Optimal Sensor Placement Technique Research Articles

Structural damage identification based on dual sensitivity analysis from optimal sensor placement

Structural damage identification (SDI) methods using incomplete modal information can avoid the extension for unmeasured degrees of freedom, but the absence of essential damage information often leads to the failure of SDI. To address this problem, a novel SDI method based on dual sensitivity analysis and optimal sensors placement technique is proposed in this study. Firstly, in the optimal sensor placement technique, an improved eigenvector sensitivity method combined with weighted modal kinetic energy is proposed, which enables the acquisition of eigenvector information related to damage sensitivity, and incorporates it into the modal strain energy sensitivity matrix to obtain the dual sensitivity analysis matrix. Then, the sparsity of structural damage is considered, and the L1 sparse regularization is selected and introduced into the dual sensitivity analysis damage equation for better SDI results. Finally, to assess the effectiveness of the proposed method, a series of numerical simulations and experimental verifications were carried out under different structural damage scenarios. The results indicate that the proposed method can efficiently localize and quantify the structural damage with minimal modal information in one single step.

Methodologies and Challenges for Optimal Sensor Placement in Historical Masonry Buildings

As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, this presents peculiar shortcomings and open challenges. One of the most relevant aspects that deserve more research is the optimisation of the sensor placement to tackle well-known issues in ambient vibration testing for such buildings. The present paper focuses on the application of optimal sensor placement (OSP) strategies for dynamic identification in historical masonry buildings. While OSP techniques have been extensively studied in various structural contexts, their application in historical masonry buildings remains relatively limited. This paper discusses the challenges and opportunities of OSP in this specific context, analysing and discussing real-world examples, as well as a numerical benchmark application to illustrate its complexities. This article aims to shed light on the progress and issues associated with OSP in masonry historical buildings, providing a detailed problem formulation, identifying ongoing challenges and presenting promising solutions for future improvements.

Merging experimental design and structural identification around the concept of modified Constitutive Relation Error in low-frequency dynamics for enhanced structural monitoring

This paper presents a novel Optimal Sensor Placement (OSP) strategy that is dedicated to model updating problems based on the modified Constitutive Relation Error (mCRE) functional in low-frequency dynamics. The mCRE is a credible alternative to model updating functionals that stands out by searching structural parameters alongside mechanical fields as the best trade-off between all available information from measured data, without any further a priori assumption. Considering damage detection problems, due to possible discrepancies in terms of parameters sensitivity with respect to mCRE, sensor locations provided by standard OSP algorithms may be irrelevant. The proposed approach uses the concept of Information Entropy by formulating a modified Fisher information matrix, in which the sensitivity of the mCRE mechanical fields with respect to the updated parameters is involved. The approach is legitimated by the strong connection between mCRE and Bayesian inference. A proof-of-concept involving an earthquake engineering inspired academic case study, where accelerometers are positioned on a two-story frame structure subjected to random ground motion, permits to illustrate the soundness and efficiency of the proposed methodology compared to other classical OSP techniques. The influence of critical mCRE parameters is shown, as well as the benefits of taking multiple scenarios into account so as to get an OSP that is relevant for a wider range of possible damage occurrences.

Optimal sensor placement techniques for modal identification of historical masonry structures

Since destructive tests are not allowed for historical structures, numerical model updating using accelerometers has gained a lot of attraction in the last decade. Furthermore, another application of structural health monitoring is damage detection for near-real-time monitoring of cultural heritage assets of infrastructures such as masonry bridges. However, high cost is the main problem that discourages the use of large-scale structural health monitoring systems, and a modal pretest analysis is required to plan and optimize the modal tests procedure. For this purpose, various optimal sensor placement (OSP) techniques have been developed to derive the operational modal analysis results with a minimum number of sensors, leading to a lower cost. In this study, various OSP techniques have been applied to optimize sensor placement in two selected case studies. The first case study is a two-span masonry arch bridge in Rhodes, Greece and the second is a stone masonry tower located in Tønsberg, Norway. Baseline finite element models were developed before performing the ambient vibration tests and model updating process. The optimum sensor locations were detected using various techniques, and a comparative study was conducted on the results. Furthermore, the effect of considering soil-structure interaction on the OSP results was investigated.

