Advertisement

Earthquake Engineering and Engineering Vibration

, Volume 19, Issue 1, pp 205–222 | Cite as

Sensor location in concrete slabs with various layout of opening using modified ‘FEMS-COMAC’ approach

  • H. VosoughifarEmail author
  • P. Manafi
Article

Abstract

Two-way concrete slabs are widely used around the world for the construction of many types of infrastructures and common buildings. The optimal sensor placement (OSP) in slabs with various opening positions is the most important issue in structural health monitoring (SHM) to increase reliability. In this study, a novel approach of OSP was evaluated to obtain the number and placement of sensors using examination of the closed loop performance. The nonlinear finite element (NFE) was used to discretize the mechanism behavior of slab. Multi-Objective Optimization based on the coordinate modal assurance criterion (COMAC) and cost considerations was considered in the optimization processes. All of the analysis, discretization and optimization process was designed and developed as a novel approach in Matlab by the author under the name ‘FEMS-COMAC’ (FEM analysis of slab with COMAC). The points in the finite element method (FEM) mesh were classified as line by line information along the slab. The OSP in each line was optimized according to the objective function. The slabs with various width, thickness, aspect ratio and opening position were selected as case studies. The results of the OSP using the COMAC algorithm around the slab openings were compared with the novel ‘FEMS-COMAC’ method. The statistical analysis according Mann-Whitney criteria shows that there were significant differences between them in some of the case studies (mean P-value=0.54).

Keywords

two-way concrete slabs openings OSP MAC COMAC 

Nomenclature

B

Length of the bay of slab (longer side)

Ceff

Effective damping coefficient eff

COMAC

Coordinate modal assurance criterion

D

The slab flexural rigidity

DOF

Degree of freedom

E

Modulus of elasticity for plane stress analysis

EI

Effective independence

FEM

Finite element method

FEMS-COMAC

FEM analysis of slab with COMAC

EVP

Eigenvalue vector product

f

Natural frequency

FF

Natural frequency of freely

fnum

Numerical natural frequency num

fanal

Analytical natural frequency anal

FBG

Fiber bragg grating

GA

Genetic algorithm

KE

Kinetic energy

G

Shear modulus

M

Moment

MAC

Modal assurance criterion

MSSP

Mode shape summation plot

MA

Modal analysis

MAE

Mean absolute error

N

Number of observations

NFE

Nonlinear finite element

NTA

Nonlinear time history analysis

OSP

Optimal sensor placement

Qx

Shearing forces parallel to z axis

Qy

Shearing forces perpendicular to x and y axes y

R2

Correlation coefficient

RC

Reinforced concrete

RMSE

Root mean square error

SHM

Structural health monitoring

SS

Simply supported

SON1

Slab with opening No. 1

SON2

Slab with opening No. 2

SON3

Slab with opening No. 3

SON4

Slab with opening No. 4

SON5

Slab with opening No. 5

SWO

Slab without opening

u

Displacements in x direction

v

Displacements in y direction

w

Displacements in z direction

W

length of the span of slab (smaller side)

