical natures: external force fields, the influence of the environment, dynamics of electrical actuators, distributed elasticity of structural elements, interaction of different parts of the system, imperfections of joints, etc.