Evaluation of optimal sensor placement algorithms for the Structural Health Monitoring of architectural heritage. Application to the Monastery of San Jerónimo de Buenavista (Seville, Spain)

In recent years, Structural Health Monitoring (SHM) based on Operational Modal Analysis (OMA) and damage detection tools has become a popular non-destructive solution to assess the real-time integrity of any kind of structure. This technique is especially well-suited for the condition-based conservation of historical structures, where minimal invasiveness must be ensured owing to their high cultural and architectural value. Optimal Sensor Placement (OSP) techniques represent a valuable tool for efficiently designing the sensor layout in a SHM system in order to achieve an effective modal identification with a reduced number of sensors and, consequently, an improved cost efficiency. In this light, this paper proposes a design methodology of sensor networks based on OSP techniques suitable for historical structures. To do so, a preliminary extensive OMA campaign is conducted in order to construct a reliable finite element (FE) model by fitting the identified modal properties. Afterwards, an optimal sensor arrangement with a limited number of sensors is obtained by applying different model-based OSP techniques. In order to improve the robustness of the solution, material uncertainties are included in the model and the optimal sensor placement is conducted within a statistical framework. This methodology is presented and evaluated with a case study of a Spanish secular building: the Monastery of San Jerónimo de Buenavista in Seville (Spain). In particular, this paper presents the results of the preliminary ambient vibration test and the modal identification of the monastery, the updating process of the FE model, as well as a critical review of the different OSP techniques within a framework of material parameter uncertainty. The presented analysis demonstrate that OSP techniques based on the rank optimization of the kinetic energy matrix of the structure yield robust sensor layout.

Genetic algorithm based optimal sensor placement forL1-regularized damage detection

Sparse recovery theory has been applied to damage detection by utilizing the sparsity feature of structural damage. The theory requires that the columns of the sensing matrix suffice certain independence criteria. In l1-regularized damage detection, the sensitivity matrix serves as the sensing matrix and is directly related to sensor locations. An optimal sensor placement technique is proposed such that the resulting sensitivity matrix is of the maximum independence in the columns or is of the least mutual coherence. Given a total number of sensors, the selection of sensor locations is a combinatorial problem. A genetic algorithm is thus used to solve this optimization problem, in which the mutual coherence of the sensitivity matrix is minimized. The obtained optimal sensor locations and associated sensitivity matrix are used in l1-regularized damage detection. An experimental cantilever beam and a three-storey frame are utilized to verify the effectiveness and reliability of the proposed sensor placement technique. Results show that using the modal data based on the optimal sensor placement can identify damage location and severity more accurately than using the ones based on uniformly selected sensor locations.

Blind source separation-based optimum sensor placement strategy for structures

Optimal sensor placement (OSP) plays a key role towards cost-effective structural health monitoring (SHM) of flexible structures like tall buildings. In the context of SHM, vibration measurements collected from a large array of sensors involve significant data storage, signal processing, and labor-intensive deployment of sensors. Moreover, with the increasing cost of vibration sensors, it is not possible to install sensors at all locations of the structure. To circumvent these practical challenges, the OSP provides a powerful mathematical framework to estimate unknown structural information based on optimally instrumented sensors located at fewer locations. The proposed research is focused on determination of optimum sensor positions in the multi-degrees-of-freedom system using blind source separation (BSS) method. The underdetermined signal separation capability of the BSS is explored to conduct the modal identification using fewer sensors and the resulting modal parameters are utilized to set up the optimization criterion for optimal sensor configurations. The methodology is illustrated using simulation models with different mass and stiffness distributions under a wide range of ground motion characteristics. Finally, the vibration measurements of the Canton tower data in China are utilized to demonstrate the performance of the proposed OSP technique.

Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm

Optimal sensor placement technique plays a key role in the design of an effective structural health monitoring system. Recent advances in sensing technologies have also promoted using multiaxial sensors to perform efficiently and economically monitoring for civil engineering structures. However, the available evaluation criteria for the optimal sensor placement can only guarantee that the optimization is conducted in a single structural direction but not in multi-dimension space, which may result in the non-optimal placement of multiaxial sensors. To tackle this issue thoroughly, a new multiaxial optimal criterion termed as the triaxial modal assurance criterion is developed by taking account into three translational degrees of freedom as a single unit in the Fisher information matrix. Afterwards, a novel distributed wolf algorithm is proposed to improve the optimization performance in identifying the best sensor locations. The dual-structure coding method is improved and adopted to represent the solution. The shuffling strategy is proposed to enhance the searching capability and convergence performance. The attacking process is also modified to prevent the algorithm from being trapped in a local minimum. The effectiveness of the proposed scheme is investigated by the benchmark structure developed by the University of Central Florida, USA. The results clearly demonstrate that the proposed distributed wolf algorithm outperforms the existing algorithm in its global optimization capability. Copyright © 2015 John Wiley & Sons, Ltd.

A triaxial accelerometer monkey algorithm for optimal sensor placement in structural health monitoring

Optimal sensor placement (OSP) technique is a vital part of the field of structural health monitoring (SHM). Triaxial accelerometers have been widely used in the SHM of large-scale structures in recent years. Triaxial accelerometers must be placed in such a way that all of the important dynamic information is obtained. At the same time, the sensor configuration must be optimal, so that the test resources are conserved. The recommended practice is to select proper degrees of freedom (DOF) based upon several criteria and the triaxial accelerometers are placed at the nodes corresponding to these DOFs. This results in non-optimal placement of many accelerometers. A ‘triaxial accelerometer monkey algorithm’ (TAMA) is presented in this paper to solve OSP problems of triaxial accelerometers. The EFI3 measurement theory is modified and involved in the objective function to make it more adaptable in the OSP technique of triaxial accelerometers. A method of calculating the threshold value based on probability theory is proposed to improve the healthy rate of monkeys in a troop generation process. Meanwhile, the processes of harmony ladder climb and scanning watch jump are proposed and given in detail. Finally, Xinghai NO.1 Bridge in Dalian is implemented to demonstrate the effectiveness of TAMA. The final results obtained by TAMA are compared with those of the original monkey algorithm and EFI3 measurement, which show that TAMA can improve computational efficiency and get a better sensor configuration.

Probabilistic damage identification of structures with uncertainty based on dynamic responses

The probabilistic damage identification problem with uncertainty in the FE model parameters, external-excitations and measured acceleration responses is studied. The uncertainty in the system is concerned with normally distributed random variables with zero mean value and given covariance. Based on the theoretical model and the measured acceleration responses, the probabilistic structural models in undamaged and damaged states are obtained by two-stage model updating, and then the Probabilities of Damage Existence (PDE) of each element are calculated as the damage criterion. The influences of the location of sensors on the damage identification results are also discussed, where one of the optimal sensor placement techniques, the effective independence method, is used to choose the nodes for measurement. The damage identification results by different numbers of measured nodes and different damage criterions are compared in the numerical example.

Optimal sensor placement for mode shapes using improved simulated annealing

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

Optimal Sensor Placement for Modal Identification of Bridge Systems Considering Number of Sensing Nodes

A series of optimal sensor placement (OSP) techniques is discussed in this paper. A framework for deciding the optimum number and location of sensors is proposed, to establish an effective structural health monitoring (SHM) system. The vibration response from an optimized sensor network reduces the installation and operational cost, simplifies the computational processes for a SHM system, and ensures an accurate estimation of monitoring parameters. In particular, the proposed framework focuses on the determination of the number of sensors and their proper locations to estimate modal properties of bridge systems. The relative importance of sensing locations in terms of signal strength was obtained by applying several OSP techniques, which include effective influence (EI), EI-driving point residue (EI-DPR), and kinetic energy (KE) methods. Additionally, the modified variance (MV) method, based on the principal component analysis (PCA), was developed with the assumption of independence in modal ordinates at each sensing location. Modal assurance criterion (MAC) between the target and interpolated mode shapes from an optimal sensor set was utilized as an effective measure to determine the number of sensors. The proposed framework is verified by three examples: (1) a numerically simulated simply supported beam, (2) finite-element (FE) model of the Northampton Street Bridge (NSB), and (3) modal information identified using a set of wireless sensor data from the Golden Gate Bridge (GGB). These three examples demonstrate the application and efficiency of the proposed framework to optimize the number of sensors and verify the performance of the MV method compared to the EI, EI-DPR, and KE methods.

