Data-driven approaches for measurement interpretation: analysing integrated thermal and vehicular response in bridge structural health monitoring

A comprehensive evaluation of a structure’s performance based on quasi-static measurements requires consideration of the response due to all applied loads. For the majority of short- and medium-span bridges, temperature and vehicular loads are the main drivers of structural deformations. This paper therefore evaluates the following two hypotheses: (i) knowledge of loads and their positions, and temperature distributions can be used to accurately predict structural response, and (ii) the difference between predicted and measured response at various sensor locations can form the basis of anomaly detection techniques. It introduces a measurement interpretation approach that merges the regression-based thermal response prediction methodology that was proposed previously by the authors with a novel methodology for predicting traffic-induced response. The approach first removes both environmentally (temperature) and operationally (traffic) induced trends from measurement time series of structural response. The resulting time series is then analysed using anomaly detection techniques. Experimental data collected from a laboratory truss is used for the evaluation of this approach. Results show that (i) traffic-induced response is recognized once thermal effects are removed, and (ii) information of the location and weight of a vehicle can be used to generate regression models that predict traffic-induced response. As a whole, the approach is shown to be capable of detecting damage by analysing measurements that include both vehicular and thermal response.     

A swift technique for damage detection of determinate truss structures (2)

This paper introduces a novel and robust probable statistical approach for the applied damage detection of determinate truss structures. This technique involves two steps; the first is called most probable damaged element identification step and the second is called probable damage severity prediction step. In the first step, a new index based on modal residual forces plays a major role to independently identify damage-suspected elements for each considered mode. Then among them, the elements, the most probable to damage, are extracted. In the second step, the probable damage severity for each most probable damaged element is individually predicted using a novel statistical approach. Finally, to justify the validity and robustness of the technique, three commonly used bridge trusses including a 29-bar Pratt truss, a 29-bar Warren truss, and finally, a 37-bar K truss under different damage scenarios are thoroughly studied while their modal parameters are corrupted by noise. The obtained results indicate that the method is innovatively capable of swiftly predicting, for determinate truss structures, not only damaged elements but also their damage severities by carrying out solely few structural analyses.

     

Identification of coexistent load and damage

Load reconstruction and damage identification are crucial problems in structural health monitoring. However, it seems there is not much investigation on identification of coexistent load and damage, although in practice they usually exist together. This paper presents a methodology to solve this problem based on the Virtual Distortion Method. A damaged structure is modeled by an equivalent intact structure subjected to the same loads and to virtual distortions which model the damages. The measured structural response is used to identify the loads, the distortions and to recover the stress-strain relationship of the damaged elements. This way both the damage type and extent are identified. The approach can be used off-line and online by repetitive applications in a moving time window. A numerical experiment of a truss with 5% measurement error validates that the two tested damage types (constant stiffness reduction and breathing crack) can be identified along with the loads.     

Damage detection of truss bridge joints using Artificial Neural Networks

Recent developments in Artificial Neural Networks (ANNs) have opened up new possibilities in the domain of inverse problems. For inverse problems like structural identification of large structures (such as bridges) where in situ measured data are expected to be imprecise and often incomplete, ANNs may hold greater promise. This study presents a method for estimating the damage intensities of joints for truss bridge structures using a back-propagation based neural network. The technique that was employed to overcome the issues associated with many unknown parameters in a large structural system is the substructural identification. The natural frequencies and mode shapes were used as input parameters to the neural network for damage identification, particularly for the case with incomplete measurements of the mode shapes. Numerical example analyses on truss bridges are presented to demonstrate the accuracy and efficiency of the proposed method.     

Identification of both structural damages in bridge deck and vehicular parameters using measured dynamic responses

A method to identify both damages in bridge and the vehicular parameters from the structural dynamic responses is presented. A dynamic response sensitivity-based finite element model updating approach is used to identify both the structural damages and the vehicular parameters. The solution is obtained iteratively with the penalty function method with regularization from the measured dynamic responses. The effects of measurement noise, different vehicle models, measurement time duration and modeling error on the identification results are investigated in the numerical simulation. Studies in this paper indicate that the proposed method is efficient and robust for both damage and parameter identification.     

Geometry and sizing optimisation of discrete structure using the genetic programming method

This paper presents a structural optimisation method using the genetic programming (GP) technique. This method applied linear GP to derive optimum geometry and sizing of discrete structure from an arbitrary initial design space. The linear GP was used to find out the optimum nodal locations and member sizing of the structure through a linear sequence of programming instructions. The nodal locations and member cross-sectional areas of the structure were used as the design variable for these instructions, with the optimal geometry and sizing obtained by evolving a population of GP individuals satisfying the optimisation design objective. The approach was applied to the benchmark example of ten-bar planar truss for verification. Other truss examples, including 18-bar planar truss and 25-bar space truss, were also used to demonstrate the effectiveness of this method. The optimum results obtained demonstrate the practicability and generality of using the proposed method in geometry and sizing optimisation problems.     