α

Numerical correction factor

γ

Shear strain

γmax

Maximum shear strain max

ε

Strain

ζ

damping ratio

θ

Rotation

ρ

Density

λ

Dimensionless natural frequency factor

μ

Mass density per unit area of slab

υ

Poisson’s ratio of the concrete

σ

Stress

φi

Eigenvector of mode i, comprising only the measured degrees of freedom

φj

Corresponding experimental eigenvector of mode j

φT

Transpose of φ

χ

Curvature along directions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ACI Committee 318 (2014), Building Code Requirements for Reinforced Concrete (ACI 318-2014) and Commentary, American Concrete Institute, Farmington Hills, 348.Google Scholar
  2. Ahmed M and Mohammad F (2014), “Theoretical Modal Analysis of Freely and Simply Supported RC Slabs,” World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 8: 2064–2068.Google Scholar
  3. Aikaterini S, Genikomsou M and Anna P (2017), “3D Finite Element Investigation of the Compressive Membrane Action Effect in Reinforced Concrete Flat Slabs,” Engineering Structures, 136: 233–244.CrossRefGoogle Scholar
  4. Allemang R (2003), “The Modal Assurance Criterion (MAC): Twenty Years of Use and Abuse,” Structural Dynamics Research Laboratory Mechanical, Sound and Vibration, 37: 14–23.Google Scholar
  5. Bompa DV and Elghazouli AY (2016), “Structural Performance of RC Flat Slabs Connected to Steel Columns with Shear Heads,” Journal of Engineering Structures, 117: 161–183.CrossRefGoogle Scholar
  6. Cardoso R, Cury A and Barbosa F (2017), “A Robust Methodology for Modal Parameters Estimation Applied to SHM,” Mechanical Systems and Signal Processing, 95: 24–41.CrossRefGoogle Scholar
  7. Chen Y, Xuepan Z, Xiaoqi H, ZhengAi C, Xinghua Z and Xinbin H (2017), “Optimal Sensor Placement for Deployable Antenna Module Health Monitoring in SSPS Using Genetic Algorithm,” Acta Astronautica, 140: 213–224.CrossRefGoogle Scholar
  8. Chiaia B, Kumpyak O, Placidi L and Maksimov V (2015), “Experimental Analysis and Modeling of Two-Way Reinforced Concrete Slabs over Different Kinds of Yielding Supports under Short-Term Dynamic Loading,” Journal of Engineering Structures, 96: 88–99.CrossRefGoogle Scholar
  9. Fernandes H. Lúcio V and Ramos A (2017), “Strengthening of RC Slabs with Reinforced Concrete Overlay on the Tensile Face,” Journal of Engineering Structures, 132: 540–550.CrossRefGoogle Scholar
  10. Firoozbakht M, Vosoughifar H and Ghari Ghoran A (2019), “Coverage Intensity of Optimal Sensors for Common, Isolated and Integrated Steel Structures Using Novel Approach of FEM-MAC-TTFD,” International Journal of Distributed Sensor Networks, 15(8): 1–17.CrossRefGoogle Scholar
  11. Hernández-Montes E, Carbonell-Márquez Luisa JF and Gil-Martín M (2014), “Limits to the Strength Design of Reinforced Concrete Shells and Slabs,” Engineering Structures, 61(1): 184–194.CrossRefGoogle Scholar
  12. Herraiz B and Vogel T (2016), “Novel Design Approach for the Analysis of Laterally Unrestrained Reinforced Concrete Slabs Considering Membrane Action,” Journal of Engineering Structures, 123: 313–329.CrossRefGoogle Scholar
  13. Hu RP, Xu YL and Zhan S (2018), “Multi-Type Sensor Placement and Response Reconstruction for Building Structures: Experimental Investigations,” Earthquake Engineering and Engineering Vibration, 17(1): 29–46.  https://doi.org/10.1007/s11803-018-0423-3.CrossRefGoogle Scholar
  14. Ibrahim A, Salim H and Shehab El-Din H (2011), “Moment Coefficients for Design of Waffle Slabs with and without Openings,” Journal of Engineering Structures, 33: 2644–2652.CrossRefGoogle Scholar