A Generalized Optimal Sensor Placement Technique for Structural Health Monitoring and System Identification

Abstract A structural health monitoring system consists of permanently installed sensors to collect structural information, and these sensors are required to be placed at ‘good’ positions for damage identification. Conventional sensor placement methods make use of dynamic characteristics of a structure, i.e., mode shapes and natural frequencies, to determine optimal sensor positions. The engineering community has traditionally relied on these modal methods and finite element analysis for dynamic predictions in the low frequency range. However, these techniques become ineffective in mid frequency range due to large number of modes and high modal density. In view of this, in this paper, an optimal sensor placement technique which can be employed for low as well as mid frequency range structures for structural health monitoring as well as structural system identification is presented. The frequency domain based optimal sensor placement technique (FEfi) presented in this paper makes use of principal components evaluated from frequency response functions at the desired frequency levels. Numerical examples with optimally placed sensors dictated by the proposed algorithm are presented and compared with the popular effective independence (Efi) technique based on mode shapes.

Theoretical Research on Two Improved Optimal Sensor Placement Methods

Optimal sensor placement technique plays a key role in structural health monitoring and structural vibration control. Based on the advantages and disadvantages of effective independence (EI) and modal kinetic energy (MKE) methods, two improved optimal sensor placement methods which are Effective Independence - Average Acceleration Amplitude (EI-AAA) method and Effective Independence - Modal Kinetic Energy (EI-MKE) method are proposed in this paper. Firstly the formulas are deduced from modal expansion of multiple-input-multiple-output (MIMO) displacement frequency response function matrix. Then a computational simulation of steel cross beam structure has been implemented to demonstrate the feasibility of the two improved methods above. The obtained optimal sensor locations using the two improved methods are compared with those gained by EI method and MKE method. Finally six classical comparison criteria are employed to demonstrate the advantage and disadvantage of these four methods. The results showed that some innovations proposed in this paper are effective and reliable. The two improved optimal sensor placement methods (EI-AAA method and EI-MKE method) can not only make the truncated mode shapes as linearly independent as possible but also enable the measured modal kinetic energy to maintain the maximum value.

Instrumentation and system identification of a typical school building in Istanbul

This study presents the findings of the structural health monitoring and the real time system identification of one of the first large scale building instrumentations in Turkey for earthquake safety. Within this context, a thorough review of steps in the instrumentation, monitoring is presented and seismic performance evaluation of structures using both nonlinear pushover and nonlinear dynamic time history analysis is carried out. The sensor locations are determined using the optimal sensor placement techniques used in NASA for on orbit modal identification of large space structures. System identification is carried out via the stochastic subspace technique. The results of the study show that under ambient vibrations, stocky buildings can be substantially stiffer than what is predicted by the finite element models due to the presence of a large number of partitioning walls. However, in a severe earthquake, it will not be safe to rely on this resistance due to the fact that once the partitioning walls crack, the bare frame contributes to the lateral stiffness of the building alone. Consequently, the periods obtained from system identification will be closer to those obtained from the FE analysis. A technique to control the validity of the damping is employed that checks the presence of phase difference in displacements of different stories obtained from band pass filtered records and it is confirmed that the proportional damping assumption is valid for this structure. Two different techniques are implemented for identifying the influence of the soil structure interaction. The first technique uses the transfer function between the roof and the basement in both directions. The second technique uses a pre-whitening filter on the data obtained from both the basement and the roof. Subsequently the impulse response function is computed from the scaled cross correlation between the input and the output. The overall results showed that the structure will satisfy the life safety performance level in a future earthquake but some soil structure interaction effects should be expected in the North South direction.