Evaluating two model-free data interpretation methods for measurements that are influenced by temperature

Interpreting measurement data to extract meaningful information for damage detection is a challenge for continuous monitoring of structures. This paper presents an evaluation of two model-free data interpretation methods that have previously been identified to be attractive for applications in structural engineering: moving principal component analysis (MPCA) and robust regression analysis (RRA). The effect of three factors are evaluated: (a) sensor-damage location, (b) traffic loading intensity and (c) damage level, using two criteria: damage detectability and the time to damage detection. In addition, the effects of these three factors are studied for the first time in situations with and without removing seasonal variations through use of a moving average filter and an ideal low-pass filter. For this purpose, a parametric study is performed using a numerical model of a railway truss bridge. Results show that MPCA has higher damage detectability than RRA. On the other hand, RRA detects damages faster than MPCA. Seasonal variation removal reduces the time to damage detection of MPCA in some cases while the benefits are consistently modest for RRA.     

Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data

This study investigates the use of correlation-based damage detection methods for long-span, cable-stayed bridges. The proposed approach is based on the multiple damage location assurance criterion (MDLAC), which combines a correlation-based technique with a forward-type estimation of damage-sensitive structural parameters. Observing the level of correlation between the variations in the measured and analytically synthesized natural frequencies enables damage localization. The sensitivity matrix, developed from the finite element model, further accommodates multiple damage detection. The locations of damage are determined by iteratively searching for the combination of structural parameters that maximizes the correlation coefficient through the application of genetic algorithms. It is demonstrated that correlation-based modal analysis is successful for damage detection and localization using a numerical model of a full-scale cable-stayed bridge.     

Increasing occupant localization precision through identification of footstep-contact dynamics

Information regarding occupants inside buildings has the potential to improve security, energy management, and caregiving. Typical sensing approaches for occupant localization rely on mobile devices and cameras. These systems compromise privacy. Occupant localization using floor-vibration measurements, induced by footsteps, is a non-intrusive sensing method that requires few sensors (one per ~35 m²). Current occupant-localization methodologies that rely on vibration measurements are data-driven techniques. These techniques do not account for the structural behavior of floor slabs leading to ambiguous interpretations of vibrations measurement in the presence of obstructions and varying floor rigidities. In this paper, a model-based approach using error-domain model falsification (EDMF) is used to overcome these limitations. EDMF incorporates information related to physics-based models in the interpretation of vibration measurements to identify a population of possible occupant locations. EDMF accommodates systematic errors and model bias to reject models that contradict measurement data. Uncertainties from multiple sources such as modeling imperfection and walking-gait variability are included explicitly while estimating occupant locations using EDMF. A unique approach to identify footstep-contact dynamics is proposed and evaluated for its ability to improve the precision of occupant localization. The approach involves dividing the floor-slab into zones using knowledge of structural behavior. Clustering measured vibrations to define several footstep-contact severity levels helps reduce uncertainty in walking gait thus improving the accuracy of footstep-contact dynamics to use as loading input into model simulations. The utility of occupant localization using this approach is evaluated using a full-scale case study. Localization precision increased by more than 50% compared with non-zone-based strategies.     

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.     

A structural defect identification approach based on a continuum damage model

This paper introduces a structural identification technique built on finite element (FE) model updating. The FE model is parameterized by a structural parameter that continuously describes the damage in the structure, and besides, an evolution equation of this damage parameter is presented. The model updating is accomplished by determining the subset of this damage parameters that minimizes a global error derived from the dynamic residue vectors, which is obtained by introducing the experimental modal properties into the original model eigenproblem. A mode-shape projection technique is used in order to achieve compatibility between the dimension of the experimental and analytical models. The adjusted model maintains basic properties of the analytical model as the sparsity and the symmetry, which plays an important role in model updating-based damage identification. The verification and assessment of the current structural defect identification is performed on a analytically derived bidimensional truss structure and on a cantilever bidimensional Euler–Bernouilli beam through a virtual test simulator. This simulator is used to realistically simulate the corrupting effects of noise, filtering, digital sampling and truncation of the modal spectrum. The eigensystem realization algorithm along with the common-based normalized system identification were utilized to obtain the required natural frequencies and mode shapes.     