  15. Khajehdehi R and Panahshahi N (2016), “Effect of Openings on In-Plane Structural Behavior of Reinforced Concrete Floor Slabs,” Journal of Building Engineering, 7: 1–11.CrossRefGoogle Scholar
  16. Li J, Wu CH, Hao H, Sul Y and Li Z (2017), “A Study of Concrete Slabs with Steel Wire Mesh Reinforcement under Close-In Explosive Loads,” International Journal of Impact Engineering, 110: 242–254.CrossRefGoogle Scholar
  17. Maazoun A, Belkassem B, Reymen B, Matthys S, Vantomme J and Lecompte D (2018), “Blast Response of RC Slabs with Externally Bonded Reinforcement: Experimental and Analytical Verification,” Composite Structures, 18th International Conference on Experimental Mechanics (ICEM18), Brussels, Belgium, 246–257.Google Scholar
  18. Moradi M, Vosoughifar H and Hassanzadeh Y (2014), “Optimal Placement of Smart Sensors in the Underground Storage Tanks Regarding to the Cavitation Effect by Monte Carlo Analysis,” 2nd International Conference on Structure, Architecture and Urban Development, Tabriz.Google Scholar
  19. Mousavi M, Ashrafi, Shafiepourmotlagh M, Niksokhan M and Vosoughifar H (2017), “Design of a Correlated Validated CFD and Genetic Algorithm Model for Optimized Sensors Placement for Indoor Air Quality Monitoring,” Heat Mass Transfer, 54(2): 509–521.CrossRefGoogle Scholar
  20. Navarro M, Ivorra S and Varona FB (2018), “Parametric Computational Analysis for Punching Shear in RC Slabs,” Journal of Engineering Structures, 165: 254–263.CrossRefGoogle Scholar
  21. Rao A, Lakshmi K and Krishnakumar S (2014), “A Generalized Optimal Sensor Placement Technique for Structural Health Monitoring and System Identification,” Procedia Engineering, 1st International Conference on Structural Integrity, 86: 529–538.CrossRefGoogle Scholar
  22. Shokouhi SK and Vosoughifar H (2013a), “Optimal Sensor Placement in the Lightweight Steel Framing Structures Using the Novel TTFD Approach Subjected to Near-Fault Earthquakes,” Journal of Civil Structure Health Monitoring, 3: 257–267.CrossRefGoogle Scholar
  23. Shokouhi SK and Vosoughifar H (2013b), “Optimal Sensor Placement in the Lightweight Steel Framing Structures Using the Novel TTFD Approach Subjected to Near-Fault Earthquakes,” Journal of Civil structure Health Monitoring, 3: 257–267.CrossRefGoogle Scholar
  24. Shokouhi SK, Dolatshah A, Vosoughifar H, Hosseininejad SZ and Rahnavard Y (2013a), “Optimal Sensor Placement of TYTON Joints in the Water Pipeline Networks Subjected to Near-Fault and Far-Fault Earthquake,” Pipelines, ASCE, 736–746.Google Scholar
  25. Shokouhi SK, Dolatshah A, Vosoughifar H and Rahnavard Y (2013b), “Optimal Placement of Viscous Dampers in the water pipeline networks subjected to near-fault earthquakes using Genetic Algorithm,” Proceedings of the ASME Pressure Vessels and Piping Conference PVP, 8.Google Scholar
  26. Shokouhi SK, Dolatshah A, Vosoughifar H, Rahnavard Y and Dowlatshahi B (2013c), “Optimal Sensor Placement in the Base-Isolated Structures Subjected to Near-Fault Earthquakes Using a Novel TTFD Approach,” Proc. SPIE 8692, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 86924.Google Scholar
  27. Spathelf CA and Vogel T (2018), “Fatigue Performance of Orthogonally Reinforced Concrete Slabs: Experimental investigation,” Journal of Engineering Structures, 168: 69–81.CrossRefGoogle Scholar
  28. Sun H and Büyüköztürk O (2015), “Optimal sensor Placement in Structural Health Monitoring Using Discrete Optimization,” Smart Mater and Structure, 24: 1–16.Google Scholar