Evaluation of Optimal Sensor Placement Techniques for Parameter Identification in Buildings

This paper addresses the application of six different optimal sensor placement (OSP) techniques in buildings. These techniques are the EFfective Independence (EFI), Optimal Driving Point (ODP), Non-Optimal Driving Point (NODP), Effective Independence Driving Point Residue (EFI-DPR), Singular Value Decomposition (SVD) and the Sensor Set Expansion (SSE) methods. A toolbox OPTISEP is developed by the author for this purpose within the context of this paper. The techniques are compared among themselves by using various criteria. The overall results show that the SSE Technique is the best. First, the technique results in a dramatic reduction in the computational effort. Second, it allows a civil engineer to specify a set of locations that they absolutely want to keep in the final sensor configuration. Mozst importantly, while the sensor distribution estimated by other techniques is mainly concentrated in a certain storey of the building, SSE gives a homogeneous sensor distribution throughout the building. Finally, it is shown that the technique i s also robust against noise in the measurements.

Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating

A methodology is presented for the identification of the best locations to install a given number of sensors on a structure so as to extract as much information as possible for structural model updating. The information entropy measure is employed to quantify the uncertainties of the set of identified model parameters. The problem of optimal sensor placement is formulated as a discrete optimization problem in which the information entropy measure is minimized and the sensor configurations are taken as the minimization variables. This discrete optimization problem is computationally demanding especially for large-scale structures. An efficient genetic algorithm-based optimization method is developed to solve this minimization problem to make the entropy-based optimal sensor configuration approach applicable for large-scale structural systems. A typical transmission tower is first used as a numerical example to illustrate the proposed methodology. The performance of the optimal sensor placement technique and the proposed optimization method are then verified using the measured dynamic data from a 2.6 m high transmission tower model under laboratory conditions. The computational time of the proposed optimization method can be reduced in the future by making use of the parallel computing technology. Copyright © 2009 John Wiley & Sons, Ltd.

Optimal sensor placement techniques for system identification and health monitoring of civil structures

Proper pretest planning is a vital component of any successful vibration test on engineering structures. The most important issue in dynamic testing of many engineering structures is arriving at the number and optimal placement of sensors. The sensors must be placed on the structure in such a way that all the important dynamic behaviour of a structural system is captured during the course of the test with sufficient accuracy so that the information can be effectively utilised for structural parameter identification or health monitoring. Several optimal sensor placement (OSP) techniques are proposed in the literature and each of these methods have been evaluated with respect to a specific problem encountered in various engineering disciplines like aerospace, civil, mechanical engineering, etc. In the present work, we propose to perform a detailed characteristic evaluation of some selective popular OSP techniques with respect to their application to practical civil engineering problems. Numerical experiments carried out in the paper on various practical civil engineering structures indicate that effective independence (EFI) method is more consistent when compared to all other sensor placement techniques.

Optimal sensor placement for spatial lattice structure based on genetic algorithms

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.

On the optimal sensor placement techniques for a bridge structure

Health monitoring systems set up to assure the safe operation of structures require linking sensors with computational tools able to interpret sensor data in terms of structural performance. Although intensive development continues on innovative sensor systems, there is still considerable uncertainty in deciding on the number of sensors required and their location in order to obtain adequate information on structural behaviour. This paper considers the problem of locating sensors on a bridge structure with the aim of maximizing the data information so that structural dynamic behaviour can be fully characterised. Six different optimal sensor placement techniques, three based on the maximisation of the Fisher information matrix (FIM), one on the properties of the covariance matrix coefficients, and two on energetic approaches, have been investigated. Mode shape displacements were taken as the measured data set and two comparison criteria were employed. The first criterion was based on the mean square error between the FE model and the cubic spline interpolated mode shapes. The second criterion measured the information content of each sensor location to investigate on the strength of the acquired signals and their ability to withstand the noise pollution keeping intact the information relative to the structure properties. The results showed that the effective independence driving-point residue (EFI-DPR) method provides an effective method for optimal sensor placement to identify the vibration characteristics of the studied bridge. The variance method (VM) developed by the authors gave results very close to the EFI-DPR technique, in terms of the capability to capture the vibration mode shape and signal strength. However, the VM presented unique characteristics in the world of the OSP techniques, which is the indication of the optimal number of sensors (ONS).