Fuzzy finite element analysis of imprecisely defined structures with fuzzy nodal force

This paper presents the fuzzy finite element analysis for static displacements of fixed free stepped rectangular beam, truss and simplified bridge structure with fuzzy nodal force. The material and geometric properties of the structures are taken as crisp. Fuzzy finite element analysis of static problem for the above structures converts the problem into fuzzy system of linear equations. As such the coefficient matrix and the right-hand side vector become crisp and fuzzy respectively. A new approach is used here to solve the fuzzy system of linear equations. Numerical results for the three stepped rectangular beam, three-bar truss and simplified bridge with fifteen elements are presented to illustrate the computational aspects of the developed method. The results obtained are depicted in term of plots.     

Measurement system design for civil infrastructure using expected utility

For system identification, most sensor-placement strategies are based on the minimization of the model-parameter uncertainty. However, reducing the uncertainty in remaining-life prognosis of structures is often more relevant. This paper proposes an optimization strategy using utility theory and probabilistic behavior prognoses based on model falsification to support decisions related to monitoring interventions. This approach, illustrated by the full-scale case study of a bridge, allows quantification of the expected utility of measurement systems while also indicating the profitability of monitoring actions. In addition, this approach is able to determine when the expected performance of monitoring configurations is reduced due to over-instrumentation. The use of model falsification for system identification allows for explicit inclusion of engineering heuristics in this knowledge intensive task while also offering robustness to effects of systematic modeling errors that are associated with idealization of complex civil structures.     

Support vector regression for anomaly detection from measurement histories

This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology.     

仿人机器人站立多平衡策略控制

设计了全状态反馈参数化控制器用于仿人机器人在站立平衡中应对外界冲击力与持续力. 采用多个机器人模型模仿人类站立平衡中的多个应对策略. 对每个模型, 设计了参数控制器作用于每一个状态误差, 并针对不同的作用力大小、方向和位置优化控制器参数. 应对不同外界作用力展现了该控制器的性能. 通过比较每一个策略处理干扰力的能力, 本文也探索了每个关节在站立平衡中的作用.     

Internal forces expressed as ratios of polynomials in the unimodal stiffnesses

The analytical solution of the linear structural problem is applied to generate approximate analysis models. As the exact expression is too long to be employed efficiently, its construct is used to develop explicit models for the internal forces. Two alternatives are presented. In both cases the models are exact along all the univariate lines radiating from the current point. A first model is created by using subsets of the exact expression. Although this approach gives excellent results it is plagued by linear dependency problems in selecting appropriate subsets. A second model can be viewed as a ratio of two linear Taylor series. After enforcing scaling invariance this model is equally efficient. The two methods are visualized by numerical examples of truss and frame design.     

Robot control strategy for in-orbit assembly of a micro satellite

We have developed novel heuristic control strategies for fitting parts with almost no clearance and also dealing with flexible objects using visual and force feedback. We have applied these control strategies for micro satellite assembly tasks of a ground research model of an in-orbit maintenance robot system. For the in-orbit maintenance system, micro satellites should be modularized as much as possible. The in-orbit maintenance system needs to assemble the modularized parts into micro satellites. We have developed a ground research model including a two-armed robotic platform and modularized micro satellite models. To assemble a micro satellite model, two problems arise: one is to engage two parts into a tight fit and the other is to handle flexible parts. We use a heuristic approach to solve the first problem — to grope one part to find the entrance of engagement of the other and to wobble this part to make a tight fit. For the second problem, visual measurement of the parts is used to position the end-effector of a robot arm and also active limp control is extensively used to adjust any misalignment that arises from the visual measurement error. With the combination of the heuristic, visual and active limp control, the system can successfully assemble a micro satellite.     

Dynamic response of trussed bridges for moving loads

A continuum approach is presented to obtain the transverse vibration of trussed bridges traversed by a single moving load. The efficiency and the accuracy of the method are determined by comparing its results with those obtained by the dynamic analysis of the bridge as a discrete lumped mass system, which can account for both truss action and flexural action of the deck in the response. Using the proposed method, a parametric study is performed to show the influence of some important parameters on the dynamic response of the bridge. The parameters include relative stiffness of the bridge deck and truss, number of panels, type of truss and speed parameter.     

激光追踪测量系统机械结构动力学建模方法

针对激光追踪测量系统提出了激光追踪测量系统机械结构的动力学建模方法,综合考虑了机械结构的结构参数和动态耦合力,实现了对激光追踪测量系统机械结构强度设计、结构改进和电机选型的精确参考.给出了激光追踪测量系统机械结构的理想情况和非理想情况的动力学模型,通过虚拟样机技术对机械模型进行了分析,结果表明激光追踪测量系统中机械结构的动态耦合项可以忽略,激光追踪测量系统的运动轴系可单独建立动力学模型,依据建立的模型选择用于激光跟踪控制的电机,并验证了建立的动力学模型的正确性.