  29. Talaslidis D and SOW I (1992), “A Discrete Kirchhoff Triangular Element for the Analysis of Thin Stiffened Shells,” Department of Civil Engineering, Aristotle University, 54006 Thessaloniki, Greece. Computers & Structures, 43(4): 663474.Google Scholar
  30. Thiagarajan G, Kadambi AV, Robert S and Johnson CF.(2015), “Experimental and Finite Element Analysis of Doubly Reinforced Concrete Slabs Subjected to Blast Loads,” International Journal of Impact Engineering, 75: 162–173.CrossRefGoogle Scholar
  31. Tang Teng, Yang Dong-Hui, Wang Lei, Zhang Jian-Ren and Yi Ting-Hua (2019), “Design and Application of Structural Health Monitoring System in Long-Span Cable-Membrane Structure,” Earthquake Engineering and Engineering Vibration, 18(2): 461–474.  https://doi.org/10.1007/s11803-019-0484-y.CrossRefGoogle Scholar
  32. Thomas G., Dohrmann C.R. (1994), “A Modal Test Design Strategy for Model Correlation,” Proceedings of the 13th International Modal Analysis Conference, Tennessee, USA, 927–933.Google Scholar
  33. Timoshenko S and Woinowsky-Krieger S (1959), “Theory of Plates and Shells, McGraw-Hill Classic Textbook Reissue Series,” 180–228.Google Scholar
  34. Vincenzi L and Simonini L (2017), “Influence of Model Errors in Optimal Sensor Placement,” Journal of Sound and Vibration, 389: 119–133.CrossRefGoogle Scholar
  35. Vosoughifar H and Shokouhi SK (2012), “Health Monitoring of LSF Structure via Novel TTFD Approach,” Civil Structural Health Monitoring Workshop (CSHM-4).Google Scholar
  36. Vosoughifar H, Shokouhi SK and Farshadmanesh P (2012), “Optimal Sensor Placement of Steel Structure with UBF System for SHM Using Hybrid FEMGA Technique,” Civil Structural Health Monitoring Workshop (CSHM-4).Google Scholar
  37. Vosoughifar H and Khorani M (2019), “Optimal Sensor Placement of RCC Dam using Modified Approach of COMAC-TTFD,” KSCE Journal of Civil Engineering, 1–15.Google Scholar
  38. Yi TH, Li HN and Gu GO (2011), “A New Method for Optimal Selection of Sensor Location on a High-Rise Building Using Simplified Finite Element Model,” Structural Engineering & Mechanics, 37(6): 671–684.CrossRefGoogle Scholar
  39. Yi TH, Li HN and Gu M. (2011), “Optimal Sensor Placement for Structural Health Monitoring Based on Multiple Optimization Strategies,” Structure Design Tall Spec. Build, 20: 881–900.CrossRefGoogle Scholar
  40. Yi TH, Li HN and Wang CW (2016), “Multiaxial Sensor Placement Optimization in Structural Health Monitoring Using Distributed Wolf Algorithm,” Structural Control and Health Monitoring, 23(4): 719–734.CrossRefGoogle Scholar
  41. Yi TH, Li HN and Zhang XD (2012), “A Modified Monkey Algorithm for Optimal Sensor Placement in Structural Health Monitoring,” Smart Materials and Structures, 21(10).Google Scholar
  42. Yi TH, Zhou GD, Li HN and Wang CW (2016), “Optimal Placement of Triaxial Sensors for Modal Identification Using Hierarchic Wolf Algorithm,” Structural Control and Health Monitoring, 24(8).Google Scholar
  43. Zhang J, Maes K, Roeck G, Reynders E, Papadimitriou C and Lombaert G (2017), “Optimal Sensor Placement for Multi-Setup Modal Analysis of Structures,” Journal of Sound and Vibration, 401: 214–232.CrossRefGoogle Scholar
  44. Zhou GD, Yi TH and Li HN (2014), “Sensor Placement Optimization in Structural Health Monitoring Using Cluster-In-Cluster Firefly Algorithm,” Advances in Structural Engineering, 17(8): 1103–1115.CrossRefGoogle Scholar

Copyright information

© Institute of Engineering Mechanics, China Earthquake Administration 2020

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of Hawaii at ManoaHonoluluUSA
  2. 2.Department of Civil Engineering, South Tehran BranchIslamic Azad UniversityTehranIran

Personalised recommendations