Optimal sensor placement in structural health monitoring using discrete optimization

The objective of optimal sensor placement (OSP) is to obtain a sensor layout that gives as much information of the dynamic system as possible in structural health monitoring (SHM). The process of OSP can be formulated as a discrete minimization (or maximization) problem with the sensor locations as the design variables, conditional on the constraint of a given sensor number. In this paper, we propose a discrete optimization scheme based on the artificial bee colony algorithm to solve the OSP problem after first transforming it into an integer optimization problem. A modal assurance criterion-oriented objective function is investigated to measure the utility of a sensor configuration in the optimization process based on the modal characteristics of a reduced order model. The reduced order model is obtained using an iterated improved reduced system technique. The constraint is handled by a penalty term added to the objective function. Three examples, including a 27 bar truss bridge, a 21-storey building at the MIT campus and the 610 m high Canton Tower, are investigated to test the applicability of the proposed algorithm to OSP. In addition, the proposed OSP algorithm is experimentally validated on a physical laboratory structure which is a three-story two-bay steel frame instrumented with triaxial accelerometers. Results indicate that the proposed method is efficient and can be potentially used in OSP in practical SHM.

[1]  K. Yuen,et al.  Modal decomposition using multi-channel response measurements , 2014 .

[2]  Raimondo Betti,et al.  Simultaneous identification of structural parameters and dynamic input with incomplete output‐only measurements , 2014 .

[3]  Ting-Hua Yi,et al.  Optimal sensor placement for health monitoring of high‐rise structure using adaptive monkey algorithm , 2015 .

[4]  Eric B. Flynn,et al.  A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing , 2010 .

[5]  Keith Worden,et al.  Optimal sensor placement for fault detection , 2001 .

[6]  Costas Papadimitriou,et al.  Optimal sensor placement methodology for parametric identification of structural systems , 2004 .

[7]  Yeesock Kim,et al.  Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures , 2012, Expert Syst. Appl..

[8]  A. Rama Mohan Rao,et al.  Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique , 2007 .

[9]  Chan Ghee Koh,et al.  Modified genetic algorithm strategy for structural identification , 2006 .

[10]  C. Papadimitriou,et al.  The effect of prediction error correlation on optimal sensor placement in structural dynamics , 2012 .

[11]  Raimondo Betti,et al.  Nondestructive identification of multiple flaws using XFEM and a topologically adapting artificial bee colony algorithm , 2013 .

[12]  Bin Ma,et al.  Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm , 2013 .

[13]  O. Büyüköztürk,et al.  Damage detection with small data set using energy‐based nonlinear features , 2016 .

[14]  Hao Sun.  Development of Hierarchical Optimization-based Models for Multiscale Damage Detection , 2014 .

[15]  Oral Büyüköztürk,et al.  Heterogeneous Data Fusion for Traffic-induced Excitation Identification of Truss Bridges , 2015 .

[16]  Ka-Veng Yuen.  Updating large models for mechanical systems using incomplete modal measurement , 2012 .

[17]  Peter Adam Trocha.  Characterization of structural properties and dynamic behavior using distributed accelerometer networks and numerical modeling , 2013 .

[18]  Sankaran Mahadevan,et al.  Bayesian wavelet packet denoising for structural system identification , 2007 .

[19]  Yi-Qing Ni,et al.  SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data , 2012 .

[20]  Oral Buyukozturk,et al.  Identification of traffic-induced nodal excitations of truss bridges through heterogeneous data fusion , 2015 .

[21]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[22]  Ting-Hua Yi,et al.  Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm , 2012 .

[23]  Bai Li,et al.  An edge-based optimization method for shape recognition using atomic potential function , 2014, Eng. Appl. Artif. Intell..

[24]  Ting-Hua Yi,et al.  Optimal sensor placement for structural health monitoring based on multiple optimization strategies , 2011 .

[25]  Young-Jin Cha,et al.  Optimal placement of active control devices and sensors in frame structures using multi‐objective genetic algorithms , 2013 .

[26]  F M.I..  MODEL REDUCTION USING DYNAMIC AND ITERATED IRS TECHNIQUES , .

[27]  Raimondo Betti,et al.  Identification of structural models using a modified Artificial Bee Colony algorithm , 2013 .

[28]  Ya Li,et al.  Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model , 2014, Eng. Appl. Artif. Intell..

[29]  Raimondo Betti,et al.  A multiscale flaw detection algorithm based on XFEM , 2014 .

[30]  Heung-Fai Lam,et al.  Optimal sensor configuration of a typical transmission tower for the purpose of structural model updating , 2011 .

[31]  Costas Papadimitriou,et al.  Optimal Sensor Placement Methodology for Identification with Unmeasured Excitation , 2001 .

[32]  James L. Beck,et al.  Structural damage detection and assessment by adaptive Markov chain Monte Carlo simulation , 2004 .

[33]  M. Friswell,et al.  Model reduction using dynamic and iterated IRS techniques , 1995 .

[34]  Roger Ghanem,et al.  A novel approach for the structural identification and monitoring of a full-scale 17-story building based on ambient vibration measurements , 2008 .

[35]  William A. Sethares,et al.  Sensor placement for on-orbit modal identification via a genetic algorithm , 1993 .

[36]  Mehmet Çelebi,et al.  Rocking Behavior of an Instrumented Unique Building on the MIT Campus Identified from Ambient Shaking Data , 2014 .

[37]  Raimondo Betti,et al.  Structural identification with incomplete instrumentation and global identifiability requirements under base excitation , 2015 .

[38]  D. Kammer.  Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures , 1990, 1990 American Control Conference.

[39]  Ka-Veng Yuen,et al.  Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems , 2015 .

[40]  Hongnan Li,et al.  Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm , 2015 .

[41]  Shamim N. Pakzad,et al.  Optimal sensor configuration for flexible structures with multi-dimensional mode shapes , 2015 .

[42]  Hong Hao,et al.  Multi-stage identification scheme for detecting damage in structures under ambient excitations , 2013 .

[43]  L. L. Zhang,et al.  Optimal placement of sensors for structural health monitoring using improved genetic algorithms , 2004 .

[44]  Oral Büyüköztürk,et al.  Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization , 2015, Comput. Aided Civ. Infrastructure Eng..

[45]  Raimondo Betti,et al.  A Hybrid Optimization Algorithm with Bayesian Inference for Probabilistic Model Updating , 2015, Comput. Aided Civ. Infrastructure Eng..

[46]  Rune Brincker,et al.  Modal identification of output-only systems using frequency domain decomposition , 2001 .

[47]  Frédo Durand,et al.  Modal identification of simple structures with high-speed video using motion magnification , 2015 .

[48]  Daniel C. Kanner.  Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures , 1990 .

[49]  Randall J. Allemang,et al.  A Correlation Coefficient for Modal Vector Analysis , 1982 .